BMDC_Analysis Project Report

This report describes all the steps performed to analyse the data of Divergent transcriptional response of dendritic cells stimulated with distinct antigen delivery systems: new insights from a comparative and reproducible computational analysis.. The report has been automatically produced with RNASeqGUI (version 1.1.2) and then modified by hand to add paragraphs and additional comments. Moreover, it has been enriched with commands used to perform the alignment step, which RNASeqGUI software does not perform.

Alignment Step

The alignment step has been performed with TopHat version 2.0.14 on mus musculus genome mm9 NCBI annotation version 37.67 available here.

The used shell code is the following:

tophat -o tophat_out_sample_name -p 16 -N 2 -g 10 -r 200 -a 15 -m 1 -i 100 --library-type fr-unstranded --segment-mismatches 3 --read-edit-dist 2 -G Mus_musculus.NCBIM37.67_final.gtf --transcriptome-index=transcriptome_index_Mus_musculus.NCBIM37.67_final ./RNA-Seq_Dendritic_cells/filename_R1.fastq.gz ./RNA-Seq_Dendritic_cells/filename_R2.fastq.gz

Filter Unique reads step

For further steps, only uniquely mapped reads have been retained using samtools.

The used shell code is the following:

samtools view -H accepted_hits.bam > header.sam
samtools view accepted_hits.bam | grep -w 'NH:i:1' | cat header.sam - | samtools view -Sb - > accepted_hits_unique.bam

RNASeqGUI mandatory functions for code chunks execution

In order to reproduce the report code chunks, the RNASeqGUI package is required.

require(RNASeqGUI)

From now on, all the steps have been performed using RNASeqGUI v.1.1.2, available here.

Gene Counting Step

This step is useful to quantify gene expression. The Feature Counts quantifier has been choosen for this aim.

Here starts the automatically generated code

You chose the following count file: /BMDC_analysis/annotation/Mus_musculus.NCBIM37.67_final.gtf , the bam folder: /BMDC_analysis/unique_reads , the GTF file: /BMDC_analysis/annotation/Mus_musculus.NCBIM37.67_final.gtf This R code has been run:

 the.file ='/BMDC_analysis/annotation/Mus_musculus.NCBIM37.67_final.gtf'
 Bam.FolderNew = '/BMDC_analysis/unique_reads'
 Project = 'BMDC_analysis'
 Paired = 'TRUE'
 strand = '0'
 Nthread = '8'
 #fls <- list.files(Bam.FolderNew,pattern='bam$',full.names=TRUE)
 #bamlst <- BamFileList(fls,obeyQname=TRUE) 
 #Vector.Bam.FolderNew = strsplit(Bam.FolderNew,'/')[[1]]
 #BAM.name = Vector.Bam.FolderNew[length(Vector.Bam.FolderNew)]
 #BAM.name_Counts <- paste(getwd(),'RNASeqGUI_Projects',Project,'Results/featureCounts_Report',sep='/') 
 #dir.create(BAM.name_Counts, showWarnings = TRUE, recursive = TRUE)
 #FeatureCountsFun <- function(bamfile){
 #fc_SE<-Rsubread::featureCounts(files=bamfile,annot.ext=the.file,isGTFAnnotationFile=TRUE,GTF.featureType='exon'
 #GTF.attrType='gene_id',useMetaFeatures=TRUE,allowMultiOverlap=FALSE,nthreads=Nthread,strandSpecific=strand,
 #countMultiMappingReads=FALSE,isPairedEnd=Paired)
 #bplapply(fls, FeatureCountsFun) 

Filtering Step

This step is useful to filter out genes with low counts. This functionality has been implemented through the NoiSeq package.

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/unique_reads_FeatureCounts/counts_FeatureCounts.txt conditions:= c( 'DEC_1', ' DEC_2', ' E2_1', ' E2_2', ' UNTR_1', ' UNTR_2' ), type1: FALSE type2: FALSE type3: TRUE cv.cutoff: 100 norm: FALSE cpm: 0.5 , Project: BMDC_analysis

This R code has been run:

 the.file2 = 'counts_FeatureCounts.txt'
 filtering.db <- InitDb(db.name=paste(the.file2,'filtering_db',sep=''), db.path='cache')
 x <- LoadCachedObject(filtering.db, 'filtering_dataframe_key')
 head(x) 
 ##                    DEC_1 DEC_2 E2_1 E2_2 UNTR_1 UNTR_2
 ## ENSMUSG00000000702     0     0    0    0      0      0
 ## ENSMUSG00000078423     0     0    0    0      0      0
 ## ENSMUSG00000078424     0     0    0    0      0      0
 ## ENSMUSG00000071964     0     0    0    0      0      0
 ## ENSMUSG00000093774     0     0    0    0      0      0
 ## ENSMUSG00000093444     0     0    0    0      0      1
 the.file <- LoadCachedObject(filtering.db, 'the_file_key')
 Project <- LoadCachedObject(filtering.db, 'project_key')
 conditions <- LoadCachedObject(filtering.db, 'conditions_key')
 type1 <- LoadCachedObject(filtering.db, 'type1_key')
 type2 <- LoadCachedObject(filtering.db, 'type2_key')
 type3 <- LoadCachedObject(filtering.db, 'type3_key')
 cpm <- LoadCachedObject(filtering.db, 'cpm_key')
 cv.cutoff <- LoadCachedObject(filtering.db, 'cvcutoff_key')
 setwd('//')
 #filteringfunction(x=x,the.file=the.file,conditions=conditions,type1=type1,type2=type2,type3=type3,cv.cutoff=cv.cutoff,norm=norm,cpm=cpm,Project=Project)
 filtered_x <- LoadCachedObject(filtering.db, 'filteredx_key')
 head(filtered_x) 
 ##                    DEC_1 DEC_2 E2_1 E2_2 UNTR_1 UNTR_2
 ## ENSMUSG00000063889  1574  1427 1914 1748   1624   1811
 ## ENSMUSG00000024231  1838  1829 1808 1650   1446   1544
 ## ENSMUSG00000024232   164   176  185  174    181    217
 ## ENSMUSG00000073647   158   144  132  106     66     63
 ## ENSMUSG00000024235  2187  2002 1391 1282   1023   1180
 ## ENSMUSG00000024234  1037   947  965  898    779    903

Data Exploration

In order to understand how data have been transformed by filtering procedure, it is necessary to plot their distributions. Each boxplot represents the counts distribution for each experiment sample.

Here starts the automatically generated code

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt , log: TRUE . This R code has been run:

 the.file2 = 'Proportion_counts_FeatureCounts.txt'
 countdistr.db <- InitDb(db.name=paste(the.file2,'countdistr_db',sep=''), db.path='cache')
 x <- LoadCachedObject(countdistr.db, 'countdistr_dataframe_key')
 the.file <- LoadCachedObject(countdistr.db, 'the_file_key')
 Project <- LoadCachedObject(countdistr.db, 'project_key')
 log <- LoadCachedObject(countdistr.db, 'log_key')
 a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Plots/',sep='')
 the.file2 = strsplit(the.file,'/')
 the.file2 = the.file2[[1]][length(the.file2[[1]])]  #estract the namefile
 outputName = paste(the.file2,'_countDistr.pdf',sep='')
 colors = c('red','red','blue','blue','purple','purple','orange','orange','pink','orange','gold',
 'darkblue','cyan','darkred')
 if (log==TRUE) { boxplot(log(x+1),col=colors, main=paste(the.file2,' Log Count Distribution'),las=1) }

plot of chunk unnamed-chunk-3

 if (log==FALSE) { boxplot(x,col=colors, main=paste(the.file2,' Count Distribution'),las=1) }

Normalization Step

In order to better compare and analyse the samples between them, a normalization is required. This step has been performed using the Upper Quartile normalization, present in the edgeR package.

Here starts the automatically generated code

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt . This R code has been run:

 require(edgeR)
 uqua.db <- InitDb(db.name='uqua_db', db.path='cache')
 x <- LoadCachedObject(uqua.db, 'uqua_dataframe_key')
 the.file <- LoadCachedObject(uqua.db, 'the_file_key')
 Project <- LoadCachedObject(uqua.db, 'project_key')
 print('You loaded the following count file:')
 ## [1] "You loaded the following count file:"
 head(x,10)
 ##                    DEC_1 DEC_2 E2_1 E2_2 UNTR_1 UNTR_2
 ## ENSMUSG00000063889  1574  1427 1914 1748   1624   1811
 ## ENSMUSG00000024231  1838  1829 1808 1650   1446   1544
 ## ENSMUSG00000024232   164   176  185  174    181    217
 ## ENSMUSG00000073647   158   144  132  106     66     63
 ## ENSMUSG00000024235  2187  2002 1391 1282   1023   1180
 ## ENSMUSG00000024234  1037   947  965  898    779    903
 ## ENSMUSG00000033960   681   663  941  823    522    658
 ## ENSMUSG00000024236  4323  4153 3535 3485   2113   2169
 ## ENSMUSG00000050945   152   133  158  166    140    146
 ## ENSMUSG00000041225  1792  1640 1518 1397   1184   1329
 #x <- edgeR::DGEList(counts = x)
 #x <- edgeR::calcNormFactors(x,method='upperquartile')
 #x <- edgeR::estimateCommonDisp(x, verbose=FALSE)
 #x <- edgeR::estimateTagwiseDisp(x) 
 #myuqua <- x$pseudo.counts
 myuqua <- LoadCachedObject(uqua.db, 'myuqua_key')
 colors = c('red','red','blue','blue','purple','purple','orange','orange','pink','orange','gold','darkblue','cyan','darkred')
 boxplot(log(myuqua+1),col=colors, main='Upper Quartile BoxPlot',las=1)

plot of chunk unnamed-chunk-4


Data Exploration

In order to understand how data have been transformed by normalization procedure, it is necessary to plot their distributions.

Scatter plot matrix

Here a scatter plot matrix has been plot to compare each couple of sample distributions.

Here starts the automatically generated code

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt . This R code has been run:

 library(car)
 the.file2 = 'Proportion_counts_FeatureCounts.txt_UQUA.txt'
 plotspm.db <- InitDb(db.name=paste(the.file2,'plotspm_db',sep=''),db.path='cache')
 x <- LoadCachedObject(plotspm.db, 'plotallcounts_dataframe_key')
 the.file <- LoadCachedObject(plotspm.db, 'the_file_key')
 Project <- LoadCachedObject(plotspm.db, 'project_key')
 spm(log10(x+1), pch=19,cex=0.3,smoother=FALSE)

plot of chunk unnamed-chunk-5


Loadings of Principal Component Analysis (PCA)

The following plot represents the Loadings of PCA, useful to undestand which of the explains the major variability.

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt . This R code has been run:

  the.file2 = 'Proportion_counts_FeatureCounts.txt_UQUA.txt'
  componenthist.db <- InitDb(db.name=paste(the.file2,'componenthist_db',sep=''), db.path='cache')
  x <- LoadCachedObject(componenthist.db, 'componenthist_dataframe_key')
  the.file <- LoadCachedObject(componenthist.db, 'the_file_key')
  Project <- LoadCachedObject(componenthist.db, 'project_key')
  a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Plots/',sep='')
  the.file2 = strsplit(the.file,'/')
  the.file2 = the.file2[[1]][length(the.file2[[1]])]  #estract the namefile
  outputName = paste(the.file2,'_ComponentHist.pdf',sep='')
  screeplot(princomp(x),main=outputName)

plot of chunk unnamed-chunk-6


Principal Component Analysis (PCA)

The PCA, useful to undestand how the samples clusterize between them.

Here starts the automatically generated code

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt . This R code has been run:

  the.file2 = 'Proportion_counts_FeatureCounts.txt_UQUA.txt'
  pcafun.db <- InitDb(db.name=paste(the.file2,'pcafun_db',sep=''), db.path='cache')
  x <- LoadCachedObject(pcafun.db, 'pcafun_dataframe_key')
  condition <- colnames(x)
  the.file <- LoadCachedObject(pcafun.db, 'the_file_key')
  Project <- LoadCachedObject(pcafun.db, 'project_key')
  legendpos <- LoadCachedObject(pcafun.db, 'legendpos_key')
  PCA <- LoadCachedObject(pcafun.db, 'pca_key')
  colors <- LoadCachedObject(pcafun.db, 'colors_key')
  n <- LoadCachedObject(pcafun.db, 'n_key')
  PCA = prcomp(t(log(x+1)))
  colors = c(rep('blue',n),rep('darkgreen',n),rep('red',n),rep('purple',n),rep('black',n),
           rep('green',n),rep('brown',n),rep('pink',n),rep('gold',n))
  a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Plots/',sep='')
  the.file2 = strsplit(the.file,'/')
  the.file2 = the.file2[[1]][length(the.file2[[1]])]  #estract the namefile
  outputName = paste(the.file2,'_PCA.pdf',sep='')
  plot(PCA$x,pch=unclass(as.factor(condition)),col=colors,cex=1.5, main = outputName, lwd=2)
  legend(legendpos,legend=condition,pch=unclass(as.factor(condition)),col=colors,
       fill='transparent',border='NA')

plot of chunk unnamed-chunk-7


Differential Expression Analysis

In order to understand the differential expressed genes for fdscaDEC (from now on just DEC) and E2, the samples have been separated in two distinct group.

DEC samples

The keep columns function has been used on complete count file to separate DEC and untreated samples from E2 samples.

Here starts the automatically generated code

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt . This R code has been run:

  dbfilename = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_1256_keepcolumns_db'
  keepcolumns.db <- InitDb(dbfilename, db.path=file.path('cache'))
  Count.File <- LoadCachedObject(keepcolumns.db, 'Count_File_key')
  counts <- LoadCachedObject(keepcolumns.db, 'counts_key')
  head(counts)
  ##                        DEC_1     DEC_2      E2_1      E2_2     UNTR_1
  ## ENSMUSG00000063889 1386.2100 1296.7846 1840.5502 1797.3179 1927.78005
  ## ENSMUSG00000024231 1618.9080 1662.3452 1738.6008 1696.5656 1716.66789
  ## ENSMUSG00000024232  144.2284  159.8542  177.8831  178.9201  214.62411
  ## ENSMUSG00000073647  139.4464  131.0292  126.9828  108.9937   78.76874
  ## ENSMUSG00000024235 1926.7159 1819.7991 1337.5621 1318.2114 1214.82938
  ## ENSMUSG00000024234  913.4006  860.6585  927.9543  923.3442  924.83273
  ##                        UNTR_2
  ## ENSMUSG00000063889 1927.03014
  ## ENSMUSG00000024231 1643.01010
  ## ENSMUSG00000024232  230.77168
  ## ENSMUSG00000073647   67.22854
  ## ENSMUSG00000024235 1255.74910
  ## ENSMUSG00000024234  960.87190
  counts <- LoadCachedObject(keepcolumns.db, 'counts_key')
  columns <- LoadCachedObject(keepcolumns.db, 'columns_key')
  sub.counts <- LoadCachedObject(keepcolumns.db, 'sub_counts_key')
  head(sub.counts)
  ##                        DEC_1     DEC_2     UNTR_1     UNTR_2
  ## ENSMUSG00000063889 1386.2100 1296.7846 1927.78005 1927.03014
  ## ENSMUSG00000024231 1618.9080 1662.3452 1716.66789 1643.01010
  ## ENSMUSG00000024232  144.2284  159.8542  214.62411  230.77168
  ## ENSMUSG00000073647  139.4464  131.0292   78.76874   67.22854
  ## ENSMUSG00000024235 1926.7159 1819.7991 1214.82938 1255.74910
  ## ENSMUSG00000024234  913.4006  860.6585  924.83273  960.87190

Differential Expression Step

This step has been performed using the NOISeq package.

Here starts the automatically generated code

You chose the following count file: Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt , prob: 0.95 , Project: BMDC_analysis , Tissue= c( 'DEC', 'DEC', 'UNTR', 'UNTR' ), TissueRun= c( 'DEC_1', 'DEC_2', 'UNTR_1', 'UNTR_2' ).

This R code has been run:

require(NOISeq)
require(plotrix)
the.file2 = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt'
noide2_db <- InitDb(db.name=paste(the.file2,'noide2_db',sep='_'), db.path='cache')
x <- LoadCachedObject(noide2_db, 'maindataframe_key')
the.file <- LoadCachedObject(noide2_db, 'the_file_key')
Project <- LoadCachedObject(noide2_db, 'project_key')
conditions <- LoadCachedObject(noide2_db, 'conditions_key')
TissueRuns <- LoadCachedObject(noide2_db, 'tissueruns_key')
p <- LoadCachedObject(noide2_db, 'p_key')
technical = 'TRUE'
print('You loaded this count file: ')
## [1] "You loaded this count file: "
head(as.matrix(x))
##                        DEC_1     DEC_2     UNTR_1     UNTR_2
## ENSMUSG00000063889 1386.2100 1296.7846 1927.78005 1927.03014
## ENSMUSG00000024231 1618.9080 1662.3452 1716.66789 1643.01010
## ENSMUSG00000024232  144.2284  159.8542  214.62411  230.77168
## ENSMUSG00000073647  139.4464  131.0292   78.76874   67.22854
## ENSMUSG00000024235 1926.7159 1819.7991 1214.82938 1255.74910
## ENSMUSG00000024234  913.4006  860.6585  924.83273  960.87190
mynoiseq = NULL
if (technical == TRUE){ # technical replicate 
  print('NOISeq has been started on TECHNICAL replicates')
  #myfactors = data.frame(Tissue = conditions, TissueRun = TissueRuns)
  #mydata <- NOISeq::readData(data=x, factors = myfactors)
  #mynoiseq = noiseq(mydata,k=0.5,norm='n',factor='Tissue',pnr = 0.2,nss = 5,
  #v = 0.02,lc = 0,replicates=technical)
  mynoiseq <- LoadCachedObject(noide2_db, 'mynoiseq_key')
}else{ # biological replicate
  print('NOISeqBIO has been started on BIOLOGICAL replicates')
  #myfactors = data.frame(Tissue = conditions, TissueRun = TissueRuns)
  #mydata <- NOISeq::readData(data=x, factors=myfactors)
  #mynoiseq = noiseqbio(mydata, k = 0.5, norm = 'n', factor='Tissue', lc = 0, r = 20, adj = 1.5,
  #plot = FALSE, a0per = 0.9, random.seed = 12345, filter = 0)
  mynoiseq <- LoadCachedObject(noide2_db, 'mynoiseq_key')
}
## [1] "NOISeq has been started on TECHNICAL replicates"
print('First five lines of the results.')
## [1] "First five lines of the results."
head(mynoiseq@results[[1]])
##                     DEC_mean  UNTR_mean           M         D      prob
## ENSMUSG00000063889 1341.4973 1927.40510 -0.52281569 585.90781 0.9659836
## ENSMUSG00000024231 1640.6266 1679.83900 -0.03407605  39.21240 0.2574143
## ENSMUSG00000024232  152.0413  222.69790 -0.55062467  70.65660 0.7524218
## ENSMUSG00000073647  135.2378   72.99864  0.88955713  62.23917 0.7369970
## ENSMUSG00000024235 1873.2575 1235.28924  0.60070035 637.96827 0.9744784
## ENSMUSG00000024234  887.0296  942.85231 -0.08804964  55.82276 0.4768629
##                       ranking
## ENSMUSG00000063889 -585.90804
## ENSMUSG00000024231  -39.21242
## ENSMUSG00000024232  -70.65874
## ENSMUSG00000073647   62.24553
## ENSMUSG00000024235  637.96855
## ENSMUSG00000024234  -55.82283
#list_DE_NOISEQ = subset(mynoiseq@results[[1]], prob > p) # select significant genes
list_DE_NOISEQ <- LoadCachedObject(noide2_db, 'listdenoiseq_key')
slices <- LoadCachedObject(noide2_db, 'slicesnoiseq_key')
lbls <- LoadCachedObject(noide2_db, 'lblsnoiseq_key')
pie3D(slices,labels=lbls,explode=0.1, main='Pie Chart of DE Genes')

plot of chunk unnamed-chunk-9


Result Inspection

To better understand the percentage of differential expressed genes, an inspection of the results has been performed with appropriate plots.

MA Plot

In this plot differential expressed genes are highlighted in red. Up-regulated genes are in the upper part of the plot, while down-regulated in the lower part.

Here starts the automatically generated code

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_results_NOISeq.txt , prob: 0.95 , Project: BMDC_analysis ,

the.file2 = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_results_NOISeq'
db.cache <- InitDb(db.name=paste(the.file2,'_plotfcnois_db',sep=''), db.path='cache')
results_NoiSeq <- LoadCachedObject(db.cache, 'res_key')
the.file <- LoadCachedObject(db.cache, 'thefile_key')
Project <- LoadCachedObject(db.cache, 'project_key')
p <- LoadCachedObject(db.cache, 'p_key')
name <- LoadCachedObject(db.cache, 'name_key')
print('This file has been loaded: ')
## [1] "This file has been loaded: "
head(results_NoiSeq)
##                     DEC_mean  UNTR_mean           M         D      prob
## ENSMUSG00000063889 1341.4973 1927.40510 -0.52281569 585.90781 0.9659836
## ENSMUSG00000024231 1640.6266 1679.83900 -0.03407605  39.21240 0.2574143
## ENSMUSG00000024232  152.0413  222.69790 -0.55062467  70.65660 0.7524218
## ENSMUSG00000073647  135.2378   72.99864  0.88955713  62.23917 0.7369970
## ENSMUSG00000024235 1873.2575 1235.28924  0.60070035 637.96827 0.9744784
## ENSMUSG00000024234  887.0296  942.85231 -0.08804964  55.82276 0.4768629
##                       ranking
## ENSMUSG00000063889 -585.90804
## ENSMUSG00000024231  -39.21242
## ENSMUSG00000024232  -70.65874
## ENSMUSG00000073647   62.24553
## ENSMUSG00000024235  637.96855
## ENSMUSG00000024234  -55.82283
cat('prob chosen: ', p, "\n")
## prob chosen:  0.95
cat('Gene Id chosen: ', name, "\n")
## Gene Id chosen:
plot(log10(results_NoiSeq[,2] * results_NoiSeq[,1]),log10(results_NoiSeq[,2]/results_NoiSeq[,1]),col='black',
     main=paste('PlotFC ',the.file2,sep=''), xlab=paste('log10( ', colnames(results_NoiSeq)[2],' * ',
                                                        colnames(results_NoiSeq)[1],')',sep=''),ylab=paste('log10( ',colnames(results_NoiSeq)[2],
                                                                                                           ' / ',colnames(results_NoiSeq)[1],' )',sep=''),pch=19,cex=0.3)
DE_genes_NoiSeq = subset(results_NoiSeq, prob>p)
points(log10(DE_genes_NoiSeq[,2] * DE_genes_NoiSeq[,1]), log10(DE_genes_NoiSeq[,2]/DE_genes_NoiSeq[,1]),
       pch=19, col='red', cex=0.5)

plot of chunk unnamed-chunk-10

if (name!=''){ OneGene = subset(results_NoiSeq, row.names(results_NoiSeq)==name)
text(log10((OneGene[,2] * OneGene[,1])), log10(OneGene[,2]/OneGene[,1]), label=name, col='green', cex=0.6)}

Volcano Plot

In this plot differential expressed genes are highlighted in red. Up-regulated genes are in the right part of the plot, while down-regulated in the left part.

Here starts the automatically generated code

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_results_NOISeq.txt , prob: 0.95 , Project: BMDC_analysis ,

the.file2 = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_results_NOISeq'
db.cache <- InitDb(db.name=paste(the.file2,'_volcanonois_db',sep=''), db.path='cache')
results_noi <- LoadCachedObject(db.cache, 'res_key')
the.file <- LoadCachedObject(db.cache, 'thefile_key')
Project <- LoadCachedObject(db.cache, 'project_key')
name <- LoadCachedObject(db.cache, 'name_key')
p <- LoadCachedObject(db.cache, 'p_key')
print('This file has been loaded: ')
## [1] "This file has been loaded: "
head(results_noi)
##                     DEC_mean  UNTR_mean           M         D      prob
## ENSMUSG00000063889 1341.4973 1927.40510 -0.52281569 585.90781 0.9659836
## ENSMUSG00000024231 1640.6266 1679.83900 -0.03407605  39.21240 0.2574143
## ENSMUSG00000024232  152.0413  222.69790 -0.55062467  70.65660 0.7524218
## ENSMUSG00000073647  135.2378   72.99864  0.88955713  62.23917 0.7369970
## ENSMUSG00000024235 1873.2575 1235.28924  0.60070035 637.96827 0.9744784
## ENSMUSG00000024234  887.0296  942.85231 -0.08804964  55.82276 0.4768629
##                       ranking
## ENSMUSG00000063889 -585.90804
## ENSMUSG00000024231  -39.21242
## ENSMUSG00000024232  -70.65874
## ENSMUSG00000073647   62.24553
## ENSMUSG00000024235  637.96855
## ENSMUSG00000024234  -55.82283
p = as.numeric(p)
cat('prob chosen: ', p, "\n")
## prob chosen:  0.95
print ('Gene Id chosen: ', name, "\n")
## Error in print.default("Gene Id chosen: ", name, "\n"): argomento 'digits' non valido
plot(log10(results_noi[,2]/results_noi[,1]), -log10(1 - results_noi$prob + 0.000001), col = 'black',
     main=paste('Volcano Plot',the.file2,sep=''),xlab='log10FC',ylab='-log10(1-results_noi$prob+0.000001)',pch=19,cex=0.3)
DE_genes_NoiSeq = subset(results_noi, prob>p)
points(log10(DE_genes_NoiSeq[,2]/DE_genes_NoiSeq[,1]),-log10(1-DE_genes_NoiSeq$prob+0.000001),
       pch=19,col='red',cex=0.5)

plot of chunk unnamed-chunk-11

if (name!=''){    OneGene = subset(results_noi, row.names(results_noi)==name)
text(OneGene$M, OneGene$prob, label=name, col='green', cex=0.6) }

E2 samples

The keep columns function has been used on complete count file to separate E2 and untreated samples from DEC samples.

Here starts the automatically generated code

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt . This R code has been run:

dbfilename = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_3456_keepcolumns_db'
keepcolumns.db <- InitDb(dbfilename, db.path=file.path('cache'))
Count.File <- LoadCachedObject(keepcolumns.db, 'Count_File_key')
counts <- LoadCachedObject(keepcolumns.db, 'counts_key')
head(counts)
##                        DEC_1     DEC_2      E2_1      E2_2     UNTR_1
## ENSMUSG00000063889 1386.2100 1296.7846 1840.5502 1797.3179 1927.78005
## ENSMUSG00000024231 1618.9080 1662.3452 1738.6008 1696.5656 1716.66789
## ENSMUSG00000024232  144.2284  159.8542  177.8831  178.9201  214.62411
## ENSMUSG00000073647  139.4464  131.0292  126.9828  108.9937   78.76874
## ENSMUSG00000024235 1926.7159 1819.7991 1337.5621 1318.2114 1214.82938
## ENSMUSG00000024234  913.4006  860.6585  927.9543  923.3442  924.83273
##                        UNTR_2
## ENSMUSG00000063889 1927.03014
## ENSMUSG00000024231 1643.01010
## ENSMUSG00000024232  230.77168
## ENSMUSG00000073647   67.22854
## ENSMUSG00000024235 1255.74910
## ENSMUSG00000024234  960.87190
counts <- LoadCachedObject(keepcolumns.db, 'counts_key')
columns <- LoadCachedObject(keepcolumns.db, 'columns_key')
sub.counts <- LoadCachedObject(keepcolumns.db, 'sub_counts_key')
head(sub.counts)
##                         E2_1      E2_2     UNTR_1     UNTR_2
## ENSMUSG00000063889 1840.5502 1797.3179 1927.78005 1927.03014
## ENSMUSG00000024231 1738.6008 1696.5656 1716.66789 1643.01010
## ENSMUSG00000024232  177.8831  178.9201  214.62411  230.77168
## ENSMUSG00000073647  126.9828  108.9937   78.76874   67.22854
## ENSMUSG00000024235 1337.5621 1318.2114 1214.82938 1255.74910
## ENSMUSG00000024234  927.9543  923.3442  924.83273  960.87190

Differential Expression Step

This step has been performed using the NOISeq package.

Here starts the automatically generated code

You chose the following count file: Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt , prob: 0.95 , Project: BMDC_analysis , Tissue= c( 'E2', 'E2', 'UNTR', 'UNTR' ), TissueRun= c( 'E2_1', 'E2_2', 'UNTR_1', 'UNTR_2' ).

This R code has been run:

require(NOISeq)
require(plotrix)
the.file2 = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt'
noide2_db <- InitDb(db.name=paste(the.file2,'noide2_db',sep='_'), db.path='cache')
x <- LoadCachedObject(noide2_db, 'maindataframe_key')
the.file <- LoadCachedObject(noide2_db, 'the_file_key')
Project <- LoadCachedObject(noide2_db, 'project_key')
conditions <- LoadCachedObject(noide2_db, 'conditions_key')
TissueRuns <- LoadCachedObject(noide2_db, 'tissueruns_key')
p <- LoadCachedObject(noide2_db, 'p_key')
technical = 'TRUE'
print('You loaded this count file: ')
## [1] "You loaded this count file: "
head(as.matrix(x))
##                         E2_1      E2_2     UNTR_1     UNTR_2
## ENSMUSG00000063889 1840.5502 1797.3179 1927.78005 1927.03014
## ENSMUSG00000024231 1738.6008 1696.5656 1716.66789 1643.01010
## ENSMUSG00000024232  177.8831  178.9201  214.62411  230.77168
## ENSMUSG00000073647  126.9828  108.9937   78.76874   67.22854
## ENSMUSG00000024235 1337.5621 1318.2114 1214.82938 1255.74910
## ENSMUSG00000024234  927.9543  923.3442  924.83273  960.87190
mynoiseq = NULL
if (technical == TRUE){ # technical replicate 
  print('NOISeq has been started on TECHNICAL replicates')
  #myfactors = data.frame(Tissue = conditions, TissueRun = TissueRuns)
  #mydata <- NOISeq::readData(data=x, factors = myfactors)
  #mynoiseq = noiseq(mydata,k=0.5,norm='n',factor='Tissue',pnr = 0.2,nss = 5,
  #v = 0.02,lc = 0,replicates=technical)
  mynoiseq <- LoadCachedObject(noide2_db, 'mynoiseq_key')
}else{ # biological replicate
  print('NOISeqBIO has been started on BIOLOGICAL replicates')
  #myfactors = data.frame(Tissue = conditions, TissueRun = TissueRuns)
  #mydata <- NOISeq::readData(data=x, factors=myfactors)
  #mynoiseq = noiseqbio(mydata, k = 0.5, norm = 'n', factor='Tissue', lc = 0, r = 20, adj = 1.5,
  #plot = FALSE, a0per = 0.9, random.seed = 12345, filter = 0)
  mynoiseq <- LoadCachedObject(noide2_db, 'mynoiseq_key')
}
## [1] "NOISeq has been started on TECHNICAL replicates"
print('First five lines of the results.')
## [1] "First five lines of the results."
head(mynoiseq@results[[1]])
##                      E2_mean  UNTR_mean           M         D      prob
## ENSMUSG00000063889 1818.9340 1927.40510 -0.08356661 108.47107 0.5591654
## ENSMUSG00000024231 1717.5832 1679.83900  0.03205704  37.74423 0.2549925
## ENSMUSG00000024232  178.4016  222.69790 -0.31995944  44.29630 0.5848361
## ENSMUSG00000073647  117.9883   72.99864  0.69270212  44.98964 0.6417660
## ENSMUSG00000024235 1327.8867 1235.28924  0.10428320  92.59749 0.5867735
## ENSMUSG00000024234  925.6492  942.85231 -0.02656624  17.20310 0.1789121
##                       ranking
## ENSMUSG00000063889 -108.47111
## ENSMUSG00000024231   37.74424
## ENSMUSG00000024232  -44.29746
## ENSMUSG00000073647   44.99497
## ENSMUSG00000024235   92.59755
## ENSMUSG00000024234  -17.20312
#list_DE_NOISEQ = subset(mynoiseq@results[[1]], prob > p) # select significant genes
list_DE_NOISEQ <- LoadCachedObject(noide2_db, 'listdenoiseq_key')
slices <- LoadCachedObject(noide2_db, 'slicesnoiseq_key')
lbls <- LoadCachedObject(noide2_db, 'lblsnoiseq_key')
pie3D(slices,labels=lbls,explode=0.1, main='Pie Chart of DE Genes')

plot of chunk unnamed-chunk-13


Result Inspection

To better understand the percentage of differential expressed genes, an inspection of the results has been performed with appropriate plots.

MA Plot

In this plot differential expressed genes are highlighted in red. Up-regulated genes are in the upper part of the plot, while down-regulated in the lower part.

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_results_NOISeq.txt , prob: 0.95 , Project: BMDC_analysis ,

the.file2 = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_results_NOISeq'
db.cache <- InitDb(db.name=paste(the.file2,'_plotfcnois_db',sep=''), db.path='cache')
results_NoiSeq <- LoadCachedObject(db.cache, 'res_key')
the.file <- LoadCachedObject(db.cache, 'thefile_key')
Project <- LoadCachedObject(db.cache, 'project_key')
p <- LoadCachedObject(db.cache, 'p_key')
name <- LoadCachedObject(db.cache, 'name_key')
print('This file has been loaded: ')
## [1] "This file has been loaded: "
head(results_NoiSeq)
##                      E2_mean  UNTR_mean           M         D      prob
## ENSMUSG00000063889 1818.9340 1927.40510 -0.08356661 108.47107 0.5591654
## ENSMUSG00000024231 1717.5832 1679.83900  0.03205704  37.74423 0.2549925
## ENSMUSG00000024232  178.4016  222.69790 -0.31995944  44.29630 0.5848361
## ENSMUSG00000073647  117.9883   72.99864  0.69270212  44.98964 0.6417660
## ENSMUSG00000024235 1327.8867 1235.28924  0.10428320  92.59749 0.5867735
## ENSMUSG00000024234  925.6492  942.85231 -0.02656624  17.20310 0.1789121
##                       ranking
## ENSMUSG00000063889 -108.47111
## ENSMUSG00000024231   37.74424
## ENSMUSG00000024232  -44.29746
## ENSMUSG00000073647   44.99497
## ENSMUSG00000024235   92.59755
## ENSMUSG00000024234  -17.20312
cat('prob chosen: ',p,"\n")
## prob chosen:  0.95
cat('Gene Id chosen: ', name,"\n")
## Gene Id chosen:
plot(log10(results_NoiSeq[,2] * results_NoiSeq[,1]),log10(results_NoiSeq[,2]/results_NoiSeq[,1]),col='black',
     main=paste('PlotFC ',the.file2,sep=''), xlab=paste('log10( ', colnames(results_NoiSeq)[2],' * ',
                                                        colnames(results_NoiSeq)[1],')',sep=''),ylab=paste('log10( ',colnames(results_NoiSeq)[2],
                                                                                                           ' / ',colnames(results_NoiSeq)[1],' )',sep=''),pch=19,cex=0.3)
DE_genes_NoiSeq = subset(results_NoiSeq, prob>p)
points(log10(DE_genes_NoiSeq[,2] * DE_genes_NoiSeq[,1]), log10(DE_genes_NoiSeq[,2]/DE_genes_NoiSeq[,1]),
       pch=19, col='red', cex=0.5)

plot of chunk unnamed-chunk-14

if (name!=''){ OneGene = subset(results_NoiSeq, row.names(results_NoiSeq)==name)
text(log10((OneGene[,2] * OneGene[,1])), log10(OneGene[,2]/OneGene[,1]), label=name, col='green', cex=0.6)}

Volcano Plot

In this plot differential expressed genes are highlighted in red. Up-regulated genes are in the right part of the plot, while down-regulated in the left part.

Here starts the automatically generated code

You chose the following count file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_results_NOISeq.txt , prob: 0.95 , Project: BMDC_analysis ,

the.file2 = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_results_NOISeq'
db.cache <- InitDb(db.name=paste(the.file2,'_volcanonois_db',sep=''), db.path='cache')
results_noi <- LoadCachedObject(db.cache, 'res_key')
the.file <- LoadCachedObject(db.cache, 'thefile_key')
Project <- LoadCachedObject(db.cache, 'project_key')
name <- LoadCachedObject(db.cache, 'name_key')
p <- LoadCachedObject(db.cache, 'p_key')
print('This file has been loaded: ')
## [1] "This file has been loaded: "
head(results_noi)
##                      E2_mean  UNTR_mean           M         D      prob
## ENSMUSG00000063889 1818.9340 1927.40510 -0.08356661 108.47107 0.5591654
## ENSMUSG00000024231 1717.5832 1679.83900  0.03205704  37.74423 0.2549925
## ENSMUSG00000024232  178.4016  222.69790 -0.31995944  44.29630 0.5848361
## ENSMUSG00000073647  117.9883   72.99864  0.69270212  44.98964 0.6417660
## ENSMUSG00000024235 1327.8867 1235.28924  0.10428320  92.59749 0.5867735
## ENSMUSG00000024234  925.6492  942.85231 -0.02656624  17.20310 0.1789121
##                       ranking
## ENSMUSG00000063889 -108.47111
## ENSMUSG00000024231   37.74424
## ENSMUSG00000024232  -44.29746
## ENSMUSG00000073647   44.99497
## ENSMUSG00000024235   92.59755
## ENSMUSG00000024234  -17.20312
p = as.numeric(p)
cat('prob chosen: ',p,"\n")
## prob chosen:  0.95
cat('Gene Id chosen: ', name,"\n")
## Gene Id chosen:
plot(log10(results_noi[,2]/results_noi[,1]), -log10(1 - results_noi$prob + 0.000001), col = 'black',
     main=paste('Volcano Plot',the.file2,sep=''),xlab='log10FC',ylab='-log10(1-results_noi$prob+0.000001)',pch=19,cex=0.3)
DE_genes_NoiSeq = subset(results_noi, prob>p)
points(log10(DE_genes_NoiSeq[,2]/DE_genes_NoiSeq[,1]),-log10(1-DE_genes_NoiSeq$prob+0.000001),
       pch=19,col='red',cex=0.5)

plot of chunk unnamed-chunk-15

if (name!=''){    OneGene = subset(results_noi, row.names(results_noi)==name)
text(OneGene$M, OneGene$prob, label=name, col='green', cex=0.6) }

Result Comparison Step

In order to compare the results obtained in the differential expression step, some Venn diagrams have been produced.

E2 genes

The E2 genes have been intersected with DEC up-regulated and down-regulated genes.

Here starts the automatically generated code

require(limma)
dbfilename <- 'E2_DEC_UP_DEC_DOWN_venn3de_db'
db <- InitDb(db.name=dbfilename, db.path='cache')
x <- LoadCachedObject(db, 'maindataframe1_key')
y <- LoadCachedObject(db, 'maindataframe2_key')
z <- LoadCachedObject(db, 'maindataframe3_key')
the.file1 <- LoadCachedObject(db, 'thefile1_key')
the.file2 <- LoadCachedObject(db, 'thefile2_key')
the.file3 <- LoadCachedObject(db, 'thefile3_key')
label1 <- LoadCachedObject(db, 'label1_key')
label2 <- LoadCachedObject(db, 'label2_key')
label3 <- LoadCachedObject(db, 'label3_key')
  a = row.names(x)
  b = row.names(y)
  c = row.names(z)
  d <- intersect(a,b)     #common gene names
  e <- intersect(d,c)     #common gene names
  Lists <- list(a, b, c)  #put the word vectors into a list to supply lapply  
  Lists <- lapply(Lists, function(x) as.character(unlist(x)))
  items <- sort(unique(unlist(Lists)))   #put in alphabetical order
  #MAT <- matrix(rep(0, length(items)*length(Lists)), ncol=3)  #make a matrix of 0's
  #names <- c(label1,label2,label3)   
  #colnames(MAT) <- names
  #rownames(MAT) <- items
  #lapply(seq_along(Lists), function(i) {   #fill the matrix
   #   MAT[items %in% Lists[[i]], i] <<- table(Lists[[i]]) })
MAT <- LoadCachedObject(db, 'mat_key')
   a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Results/',sep='')
   outputName=paste(label1,'_',label2,'_',label3,'_genes_in_intersection.txt',sep='')
   b = paste(a, outputName,sep='')
   #write.table(e, file = b , quote=FALSE, sep='    ', row.names=FALSE)
   a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Plots/',sep='') 
   outputName = paste(label1,'_',label2,'_',label3,'_VennDiagramDE.pdf',sep='')
   b = paste(a,outputName,sep='')
   vennDiagram(MAT,circle.col= c('red','green','yellow'),main='Venn Diagram of DE genes')

plot of chunk unnamed-chunk-16


DEC genes

The DEC genes have been intersected with E2 up-regulated and down-regulated genes.

Here starts the automatically generated code

require(limma)
dbfilename <- 'DEC_E2_UP_E2_DOWN_venn3de_db'
db <- InitDb(db.name=dbfilename, db.path='cache')
x <- LoadCachedObject(db, 'maindataframe1_key')
y <- LoadCachedObject(db, 'maindataframe2_key')
z <- LoadCachedObject(db, 'maindataframe3_key')
the.file1 <- LoadCachedObject(db, 'thefile1_key')
the.file2 <- LoadCachedObject(db, 'thefile2_key')
the.file3 <- LoadCachedObject(db, 'thefile3_key')
label1 <- LoadCachedObject(db, 'label1_key')
label2 <- LoadCachedObject(db, 'label2_key')
label3 <- LoadCachedObject(db, 'label3_key')
  a = row.names(x)
  b = row.names(y)
  c = row.names(z)
  d <- intersect(a,b)     #common gene names
  e <- intersect(d,c)     #common gene names
  Lists <- list(a, b, c)  #put the word vectors into a list to supply lapply  
  Lists <- lapply(Lists, function(x) as.character(unlist(x)))
  items <- sort(unique(unlist(Lists)))   #put in alphabetical order
  #MAT <- matrix(rep(0, length(items)*length(Lists)), ncol=3)  #make a matrix of 0's
  #names <- c(label1,label2,label3)   
  #colnames(MAT) <- names
  #rownames(MAT) <- items
  #lapply(seq_along(Lists), function(i) {   #fill the matrix
   #   MAT[items %in% Lists[[i]], i] <<- table(Lists[[i]]) })
MAT <- LoadCachedObject(db, 'mat_key')
   a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Results/',sep='')
   outputName = paste(label1,'_',label2,'_',label3,'_genes_in_intersection.txt',sep='')
   b = paste(a,outputName,sep='')
   #write.table(e, file = b , quote=FALSE, sep='    ', row.names=FALSE)
   a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Plots/',sep='') 
   outputName = paste(label1,'_',label2,'_',label3,'_VennDiagramDE.pdf',sep='')
   b = paste(a,outputName,sep='')
   vennDiagram(MAT,circle.col= c('red','green','yellow'),main='Venn Diagram of DE genes')

plot of chunk unnamed-chunk-17


DEC up-regulated genes

The DEC up-regulated genes have been intersected with E2 up-regulated and down-regulated genes.

Here starts the automatically generated code

require(limma)
dbfilename <- 'DEC_UP_E2_UP_E2_DOWN_venn3de_db'
db <- InitDb(db.name=dbfilename, db.path='cache')
x <- LoadCachedObject(db, 'maindataframe1_key')
y <- LoadCachedObject(db, 'maindataframe2_key')
z <- LoadCachedObject(db, 'maindataframe3_key')
the.file1 <- LoadCachedObject(db, 'thefile1_key')
the.file2 <- LoadCachedObject(db, 'thefile2_key')
the.file3 <- LoadCachedObject(db, 'thefile3_key')
label1 <- LoadCachedObject(db, 'label1_key')
label2 <- LoadCachedObject(db, 'label2_key')
label3 <- LoadCachedObject(db, 'label3_key')
  a = row.names(x)
  b = row.names(y)
  c = row.names(z)
  d <- intersect(a,b)     #common gene names
  e <- intersect(d,c)     #common gene names
  Lists <- list(a, b, c)  #put the word vectors into a list to supply lapply  
  Lists <- lapply(Lists, function(x) as.character(unlist(x)))
  items <- sort(unique(unlist(Lists)))   #put in alphabetical order
  #MAT <- matrix(rep(0, length(items)*length(Lists)), ncol=3)  #make a matrix of 0's
  #names <- c(label1,label2,label3)   
  #colnames(MAT) <- names
  #rownames(MAT) <- items
  #lapply(seq_along(Lists), function(i) {   #fill the matrix
   #   MAT[items %in% Lists[[i]], i] <<- table(Lists[[i]]) })
MAT <- LoadCachedObject(db, 'mat_key')
   a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Results/',sep='')
   outputName = paste(label1,'_',label2,'_',label3,'_genes_in_intersection.txt',sep='')
   b = paste(a,outputName,sep='')
   #write.table(e, file = b , quote=FALSE, sep='    ', row.names=FALSE)
   a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Plots/',sep='') 
   outputName = paste(label1,'_',label2,'_',label3,'_VennDiagramDE.pdf',sep='')
   b = paste(a,outputName,sep='')
   vennDiagram(MAT,circle.col= c('red','green','yellow'),main='Venn Diagram of DE genes')

plot of chunk unnamed-chunk-18


DEC down-regulated genes

The DEC down-regulated genes have been intersected with E2 up-regulated and down-regulated genes.

Here starts the automatically generated code

require(limma)
dbfilename <- 'DEC_DOWN_E2_UP_E2_DOWN_venn3de_db'
db <- InitDb(db.name=dbfilename, db.path='cache')
x <- LoadCachedObject(db, 'maindataframe1_key')
y <- LoadCachedObject(db, 'maindataframe2_key')
z <- LoadCachedObject(db, 'maindataframe3_key')
the.file1 <- LoadCachedObject(db, 'thefile1_key')
the.file2 <- LoadCachedObject(db, 'thefile2_key')
the.file3 <- LoadCachedObject(db, 'thefile3_key')
label1 <- LoadCachedObject(db, 'label1_key')
label2 <- LoadCachedObject(db, 'label2_key')
label3 <- LoadCachedObject(db, 'label3_key')
  a = row.names(x)
  b = row.names(y)
  c = row.names(z)
  d <- intersect(a,b)     #common gene names
  e <- intersect(d,c)     #common gene names
  Lists <- list(a, b, c)  #put the word vectors into a list to supply lapply  
  Lists <- lapply(Lists, function(x) as.character(unlist(x)))
  items <- sort(unique(unlist(Lists)))   #put in alphabetical order
  #MAT <- matrix(rep(0, length(items)*length(Lists)), ncol=3)  #make a matrix of 0's
  #names <- c(label1,label2,label3)   
  #colnames(MAT) <- names
  #rownames(MAT) <- items
  #lapply(seq_along(Lists), function(i) {   #fill the matrix
   #   MAT[items %in% Lists[[i]], i] <<- table(Lists[[i]]) })
MAT <- LoadCachedObject(db, 'mat_key')
   a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Results/',sep='')
   outputName = paste(label1,'_',label2,'_',label3,'_genes_in_intersection.txt',sep='')
   b = paste(a,outputName,sep='')
   #write.table(e, file = b , quote=FALSE, sep='    ', row.names=FALSE)
   a = paste(getwd(),'/RNASeqGUI_Projects/',Project,'/Plots/',sep='') 
   outputName = paste(label1,'_',label2,'_',label3,'_VennDiagramDE.pdf',sep='')
   b = paste(a,outputName,sep='')
   vennDiagram(MAT,circle.col= c('red','green','yellow'),main='Venn Diagram of DE genes')

plot of chunk unnamed-chunk-19


Functional Analysis Step

In order to understand if our results regulate revelevant pathways or Gene Ontology terms, a functional analysis has been performed on them. A Gene Ontology and Pathway analysis has been performed with Gage and David packages.

GAGE

To work with Gage the result file of differential expression step is necessary.

Gene Ontology on DEC up-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt ,geneSet1: FALSE ,geneSet2: TRUE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE ,conversion2: FALSE ,conversion3: FALSE ,n: 1 ,

This R code has been run:

 require(biomaRt)
 require(gage)
 require(pathview)
 require(lattice)
 geneSet1 <- FALSE
 geneSet2 <-  TRUE
 cachedbname = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt_FALSE_TRUE_gage_db'
 db <- InitDb(db.name=cachedbname, db.path='cache')
 fileName <- LoadCachedObject(db, 'filename_key')
 Project <- LoadCachedObject(db, 'project_key')
 geneSet1 <- LoadCachedObject(db, 'geneset1_key')
 geneSet2 <- LoadCachedObject(db, 'geneset2_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 dme <- LoadCachedObject(db, 'dme_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 conversion2 <- LoadCachedObject(db, 'conversion2_key')
 conversion3 <- LoadCachedObject(db, 'conversion3_key')
 n <- LoadCachedObject(db, 'n_key')
 res = NULL 
 print('Expression data selected: ')
 ## [1] "Expression data selected: "
 print('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt')
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt"
 file.name = read.table('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt', row.names=1, header=TRUE)
 print('dimensions:')
 ## [1] "dimensions:"
 head(file.name)
 ##                    DEC_mean UNTR_mean         M         D      prob
 ## ENSMUSG00000024235 1873.258  1235.289 0.6007003  637.9683 0.9744784
 ## ENSMUSG00000024236 3791.390  2408.594 0.6545356 1382.7959 0.9850969
 ## ENSMUSG00000006740 5218.663  3729.466 0.4847110 1489.1967 0.9697839
 ## ENSMUSG00000024290 3261.294  2433.127 0.4226331  828.1673 0.9561475
 ## ENSMUSG00000024294 4922.510  3752.394 0.3915829 1170.1164 0.9525335
 ## ENSMUSG00000056124 3262.128  2359.437 0.4673712  902.6919 0.9654620
 ##                      ranking
 ## ENSMUSG00000024235  637.9685
 ## ENSMUSG00000024236 1382.7960
 ## ENSMUSG00000006740 1489.1968
 ## ENSMUSG00000024290  828.1674
 ## ENSMUSG00000024294 1170.1164
 ## ENSMUSG00000056124  902.6920
 kegg.gs = NULL
 go.gs = NULL
 if (geneSet1==TRUE){ 
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 kegg.gs=ks$kg.sets}
 if (geneSet2==TRUE){ data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets}
 if(conversion==TRUE){ #ensembl gene ids to entrez genes
 if(hsa==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='hsapiens_gene_ensembl')} 
 if(mmu==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='mmusculus_gene_ensembl')} 
 if(dme==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='dmelanogaster_gene_ensembl')} 
 if(dre==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='drerio_gene_ensembl')}} 

 file.name <- LoadCachedObject(db, 'filenameconverted_key')
 print('Expression data modified:')
 ## [1] "Expression data modified:"
 print(dim(file.name))
 ## [1] 931   6
 print(head(file.name))
 ##        DEC_mean UNTR_mean         M          D      prob    ranking
 ## 11303 21460.367 10163.520 1.0782746 11296.8473 0.9980626 11296.8474
 ## 11352  1702.958  1154.060 0.5613248   548.8982 0.9687779   548.8985
 ## 11426 14327.668  7186.994 0.9953434  7140.6739 0.9972057  7140.6740
 ## 11432  8559.344  5743.498 0.5755704  2815.8454 0.9819672  2815.8454
 ## 11479  1593.209  1149.163 0.4713517   444.0457 0.9545082   444.0459
 ## 11491  9032.251  6217.521 0.5387460  2814.7293 0.9774218  2814.7294
 out.suffix=NULL
 fc=NULL
 exp.fc <- LoadCachedObject(db, 'expfc_key')
 out.suffix <- LoadCachedObject(db, 'outsuffix_key')
 #if (colnames(file.name)[1] == 'log2FoldChange') {fc=file.name[,'log2FoldChange']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='deseq' }
 #if(colnames(file.name)[1] == 'logFC'){ fc=file.name[,'logFC']
 #names(fc)=rownames(file.name)
 #out.suffix='edger'}
 #if(colnames(file.name)[3] == 'M'){ fc=file.name[,'M']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='noiseq'}
 #exp.fc=fc
 #if (geneSet1==TRUE){res <- gage(exp.fc, gsets = kegg.gs, ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)
 #}else{res <- gage(exp.fc, gsets = go.gs  , ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)}
 res <- LoadCachedObject(db, 'res_key')
 print('First lines of results:')
 ## [1] "First lines of results:"
 print(head(res[[1]],10))
 ##                                                                                                                                                                            p.geomean
 ## GO:0035458 cellular response to interferon-beta                                                                                                                         4.438578e-16
 ## GO:0035456 response to interferon-beta                                                                                                                                  4.122978e-15
 ## GO:0051607 defense response to virus                                                                                                                                    4.981495e-15
 ## GO:0009615 response to virus                                                                                                                                            1.215015e-13
 ## GO:0098542 defense response to other organism                                                                                                                           3.649247e-13
 ## GO:0003690 double-stranded DNA binding                                                                                                                                  5.333721e-12
 ## GO:0071345 cellular response to cytokine stimulus                                                                                                                       1.107142e-11
 ## GO:0001078 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in negative regulation of transcription 2.478565e-11
 ## GO:0034097 response to cytokine                                                                                                                                         3.355081e-11
 ## GO:0045087 innate immune response                                                                                                                                       1.939811e-10
 ##                                                                                                                                                                         stat.mean
 ## GO:0035458 cellular response to interferon-beta                                                                                                                          8.041464
 ## GO:0035456 response to interferon-beta                                                                                                                                   7.763742
 ## GO:0051607 defense response to virus                                                                                                                                     7.739728
 ## GO:0009615 response to virus                                                                                                                                             7.322715
 ## GO:0098542 defense response to other organism                                                                                                                            7.173724
 ## GO:0003690 double-stranded DNA binding                                                                                                                                   6.797197
 ## GO:0071345 cellular response to cytokine stimulus                                                                                                                        6.691147
 ## GO:0001078 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in negative regulation of transcription  6.572218
 ## GO:0034097 response to cytokine                                                                                                                                          6.526996
 ## GO:0045087 innate immune response                                                                                                                                        6.258796
 ##                                                                                                                                                                                p.val
 ## GO:0035458 cellular response to interferon-beta                                                                                                                         4.438578e-16
 ## GO:0035456 response to interferon-beta                                                                                                                                  4.122978e-15
 ## GO:0051607 defense response to virus                                                                                                                                    4.981495e-15
 ## GO:0009615 response to virus                                                                                                                                            1.215015e-13
 ## GO:0098542 defense response to other organism                                                                                                                           3.649247e-13
 ## GO:0003690 double-stranded DNA binding                                                                                                                                  5.333721e-12
 ## GO:0071345 cellular response to cytokine stimulus                                                                                                                       1.107142e-11
 ## GO:0001078 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in negative regulation of transcription 2.478565e-11
 ## GO:0034097 response to cytokine                                                                                                                                         3.355081e-11
 ## GO:0045087 innate immune response                                                                                                                                       1.939811e-10
 ##                                                                                                                                                                                q.val
 ## GO:0035458 cellular response to interferon-beta                                                                                                                         7.385794e-13
 ## GO:0035456 response to interferon-beta                                                                                                                                  2.763069e-12
 ## GO:0051607 defense response to virus                                                                                                                                    2.763069e-12
 ## GO:0009615 response to virus                                                                                                                                            5.054461e-11
 ## GO:0098542 defense response to other organism                                                                                                                           1.214469e-10
 ## GO:0003690 double-stranded DNA binding                                                                                                                                  1.479219e-09
 ## GO:0071345 cellular response to cytokine stimulus                                                                                                                       2.631834e-09
 ## GO:0001078 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in negative regulation of transcription 5.155414e-09
 ## GO:0034097 response to cytokine                                                                                                                                         6.203172e-09
 ## GO:0045087 innate immune response                                                                                                                                       3.227845e-08
 ##                                                                                                                                                                         set.size
 ## GO:0035458 cellular response to interferon-beta                                                                                                                               16
 ## GO:0035456 response to interferon-beta                                                                                                                                        19
 ## GO:0051607 defense response to virus                                                                                                                                          43
 ## GO:0009615 response to virus                                                                                                                                                  48
 ## GO:0098542 defense response to other organism                                                                                                                                 73
 ## GO:0003690 double-stranded DNA binding                                                                                                                                        19
 ## GO:0071345 cellular response to cytokine stimulus                                                                                                                             55
 ## GO:0001078 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in negative regulation of transcription       15
 ## GO:0034097 response to cytokine                                                                                                                                               73
 ## GO:0045087 innate immune response                                                                                                                                             93
 ##                                                                                                                                                                                 exp1
 ## GO:0035458 cellular response to interferon-beta                                                                                                                         4.438578e-16
 ## GO:0035456 response to interferon-beta                                                                                                                                  4.122978e-15
 ## GO:0051607 defense response to virus                                                                                                                                    4.981495e-15
 ## GO:0009615 response to virus                                                                                                                                            1.215015e-13
 ## GO:0098542 defense response to other organism                                                                                                                           3.649247e-13
 ## GO:0003690 double-stranded DNA binding                                                                                                                                  5.333721e-12
 ## GO:0071345 cellular response to cytokine stimulus                                                                                                                       1.107142e-11
 ## GO:0001078 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity involved in negative regulation of transcription 2.478565e-11
 ## GO:0034097 response to cytokine                                                                                                                                         3.355081e-11
 ## GO:0045087 innate immune response                                                                                                                                       1.939811e-10
 #sel <- res$greater[, 'q.val'] < 0.01 & !is.na(res$greater[, 'q.val'])
 #path.ids <- rownames(res$greater)[sel]
 #sel.l <- res$less[, 'q.val'] < 0.01 & !is.na(res$less[,'q.val'])
 #path.ids.l <- rownames(res$less)[sel.l]
 #path.ids2 <- substr(c(path.ids, path.ids.l), 1, 8) #put greater and less together
 path.ids2 <- LoadCachedObject(db, 'finalresults_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(path.ids2))
 ## [1] "GO:00354" "GO:00354" "GO:00516" "GO:00096" "GO:00985" "GO:00036"
 #pv.out.list <- sapply(path.ids2, function(pid){ pathview( gene.data = exp.fc, pathway.id = pid, species = specie, out.suffix=out.suffix)})

Gene Ontology on DEC down-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt ,geneSet1: FALSE ,geneSet2: TRUE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE ,conversion2: FALSE ,conversion3: FALSE ,n: 1 ,

This R code has been run:

 require(biomaRt)
 require(gage)
 require(pathview)
 require(lattice)
 geneSet1 <- FALSE
 geneSet2 <-  TRUE
 cachedbname = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt_FALSE_TRUE_gage_db'
 db <- InitDb(db.name=cachedbname, db.path='cache')
 fileName <- LoadCachedObject(db, 'filename_key')
 Project <- LoadCachedObject(db, 'project_key')
 geneSet1 <- LoadCachedObject(db, 'geneset1_key')
 geneSet2 <- LoadCachedObject(db, 'geneset2_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 dme <- LoadCachedObject(db, 'dme_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 conversion2 <- LoadCachedObject(db, 'conversion2_key')
 conversion3 <- LoadCachedObject(db, 'conversion3_key')
 n <- LoadCachedObject(db, 'n_key')
 res=NULL 
 print('Expression data selected: ')
 ## [1] "Expression data selected: "
 print('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt')
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt"
 file.name = read.table('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt', row.names=1, header=TRUE)
 print('dimensions:')
 ## [1] "dimensions:"
 print(head(file.name))
 ##                     DEC_mean UNTR_mean          M         D      prob
 ## ENSMUSG00000063889  1341.497  1927.405 -0.5228157  585.9078 0.9659836
 ## ENSMUSG00000002477  1122.267  1572.918 -0.4870281  450.6514 0.9572653
 ## ENSMUSG00000024424  2829.878  4108.648 -0.5379237 1278.7698 0.9754844
 ## ENSMUSG00000024381  4013.433  5221.045 -0.3795020 1207.6126 0.9504844
 ## ENSMUSG00000090523  1423.218  2019.995 -0.5051948  596.7766 0.9650522
 ## ENSMUSG00000037815 20141.680 26196.586 -0.3791948 6054.9058 0.9534650
 ##                       ranking
 ## ENSMUSG00000063889  -585.9080
 ## ENSMUSG00000002477  -450.6517
 ## ENSMUSG00000024424 -1278.7699
 ## ENSMUSG00000024381 -1207.6127
 ## ENSMUSG00000090523  -596.7768
 ## ENSMUSG00000037815 -6054.9058
 kegg.gs = NULL
 go.gs = NULL
 if (geneSet1==TRUE){ 
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 kegg.gs=ks$kg.sets}
 if (geneSet2==TRUE){ data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets}
 ## Gene ID type for 'mouse' is: 'EG'
 if(conversion==TRUE){ #ensembl gene ids to entrez genes
 if(hsa==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='hsapiens_gene_ensembl')} 
 if(mmu==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='mmusculus_gene_ensembl')} 
 if(dme==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='dmelanogaster_gene_ensembl')} 
 if(dre==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='drerio_gene_ensembl')}} 

 file.name <- LoadCachedObject(db, 'filenameconverted_key')
 print('Expression data modified:')
 ## [1] "Expression data modified:"
 print(dim(file.name))
 ## [1] 995   6
 print(head(file.name))
 ##          DEC_mean  UNTR_mean          M          D      prob     ranking
 ## 11409   1663.4981   2492.903 -0.5836063   829.4045 0.9766393   -829.4047
 ## 11416   1625.0272   2252.548 -0.4710940   627.5208 0.9609911   -627.5210
 ## 11461 252952.6590 343899.257 -0.4431186 90946.5983 0.9672504 -90946.5983
 ## 11465  77439.7335 101541.732 -0.3909269 24101.9988 0.9563338 -24101.9988
 ## 11475    952.0971   1374.974 -0.5302232   422.8764 0.9596125   -422.8768
 ## 11540   1389.6178   2233.098 -0.6843586   843.4805 0.9836438   -843.4808
 out.suffix=NULL
 fc=NULL
 exp.fc <- LoadCachedObject(db, 'expfc_key')
 out.suffix <- LoadCachedObject(db, 'outsuffix_key')
 #if (colnames(file.name)[1] == 'log2FoldChange') {fc=file.name[,'log2FoldChange']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='deseq' }
 #if(colnames(file.name)[1] == 'logFC'){ fc=file.name[,'logFC']
 #names(fc)=rownames(file.name)
 #out.suffix='edger'}
 #if(colnames(file.name)[3] == 'M'){ fc=file.name[,'M']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='noiseq'}
 #exp.fc=fc
 #if (geneSet1==TRUE){res <- gage(exp.fc, gsets = kegg.gs, ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)
 #}else{res <- gage(exp.fc, gsets = go.gs  , ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)}
 res <- LoadCachedObject(db, 'res_key')
 print('First lines of results:')
 ## [1] "First lines of results:"
 print(head(res[[1]],10))
 ##                                                  p.geomean stat.mean
 ## GO:0003735 structural constituent of ribosome 0.0002410070  3.490557
 ## GO:0022627 cytosolic small ribosomal subunit  0.0003205543  3.413599
 ## GO:0005840 ribosome                           0.0005093112  3.285332
 ## GO:0005126 cytokine receptor binding          0.0005920397  3.242688
 ## GO:0022626 cytosolic ribosome                 0.0010730612  3.069229
 ## GO:0015935 small ribosomal subunit            0.0016157705  2.944809
 ## GO:0005125 cytokine activity                  0.0018521616  2.902301
 ## GO:0044391 ribosomal subunit                  0.0025001536  2.807014
 ## GO:0005578 proteinaceous extracellular matrix 0.0044961935  2.612343
 ## GO:0031012 extracellular matrix               0.0047064300  2.596684
 ##                                                      p.val     q.val
 ## GO:0003735 structural constituent of ribosome 0.0002410070 0.1944850
 ## GO:0022627 cytosolic small ribosomal subunit  0.0003205543 0.1944850
 ## GO:0005840 ribosome                           0.0005093112 0.1944850
 ## GO:0005126 cytokine receptor binding          0.0005920397 0.1944850
 ## GO:0022626 cytosolic ribosome                 0.0010730612 0.2820005
 ## GO:0015935 small ribosomal subunit            0.0016157705 0.3476772
 ## GO:0005125 cytokine activity                  0.0018521616 0.3476772
 ## GO:0044391 ribosomal subunit                  0.0025001536 0.4106502
 ## GO:0005578 proteinaceous extracellular matrix 0.0044961935 0.5993361
 ## GO:0031012 extracellular matrix               0.0047064300 0.5993361
 ##                                               set.size         exp1
 ## GO:0003735 structural constituent of ribosome       88 0.0002410070
 ## GO:0022627 cytosolic small ribosomal subunit        30 0.0003205543
 ## GO:0005840 ribosome                                103 0.0005093112
 ## GO:0005126 cytokine receptor binding                14 0.0005920397
 ## GO:0022626 cytosolic ribosome                       70 0.0010730612
 ## GO:0015935 small ribosomal subunit                  43 0.0016157705
 ## GO:0005125 cytokine activity                        13 0.0018521616
 ## GO:0044391 ribosomal subunit                        91 0.0025001536
 ## GO:0005578 proteinaceous extracellular matrix       24 0.0044961935
 ## GO:0031012 extracellular matrix                     34 0.0047064300
 #sel <- res$greater[, 'q.val'] < 0.01 & !is.na(res$greater[, 'q.val'])
 #path.ids <- rownames(res$greater)[sel]
 #sel.l <- res$less[, 'q.val'] < 0.01 & !is.na(res$less[,'q.val'])
 #path.ids.l <- rownames(res$less)[sel.l]
 #path.ids2 <- substr(c(path.ids, path.ids.l), 1, 8) #put greater and less together
 path.ids2 <- LoadCachedObject(db, 'finalresults_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(path.ids2))
 ## character(0)
 #pv.out.list <- sapply(path.ids2, function(pid){ pathview( gene.data = exp.fc, pathway.id = pid, species = specie, out.suffix=out.suffix)})

Pathway Analysis on DEC up-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt ,geneSet1: TRUE ,geneSet2: FALSE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE ,conversion2: FALSE ,conversion3: FALSE ,n: 1 ,

This R code has been run:

 require(biomaRt)
 require(gage)
 require(pathview)
 require(lattice)
 geneSet1 <- TRUE
 geneSet2 <-  FALSE
 cachedbname = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt_TRUE_FALSE_gage_db'
 db <- InitDb(db.name=cachedbname, db.path='cache')
 fileName <- LoadCachedObject(db, 'filename_key')
 Project <- LoadCachedObject(db, 'project_key')
 geneSet1 <- LoadCachedObject(db, 'geneset1_key')
 geneSet2 <- LoadCachedObject(db, 'geneset2_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 dme <- LoadCachedObject(db, 'dme_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 conversion2 <- LoadCachedObject(db, 'conversion2_key')
 conversion3 <- LoadCachedObject(db, 'conversion3_key')
 n <- LoadCachedObject(db, 'n_key')
 res=NULL 
 print('Expression data selected: ')
 ## [1] "Expression data selected: "
 print('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt')
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt"
 file.name=read.table('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt', row.names=1, header=TRUE)
 print('dimensions:')
 ## [1] "dimensions:"
 print(dim(file.name))
 ## [1] 974   6
 print(head(file.name))
 ##                    DEC_mean UNTR_mean         M         D      prob
 ## ENSMUSG00000024235 1873.258  1235.289 0.6007003  637.9683 0.9744784
 ## ENSMUSG00000024236 3791.390  2408.594 0.6545356 1382.7959 0.9850969
 ## ENSMUSG00000006740 5218.663  3729.466 0.4847110 1489.1967 0.9697839
 ## ENSMUSG00000024290 3261.294  2433.127 0.4226331  828.1673 0.9561475
 ## ENSMUSG00000024294 4922.510  3752.394 0.3915829 1170.1164 0.9525335
 ## ENSMUSG00000056124 3262.128  2359.437 0.4673712  902.6919 0.9654620
 ##                      ranking
 ## ENSMUSG00000024235  637.9685
 ## ENSMUSG00000024236 1382.7960
 ## ENSMUSG00000006740 1489.1968
 ## ENSMUSG00000024290  828.1674
 ## ENSMUSG00000024294 1170.1164
 ## ENSMUSG00000056124  902.6920
 kegg.gs = NULL
 go.gs = NULL
 if (geneSet1==TRUE){ 
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 kegg.gs=ks$kg.sets}
 if (geneSet2==TRUE){ data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets}
 if(conversion==TRUE){ #ensembl gene ids to entrez genes
 if(hsa==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='hsapiens_gene_ensembl')} 
 if(mmu==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='mmusculus_gene_ensembl')} 
 if(dme==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='dmelanogaster_gene_ensembl')} 
 if(dre==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='drerio_gene_ensembl')}} 

 file.name <- LoadCachedObject(db, 'filenameconverted_key')
 print('Expression data modified:')
 ## [1] "Expression data modified:"
 print(dim(file.name))
 ## [1] 931   6
 print(head(file.name))
 ##        DEC_mean UNTR_mean         M          D      prob    ranking
 ## 11303 21460.367 10163.520 1.0782746 11296.8473 0.9980626 11296.8474
 ## 11352  1702.958  1154.060 0.5613248   548.8982 0.9687779   548.8985
 ## 11426 14327.668  7186.994 0.9953434  7140.6739 0.9972057  7140.6740
 ## 11432  8559.344  5743.498 0.5755704  2815.8454 0.9819672  2815.8454
 ## 11479  1593.209  1149.163 0.4713517   444.0457 0.9545082   444.0459
 ## 11491  9032.251  6217.521 0.5387460  2814.7293 0.9774218  2814.7294
 out.suffix=NULL
 fc=NULL
 exp.fc <- LoadCachedObject(db, 'expfc_key')
 out.suffix <- LoadCachedObject(db, 'outsuffix_key')
 #if (colnames(file.name)[1] == 'log2FoldChange') {fc=file.name[,'log2FoldChange']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='deseq' }
 #if(colnames(file.name)[1] == 'logFC'){ fc=file.name[,'logFC']
 #names(fc)=rownames(file.name)
 #out.suffix='edger'}
 #if(colnames(file.name)[3] == 'M'){ fc=file.name[,'M']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='noiseq'}
 #exp.fc=fc
 #if (geneSet1==TRUE){res <- gage(exp.fc, gsets = kegg.gs, ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)
 #}else{res <- gage(exp.fc, gsets = go.gs  , ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)}
 res <- LoadCachedObject(db, 'res_key')
 print('First lines of results:')
 ## [1] "First lines of results:"
 print(head(res[[1]],10))
 ##                                                    p.geomean stat.mean
 ## mmu05160 Hepatitis C                            2.981657e-07  4.992401
 ## mmu05164 Influenza A                            4.379829e-07  4.917634
 ## mmu05168 Herpes simplex infection               4.526240e-07  4.911191
 ## mmu05162 Measles                                1.773026e-05  4.135226
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03  3.011642
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03  2.624313
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02  1.891229
 ## mmu05161 Hepatitis B                            3.748099e-02  1.780697
 ## mmu04668 TNF signaling pathway                  4.778172e-02  1.666753
 ## mmu05203 Viral carcinogenesis                   7.479971e-02  1.440948
 ##                                                        p.val        q.val
 ## mmu05160 Hepatitis C                            2.981657e-07 1.025948e-05
 ## mmu05164 Influenza A                            4.379829e-07 1.025948e-05
 ## mmu05168 Herpes simplex infection               4.526240e-07 1.025948e-05
 ## mmu05162 Measles                                1.773026e-05 3.014144e-04
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03 1.766902e-02
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03 4.920026e-02
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02 2.845983e-01
 ## mmu05161 Hepatitis B                            3.748099e-02 3.185884e-01
 ## mmu04668 TNF signaling pathway                  4.778172e-02 3.610174e-01
 ## mmu05203 Viral carcinogenesis                   7.479971e-02 5.086380e-01
 ##                                                 set.size         exp1
 ## mmu05160 Hepatitis C                                  23 2.981657e-07
 ## mmu05164 Influenza A                                  37 4.379829e-07
 ## mmu05168 Herpes simplex infection                     40 4.526240e-07
 ## mmu05162 Measles                                      29 1.773026e-05
 ## mmu04622 RIG-I-like receptor signaling pathway        13 1.299193e-03
 ## mmu04623 Cytosolic DNA-sensing pathway                13 4.341199e-03
 ## mmu04060 Cytokine-cytokine receptor interaction       23 2.929689e-02
 ## mmu05161 Hepatitis B                                  22 3.748099e-02
 ## mmu04668 TNF signaling pathway                        19 4.778172e-02
 ## mmu05203 Viral carcinogenesis                         21 7.479971e-02
 #sel <- res$greater[, 'q.val'] < 0.01 & !is.na(res$greater[, 'q.val'])
 #path.ids <- rownames(res$greater)[sel]
 #sel.l <- res$less[, 'q.val'] < 0.01 & !is.na(res$less[,'q.val'])
 #path.ids.l <- rownames(res$less)[sel.l]
 #path.ids2 <- substr(c(path.ids, path.ids.l), 1, 8) #put greater and less together
 path.ids2 <- LoadCachedObject(db, 'finalresults_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(path.ids2))
 ## [1] "mmu05160" "mmu05164" "mmu05168" "mmu05162" "mmu04622" "mmu04623"
 #pv.out.list <- sapply(path.ids2, function(pid){ pathview( gene.data = exp.fc, pathway.id = pid, species = specie, out.suffix=out.suffix)})

Pathway Analysis on E2 up-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt ,geneSet1: TRUE ,geneSet2: FALSE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE ,conversion2: FALSE ,conversion3: FALSE ,n: 1 ,

This R code has been run:

 require(biomaRt)
 require(gage)
 require(pathview)
 require(lattice)
 geneSet1 <- TRUE
 geneSet2 <-  FALSE
 cachedbname = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt_TRUE_FALSE_gage_db'
 db <- InitDb(db.name=cachedbname, db.path='cache')
 fileName <- LoadCachedObject(db, 'filename_key')
 Project <- LoadCachedObject(db, 'project_key')
 geneSet1 <- LoadCachedObject(db, 'geneset1_key')
 geneSet2 <- LoadCachedObject(db, 'geneset2_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 dme <- LoadCachedObject(db, 'dme_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 conversion2 <- LoadCachedObject(db, 'conversion2_key')
 conversion3 <- LoadCachedObject(db, 'conversion3_key')
 n <- LoadCachedObject(db, 'n_key')
 res=NULL 
 print('Expression data selected: ')
 ## [1] "Expression data selected: "
 print('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt')
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt"
 file.name=read.table('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt', row.names=1, header=TRUE)
 print('dimensions:')
 ## [1] "dimensions:"
 head(file.name)
 ##                     E2_mean UNTR_mean         M         D      prob
 ## ENSMUSG00000024236 3491.317 2408.5939 0.5355801 1082.7228 0.9695231
 ## ENSMUSG00000006740 5179.638 3729.4665 0.4738820 1450.1715 0.9626677
 ## ENSMUSG00000024290 3519.108 2433.1265 0.5323986 1085.9818 0.9687034
 ## ENSMUSG00000024294 5623.267 3752.3939 0.5835973 1870.8733 0.9753353
 ## ENSMUSG00000033382 3250.364 2356.2755 0.4640931  894.0885 0.9585320
 ## ENSMUSG00000041915 1016.327  589.2449 0.7864249  427.0818 0.9752981
 ##                      ranking
 ## ENSMUSG00000024236 1082.7229
 ## ENSMUSG00000006740 1450.1715
 ## ENSMUSG00000024290 1085.9820
 ## ENSMUSG00000024294 1870.8734
 ## ENSMUSG00000033382  894.0886
 ## ENSMUSG00000041915  427.0825
 kegg.gs = NULL
 go.gs = NULL
 if (geneSet1==TRUE){ 
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 kegg.gs=ks$kg.sets}
 if (geneSet2==TRUE){ data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets}
 if(conversion==TRUE){ #ensembl gene ids to entrez genes
 if(hsa==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='hsapiens_gene_ensembl')} 
 if(mmu==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='mmusculus_gene_ensembl')} 
 if(dme==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='dmelanogaster_gene_ensembl')} 
 if(dre==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='drerio_gene_ensembl')}} 

 file.name <- LoadCachedObject(db, 'filenameconverted_key')
 print('Expression data modified:')
 ## [1] "Expression data modified:"
 print(dim(file.name))
 ## [1] 805   6
 print(head(file.name))
 ##         E2_mean UNTR_mean         M         D      prob   ranking
 ## 11305  2251.808  1587.416 0.5044036  664.3920 0.9614754  664.3922
 ## 11352  1756.638  1154.060 0.6060989  602.5782 0.9710134  602.5785
 ## 11426 13403.227  7186.994 0.8991199 6216.2327 0.9929955 6216.2327
 ## 11433  8977.105  5383.496 0.7377070 3593.6095 0.9877049 3593.6096
 ## 11479  1613.703  1149.163 0.4897913  464.5398 0.9542474  464.5400
 ## 11492  5231.593  3654.661 0.5175125 1576.9314 0.9691505 1576.9315
 out.suffix=NULL
 fc=NULL
 exp.fc <- LoadCachedObject(db, 'expfc_key')
 out.suffix <- LoadCachedObject(db, 'outsuffix_key')
 #if (colnames(file.name)[1] == 'log2FoldChange') {fc=file.name[,'log2FoldChange']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='deseq' }
 #if(colnames(file.name)[1] == 'logFC'){ fc=file.name[,'logFC']
 #names(fc)=rownames(file.name)
 #out.suffix='edger'}
 #if(colnames(file.name)[3] == 'M'){ fc=file.name[,'M']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='noiseq'}
 #exp.fc=fc
 #if (geneSet1==TRUE){res <- gage(exp.fc, gsets = kegg.gs, ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)
 #}else{res <- gage(exp.fc, gsets = go.gs  , ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)}
 res <- LoadCachedObject(db, 'res_key')
 print('First lines of results:')
 ## [1] "First lines of results:"
 head(res[[1]],10)
 ##                                               p.geomean stat.mean
 ## mmu05410 Hypertrophic cardiomyopathy (HCM) 0.0001113742 3.6917135
 ## mmu05414 Dilated cardiomyopathy            0.0005946565 3.2414308
 ## mmu04810 Regulation of actin cytoskeleton  0.0066885871 2.4735669
 ## mmu04514 Cell adhesion molecules (CAMs)    0.0088502965 2.3718225
 ## mmu04145 Phagosome                         0.0445257276 1.7004226
 ## mmu04510 Focal adhesion                    0.0882811808 1.3514155
 ## mmu05206 MicroRNAs in cancer               0.1482244541 1.0440789
 ## mmu04120 Ubiquitin mediated proteolysis    0.1607398999 0.9914215
 ## mmu04151 PI3K-Akt signaling pathway        0.1820232294 0.9076816
 ## mmu00310 Lysine degradation                0.2182508717 0.7781141
 ##                                                   p.val       q.val
 ## mmu05410 Hypertrophic cardiomyopathy (HCM) 0.0001113742 0.006459701
 ## mmu05414 Dilated cardiomyopathy            0.0005946565 0.017245038
 ## mmu04810 Regulation of actin cytoskeleton  0.0066885871 0.128329299
 ## mmu04514 Cell adhesion molecules (CAMs)    0.0088502965 0.128329299
 ## mmu04145 Phagosome                         0.0445257276 0.516498440
 ## mmu04510 Focal adhesion                    0.0882811808 0.853384748
 ## mmu05206 MicroRNAs in cancer               0.1482244541 0.932795641
 ## mmu04120 Ubiquitin mediated proteolysis    0.1607398999 0.932795641
 ## mmu04151 PI3K-Akt signaling pathway        0.1820232294 0.932795641
 ## mmu00310 Lysine degradation                0.2182508717 0.932795641
 ##                                            set.size         exp1
 ## mmu05410 Hypertrophic cardiomyopathy (HCM)       10 0.0001113742
 ## mmu05414 Dilated cardiomyopathy                  11 0.0005946565
 ## mmu04810 Regulation of actin cytoskeleton        25 0.0066885871
 ## mmu04514 Cell adhesion molecules (CAMs)          11 0.0088502965
 ## mmu04145 Phagosome                               11 0.0445257276
 ## mmu04510 Focal adhesion                          25 0.0882811808
 ## mmu05206 MicroRNAs in cancer                     22 0.1482244541
 ## mmu04120 Ubiquitin mediated proteolysis          12 0.1607398999
 ## mmu04151 PI3K-Akt signaling pathway              24 0.1820232294
 ## mmu00310 Lysine degradation                      11 0.2182508717
 #sel <- res$greater[, 'q.val'] < 0.01 & !is.na(res$greater[, 'q.val'])
 #path.ids <- rownames(res$greater)[sel]
 #sel.l <- res$less[, 'q.val'] < 0.01 & !is.na(res$less[,'q.val'])
 #path.ids.l <- rownames(res$less)[sel.l]
 #path.ids2 <- substr(c(path.ids, path.ids.l), 1, 8) #put greater and less together
 path.ids2 <- LoadCachedObject(db, 'finalresults_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 head(path.ids2)
 ## [1] "mmu05410" "mmu05414"
 #pv.out.list <- sapply(path.ids2, function(pid){ pathview( gene.data = exp.fc, pathway.id = pid, species = specie, out.suffix=out.suffix)})

Gene Ontology on E2 down-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt ,geneSet1: TRUE ,geneSet2: FALSE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE ,conversion2: FALSE ,conversion3: FALSE ,n: 1 ,

This R code has been run:

 require(biomaRt)
 require(gage)
 require(pathview)
 require(lattice)
 geneSet1 <- TRUE
 geneSet2 <-  FALSE
 cachedbname = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt_TRUE_FALSE_gage_db'
 db <- InitDb(db.name=cachedbname, db.path='cache')
 fileName <- LoadCachedObject(db, 'filename_key')
 Project <- LoadCachedObject(db, 'project_key')
 geneSet1 <- LoadCachedObject(db, 'geneset1_key')
 geneSet2 <- LoadCachedObject(db, 'geneset2_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 dme <- LoadCachedObject(db, 'dme_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 conversion2 <- LoadCachedObject(db, 'conversion2_key')
 conversion3 <- LoadCachedObject(db, 'conversion3_key')
 n <- LoadCachedObject(db, 'n_key')
 res = NULL 
 print('Expression data selected: ')
 ## [1] "Expression data selected: "
 print('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt')
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt"
 file.name=read.table('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt', row.names=1, header=TRUE)
 print('dimensions:')
 ## [1] "dimensions:"
 head(file.name)
 ##                      E2_mean UNTR_mean          M         D      prob
 ## ENSMUSG00000002477  931.9589  1572.918 -0.7551052  640.9591 0.9811475
 ## ENSMUSG00000040957  312.1442   649.083 -1.0561903  336.9388 0.9732489
 ## ENSMUSG00000090523 1390.8650  2019.995 -0.5383691  629.1297 0.9647541
 ## ENSMUSG00000038418  952.2682  3287.633 -1.7876094 2335.3649 0.9967586
 ## ENSMUSG00000024346  904.5954  1433.102 -0.6637968  528.5067 0.9727273
 ## ENSMUSG00000014294 3013.9511  5375.044 -0.8346205 2361.0927 0.9904993
 ##                       ranking
 ## ENSMUSG00000002477  -640.9595
 ## ENSMUSG00000040957  -336.9404
 ## ENSMUSG00000090523  -629.1299
 ## ENSMUSG00000038418 -2335.3656
 ## ENSMUSG00000024346  -528.5071
 ## ENSMUSG00000014294 -2361.0929
 kegg.gs = NULL
 go.gs = NULL
 if (geneSet1==TRUE){ 
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 kegg.gs=ks$kg.sets}
 if (geneSet2==TRUE){ data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets}
 if(conversion==TRUE){ #ensembl gene ids to entrez genes
 if(hsa==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='hsapiens_gene_ensembl')} 
 if(mmu==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='mmusculus_gene_ensembl')} 
 if(dme==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='dmelanogaster_gene_ensembl')} 
 if(dre==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='drerio_gene_ensembl')}} 

 file.name <- LoadCachedObject(db, 'filenameconverted_key')
 print('Expression data modified:')
 ## [1] "Expression data modified:"
 print(dim(file.name))
 ## [1] 936   6
 print(head(file.name))
 ##          E2_mean UNTR_mean          M          D      prob     ranking
 ## 11409  1814.2001  2492.903 -0.4584929   678.7025 0.9561103   -678.7027
 ## 11475   306.2560  1374.974 -2.1665939  1068.7176 0.9949702  -1068.7197
 ## 11520 45375.6035 89520.067 -0.9802943 44144.4632 0.9946349 -44144.4633
 ## 11551   168.8537   402.214 -1.2521896   233.3603 0.9556259   -233.3637
 ## 11568   126.0248  1687.441 -3.7430576  1561.4163 0.9975037  -1561.4208
 ## 11629  1748.0061  3455.473 -0.9831731  1707.4670 0.9925112  -1707.4673
 out.suffix=NULL
 fc=NULL
 exp.fc <- LoadCachedObject(db, 'expfc_key')
 out.suffix <- LoadCachedObject(db, 'outsuffix_key')
 #if (colnames(file.name)[1] == 'log2FoldChange') {fc=file.name[,'log2FoldChange']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='deseq' }
 #if(colnames(file.name)[1] == 'logFC'){ fc=file.name[,'logFC']
 #names(fc)=rownames(file.name)
 #out.suffix='edger'}
 #if(colnames(file.name)[3] == 'M'){ fc=file.name[,'M']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='noiseq'}
 #exp.fc=fc
 #if (geneSet1==TRUE){res <- gage(exp.fc, gsets = kegg.gs, ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)
 #}else{res <- gage(exp.fc, gsets = go.gs  , ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)}
 res <- LoadCachedObject(db, 'res_key')
 print('First lines of results:')
 ## [1] "First lines of results:"
 head(res[[1]],10)
 ##                                                    p.geomean stat.mean
 ## mmu04974 Protein digestion and absorption       4.514822e-23 9.8222596
 ## mmu04512 ECM-receptor interaction               1.334094e-16 8.1874501
 ## mmu04510 Focal adhesion                         1.013753e-15 7.9396512
 ## mmu04151 PI3K-Akt signaling pathway             1.623934e-08 5.5275213
 ## mmu05200 Pathways in cancer                     3.881611e-03 2.6621979
 ## mmu04060 Cytokine-cytokine receptor interaction 1.781937e-02 2.1010253
 ## mmu05160 Hepatitis C                            1.055064e-01 1.2507856
 ## mmu04066 HIF-1 signaling pathway                1.524016e-01 1.0261874
 ## mmu05150 Staphylococcus aureus infection        3.239795e-01 0.4565994
 ## mmu05164 Influenza A                            3.659037e-01 0.3427223
 ##                                                        p.val        q.val
 ## mmu04974 Protein digestion and absorption       4.514822e-23 1.760781e-21
 ## mmu04512 ECM-receptor interaction               1.334094e-16 2.601483e-15
 ## mmu04510 Focal adhesion                         1.013753e-15 1.317879e-14
 ## mmu04151 PI3K-Akt signaling pathway             1.623934e-08 1.583336e-07
 ## mmu05200 Pathways in cancer                     3.881611e-03 3.027656e-02
 ## mmu04060 Cytokine-cytokine receptor interaction 1.781937e-02 1.158259e-01
 ## mmu05160 Hepatitis C                            1.055064e-01 5.878211e-01
 ## mmu04066 HIF-1 signaling pathway                1.524016e-01 7.429579e-01
 ## mmu05150 Staphylococcus aureus infection        3.239795e-01 9.998662e-01
 ## mmu05164 Influenza A                            3.659037e-01 9.998662e-01
 ##                                                 set.size         exp1
 ## mmu04974 Protein digestion and absorption             13 4.514822e-23
 ## mmu04512 ECM-receptor interaction                     11 1.334094e-16
 ## mmu04510 Focal adhesion                               13 1.013753e-15
 ## mmu04151 PI3K-Akt signaling pathway                   21 1.623934e-08
 ## mmu05200 Pathways in cancer                           20 3.881611e-03
 ## mmu04060 Cytokine-cytokine receptor interaction       15 1.781937e-02
 ## mmu05160 Hepatitis C                                  13 1.055064e-01
 ## mmu04066 HIF-1 signaling pathway                      12 1.524016e-01
 ## mmu05150 Staphylococcus aureus infection              10 3.239795e-01
 ## mmu05164 Influenza A                                  13 3.659037e-01
 #sel <- res$greater[, 'q.val'] < 0.01 & !is.na(res$greater[, 'q.val'])
 #path.ids <- rownames(res$greater)[sel]
 #sel.l <- res$less[, 'q.val'] < 0.01 & !is.na(res$less[,'q.val'])
 #path.ids.l <- rownames(res$less)[sel.l]
 #path.ids2 <- substr(c(path.ids, path.ids.l), 1, 8) #put greater and less together
 path.ids2 <- LoadCachedObject(db, 'finalresults_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 head(path.ids2)
 ## [1] "mmu04974" "mmu04512" "mmu04510" "mmu04151" "mmu05200"
 #pv.out.list <- sapply(path.ids2, function(pid){ pathview( gene.data = exp.fc, pathway.id = pid, species = specie, out.suffix=out.suffix)})

Gene Ontology on E2 down-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt ,geneSet1: FALSE ,geneSet2: TRUE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE ,conversion2: FALSE ,conversion3: FALSE ,n: 1 ,

This R code has been run:

 require(biomaRt)
 require(gage)
 require(pathview)
 require(lattice)
 geneSet1 <- FALSE
 geneSet2 <-  TRUE
 cachedbname = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt_FALSE_TRUE_gage_db'
 db <- InitDb(db.name=cachedbname, db.path='cache')
 fileName <- LoadCachedObject(db, 'filename_key')
 Project <- LoadCachedObject(db, 'project_key')
 geneSet1 <- LoadCachedObject(db, 'geneset1_key')
 geneSet2 <- LoadCachedObject(db, 'geneset2_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 dme <- LoadCachedObject(db, 'dme_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 conversion2 <- LoadCachedObject(db, 'conversion2_key')
 conversion3 <- LoadCachedObject(db, 'conversion3_key')
 n <- LoadCachedObject(db, 'n_key')
 res = NULL 
 print('Expression data selected: ')
 ## [1] "Expression data selected: "
 print('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt')
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt"
 file.name=read.table('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_DOWN_genes_NOISeq.txt', row.names=1, header=TRUE)
 print('dimensions:')
 ## [1] "dimensions:"
 head(file.name)
 ##                      E2_mean UNTR_mean          M         D      prob
 ## ENSMUSG00000002477  931.9589  1572.918 -0.7551052  640.9591 0.9811475
 ## ENSMUSG00000040957  312.1442   649.083 -1.0561903  336.9388 0.9732489
 ## ENSMUSG00000090523 1390.8650  2019.995 -0.5383691  629.1297 0.9647541
 ## ENSMUSG00000038418  952.2682  3287.633 -1.7876094 2335.3649 0.9967586
 ## ENSMUSG00000024346  904.5954  1433.102 -0.6637968  528.5067 0.9727273
 ## ENSMUSG00000014294 3013.9511  5375.044 -0.8346205 2361.0927 0.9904993
 ##                       ranking
 ## ENSMUSG00000002477  -640.9595
 ## ENSMUSG00000040957  -336.9404
 ## ENSMUSG00000090523  -629.1299
 ## ENSMUSG00000038418 -2335.3656
 ## ENSMUSG00000024346  -528.5071
 ## ENSMUSG00000014294 -2361.0929
 kegg.gs = NULL
 go.gs = NULL
 if (geneSet1==TRUE){ 
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 kegg.gs=ks$kg.sets}
 if (geneSet2==TRUE){ data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets}
 ## Gene ID type for 'mouse' is: 'EG'
 if(conversion==TRUE){ #ensembl gene ids to entrez genes
 if(hsa==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='hsapiens_gene_ensembl')} 
 if(mmu==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='mmusculus_gene_ensembl')} 
 if(dme==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='dmelanogaster_gene_ensembl')} 
 if(dre==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='drerio_gene_ensembl')}} 

 file.name <- LoadCachedObject(db, 'filenameconverted_key')
 print('Expression data modified:')
 ## [1] "Expression data modified:"
 head(file.name)
 ##          E2_mean UNTR_mean          M          D      prob     ranking
 ## 11409  1814.2001  2492.903 -0.4584929   678.7025 0.9561103   -678.7027
 ## 11475   306.2560  1374.974 -2.1665939  1068.7176 0.9949702  -1068.7197
 ## 11520 45375.6035 89520.067 -0.9802943 44144.4632 0.9946349 -44144.4633
 ## 11551   168.8537   402.214 -1.2521896   233.3603 0.9556259   -233.3637
 ## 11568   126.0248  1687.441 -3.7430576  1561.4163 0.9975037  -1561.4208
 ## 11629  1748.0061  3455.473 -0.9831731  1707.4670 0.9925112  -1707.4673
 out.suffix=NULL
 fc=NULL
 exp.fc <- LoadCachedObject(db, 'expfc_key')
 out.suffix <- LoadCachedObject(db, 'outsuffix_key')
 #if (colnames(file.name)[1] == 'log2FoldChange') {fc=file.name[,'log2FoldChange']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='deseq' }
 #if(colnames(file.name)[1] == 'logFC'){ fc=file.name[,'logFC']
 #names(fc)=rownames(file.name)
 #out.suffix='edger'}
 #if(colnames(file.name)[3] == 'M'){ fc=file.name[,'M']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='noiseq'}
 #exp.fc=fc
 #if (geneSet1==TRUE){res <- gage(exp.fc, gsets = kegg.gs, ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)
 #}else{res <- gage(exp.fc, gsets = go.gs  , ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)}
 res <- LoadCachedObject(db, 'res_key')
 print('First lines of results:')
 ## [1] "First lines of results:"
 head(res[[1]], 10)
 ##                                                    p.geomean stat.mean
 ## GO:0031012 extracellular matrix                 2.106203e-53 15.338748
 ## GO:0005578 proteinaceous extracellular matrix   7.661242e-50 14.796973
 ## GO:0044420 extracellular matrix component       1.274277e-30 11.443021
 ## GO:0005615 extracellular space                  2.232315e-30 11.394288
 ## GO:0030198 extracellular matrix organization    9.172815e-27 10.645268
 ## GO:0043062 extracellular structure organization 9.172815e-27 10.645268
 ## GO:0005581 collagen trimer                      2.927857e-25 10.317787
 ## GO:0001568 blood vessel development             1.131038e-23  9.960812
 ## GO:0001944 vasculature development              1.203891e-23  9.954605
 ## GO:0005539 glycosaminoglycan binding            5.232807e-23  9.807374
 ##                                                        p.val        q.val
 ## GO:0031012 extracellular matrix                 2.106203e-53 2.834949e-50
 ## GO:0005578 proteinaceous extracellular matrix   7.661242e-50 5.156016e-47
 ## GO:0044420 extracellular matrix component       1.274277e-30 5.717254e-28
 ## GO:0005615 extracellular space                  2.232315e-30 7.511739e-28
 ## GO:0030198 extracellular matrix organization    9.172815e-27 2.057768e-24
 ## GO:0043062 extracellular structure organization 9.172815e-27 2.057768e-24
 ## GO:0005581 collagen trimer                      2.927857e-25 5.629851e-23
 ## GO:0001568 blood vessel development             1.131038e-23 1.800486e-21
 ## GO:0001944 vasculature development              1.203891e-23 1.800486e-21
 ## GO:0005539 glycosaminoglycan binding            5.232807e-23 7.043359e-21
 ##                                                 set.size         exp1
 ## GO:0031012 extracellular matrix                       64 2.106203e-53
 ## GO:0005578 proteinaceous extracellular matrix         46 7.661242e-50
 ## GO:0044420 extracellular matrix component             26 1.274277e-30
 ## GO:0005615 extracellular space                       108 2.232315e-30
 ## GO:0030198 extracellular matrix organization          21 9.172815e-27
 ## GO:0043062 extracellular structure organization       21 9.172815e-27
 ## GO:0005581 collagen trimer                            20 2.927857e-25
 ## GO:0001568 blood vessel development                   38 1.131038e-23
 ## GO:0001944 vasculature development                    40 1.203891e-23
 ## GO:0005539 glycosaminoglycan binding                  22 5.232807e-23
 #sel <- res$greater[, 'q.val'] < 0.01 & !is.na(res$greater[, 'q.val'])
 #path.ids <- rownames(res$greater)[sel]
 #sel.l <- res$less[, 'q.val'] < 0.01 & !is.na(res$less[,'q.val'])
 #path.ids.l <- rownames(res$less)[sel.l]
 #path.ids2 <- substr(c(path.ids, path.ids.l), 1, 8) #put greater and less together
 path.ids2 <- LoadCachedObject(db, 'finalresults_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 head(path.ids2)
 ## [1] "GO:00310" "GO:00055" "GO:00444" "GO:00056" "GO:00301" "GO:00430"
 #pv.out.list <- sapply(path.ids2, function(pid){ pathview( gene.data = exp.fc, pathway.id = pid, species = specie, out.suffix=out.suffix)})

Gene Ontology on E2 up-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt ,geneSet1: FALSE ,geneSet2: TRUE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE ,conversion2: FALSE ,conversion3: FALSE ,n: 1 ,

This R code has been run:

 require(biomaRt)
 require(gage)
 require(pathview)
 require(lattice)
 geneSet1 <- FALSE
 geneSet2 <-  TRUE
 cachedbname = 'Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt_FALSE_TRUE_gage_db'
 db <- InitDb(db.name=cachedbname, db.path='cache')
 fileName <- LoadCachedObject(db, 'filename_key')
 Project <- LoadCachedObject(db, 'project_key')
 geneSet1 <- LoadCachedObject(db, 'geneset1_key')
 geneSet2 <- LoadCachedObject(db, 'geneset2_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 dme <- LoadCachedObject(db, 'dme_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 conversion2 <- LoadCachedObject(db, 'conversion2_key')
 conversion3 <- LoadCachedObject(db, 'conversion3_key')
 n <- LoadCachedObject(db, 'n_key')
 res = NULL 
 print('Expression data selected: ')
 ## [1] "Expression data selected: "
 print('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt')
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt"
 file.name = read.table('/RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq.txt', row.names=1, header=TRUE)
 print('dimensions:')
 ## [1] "dimensions:"
 head(file.name)
 ##                     E2_mean UNTR_mean         M         D      prob
 ## ENSMUSG00000024236 3491.317 2408.5939 0.5355801 1082.7228 0.9695231
 ## ENSMUSG00000006740 5179.638 3729.4665 0.4738820 1450.1715 0.9626677
 ## ENSMUSG00000024290 3519.108 2433.1265 0.5323986 1085.9818 0.9687034
 ## ENSMUSG00000024294 5623.267 3752.3939 0.5835973 1870.8733 0.9753353
 ## ENSMUSG00000033382 3250.364 2356.2755 0.4640931  894.0885 0.9585320
 ## ENSMUSG00000041915 1016.327  589.2449 0.7864249  427.0818 0.9752981
 ##                      ranking
 ## ENSMUSG00000024236 1082.7229
 ## ENSMUSG00000006740 1450.1715
 ## ENSMUSG00000024290 1085.9820
 ## ENSMUSG00000024294 1870.8734
 ## ENSMUSG00000033382  894.0886
 ## ENSMUSG00000041915  427.0825
 kegg.gs = NULL
 go.gs = NULL
 if (geneSet1==TRUE){ 
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 kegg.gs=ks$kg.sets}
 if (geneSet2==TRUE){ data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets}
 ## Gene ID type for 'mouse' is: 'EG'
 if(conversion==TRUE){ #ensembl gene ids to entrez genes
 if(hsa==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='hsapiens_gene_ensembl')} 
 if(mmu==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='mmusculus_gene_ensembl')} 
 if(dme==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='dmelanogaster_gene_ensembl')} 
 if(dre==TRUE){ensembl=useMart('ENSEMBL_MART_ENSEMBL', host='www.ensembl.org', dataset='drerio_gene_ensembl')}} 

 file.name <- LoadCachedObject(db, 'filenameconverted_key')
 print('Expression data modified:')
 ## [1] "Expression data modified:"
 print(dim(file.name))
 ## [1] 805   6
 print(head(file.name))
 ##         E2_mean UNTR_mean         M         D      prob   ranking
 ## 11305  2251.808  1587.416 0.5044036  664.3920 0.9614754  664.3922
 ## 11352  1756.638  1154.060 0.6060989  602.5782 0.9710134  602.5785
 ## 11426 13403.227  7186.994 0.8991199 6216.2327 0.9929955 6216.2327
 ## 11433  8977.105  5383.496 0.7377070 3593.6095 0.9877049 3593.6096
 ## 11479  1613.703  1149.163 0.4897913  464.5398 0.9542474  464.5400
 ## 11492  5231.593  3654.661 0.5175125 1576.9314 0.9691505 1576.9315
 out.suffix=NULL
 fc=NULL
 exp.fc <- LoadCachedObject(db, 'expfc_key')
 out.suffix <- LoadCachedObject(db, 'outsuffix_key')
 #if (colnames(file.name)[1] == 'log2FoldChange') {fc=file.name[,'log2FoldChange']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='deseq' }
 #if(colnames(file.name)[1] == 'logFC'){ fc=file.name[,'logFC']
 #names(fc)=rownames(file.name)
 #out.suffix='edger'}
 #if(colnames(file.name)[3] == 'M'){ fc=file.name[,'M']
 #names(fc)=rownames(file.name)
 #sum(is.infinite(fc)) #to manage the problem of inf
 #fc[fc>10]=10
 #fc[fc< -10]=-10
 #out.suffix='noiseq'}
 #exp.fc=fc
 #if (geneSet1==TRUE){res <- gage(exp.fc, gsets = kegg.gs, ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)
 #}else{res <- gage(exp.fc, gsets = go.gs  , ref = NULL, samp = NULL, same.dir = FALSE, saaTest = gs.zTest, use.fold = FALSE)}
 res <- LoadCachedObject(db, 'res_key')
 print('First lines of results:')
 ## [1] "First lines of results:"
 head(res[[1]],10)
 ##                                                      p.geomean stat.mean
 ## GO:0043025 neuronal cell body                     1.485400e-05  4.175693
 ## GO:1901214 regulation of neuron death             2.622810e-05  4.044405
 ## GO:0070997 neuron death                           6.217046e-05  3.837406
 ## GO:0044297 cell body                              1.211058e-04  3.670357
 ## GO:0043523 regulation of neuron apoptotic process 2.489943e-04  3.481836
 ## GO:0043235 receptor complex                       4.586850e-04  3.314714
 ## GO:0060326 cell chemotaxis                        5.378943e-04  3.269918
 ## GO:0051402 neuron apoptotic process               6.272107e-04  3.226208
 ## GO:0030246 carbohydrate binding                   1.156817e-03  3.046707
 ## GO:0048545 response to steroid hormone            1.177995e-03  3.041249
 ##                                                          p.val      q.val
 ## GO:0043025 neuronal cell body                     1.485400e-05 0.01864818
 ## GO:1901214 regulation of neuron death             2.622810e-05 0.01864818
 ## GO:0070997 neuron death                           6.217046e-05 0.02946880
 ## GO:0044297 cell body                              1.211058e-04 0.04305310
 ## GO:0043523 regulation of neuron apoptotic process 2.489943e-04 0.07081398
 ## GO:0043235 receptor complex                       4.586850e-04 0.10870835
 ## GO:0060326 cell chemotaxis                        5.378943e-04 0.10926938
 ## GO:0051402 neuron apoptotic process               6.272107e-04 0.11148670
 ## GO:0030246 carbohydrate binding                   1.156817e-03 0.16751084
 ## GO:0048545 response to steroid hormone            1.177995e-03 0.16751084
 ##                                                   set.size         exp1
 ## GO:0043025 neuronal cell body                           26 1.485400e-05
 ## GO:1901214 regulation of neuron death                   19 2.622810e-05
 ## GO:0070997 neuron death                                 21 6.217046e-05
 ## GO:0044297 cell body                                    29 1.211058e-04
 ## GO:0043523 regulation of neuron apoptotic process       16 2.489943e-04
 ## GO:0043235 receptor complex                             21 4.586850e-04
 ## GO:0060326 cell chemotaxis                              11 5.378943e-04
 ## GO:0051402 neuron apoptotic process                     17 6.272107e-04
 ## GO:0030246 carbohydrate binding                         13 1.156817e-03
 ## GO:0048545 response to steroid hormone                  15 1.177995e-03
 #sel <- res$greater[, 'q.val'] < 0.01 & !is.na(res$greater[, 'q.val'])
 #path.ids <- rownames(res$greater)[sel]
 #sel.l <- res$less[, 'q.val'] < 0.01 & !is.na(res$less[,'q.val'])
 #path.ids.l <- rownames(res$less)[sel.l]
 #path.ids2 <- substr(c(path.ids, path.ids.l), 1, 8) #put greater and less together
 path.ids2 <- LoadCachedObject(db, 'finalresults_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 head(path.ids2)
 ## [1] "GO:00430" "GO:19012" "GO:00709" "GO:00442"
 #pv.out.list <- sapply(path.ids2, function(pid){ pathview( gene.data = exp.fc, pathway.id = pid, species = specie, out.suffix=out.suffix)})

DAVID

To work with DAVID, it is necessary just a gene list. The following gene lists have been automatically produced during the Result Comparison step. In order to help the lecturer, the same order described in the paper has been mainteined. The followint reported Pathway analysis has been performed on KEGG database, while the selected Gene Ontology categories are GOTERM_BP_ALL and GOTERM_MF_ALL.

Pathway Analysis on DEC up-regulated intersected with E2 up-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_UP_E2_UP_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list_1 , db.selected: KEGG_PATHWAY , analysis.type: Pathway , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_DEC_UP_E2_UP_genes_in_intersection.txt_DAVID_Pathway_KEGG_PATHWAY_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##  [1] Category        Term            Count           X.             
 ##  [5] PValue          Genes           List.Total      Pop.Hits       
 ##  [9] Pop.Total       Fold.Enrichment Bonferroni      Benjamini      
 ## [13] FDR            
 ## <0 rows> (or 0-length row.names)

Gene Ontology Analysis on DEC up-regulated intersected with E2 up-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_UP_E2_UP_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list_1 , db.selected: GOTERM_MF_ALL , analysis.type: GO , specie:

, filter: FALSE , filter.column: No , filter.threshold: Inf , Project: BMDC_analysis You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_UP_E2_UP_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list_1 , db.selected: GOTERM_BP_ALL , analysis.type: GO , specie:

, filter: FALSE , filter.column: No , filter.threshold: Inf , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_DEC_UP_E2_UP_genes_in_intersection.txt_DAVID_ALL_GO_GOTERM_MF_ALL_GOTERM_BP_ALL_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##        Category                                                Term Count
 ## 1 GOTERM_BP_ALL                         GO:0009987~cellular process   234
 ## 2 GOTERM_MF_ALL                                  GO:0005488~binding   285
 ## 3 GOTERM_BP_ALL GO:0044260~cellular macromolecule metabolic process   147
 ## 4 GOTERM_BP_ALL                  GO:0051276~chromosome organization    33
 ## 5 GOTERM_BP_ALL                  GO:0043412~biopolymer modification    63
 ## 6 GOTERM_BP_ALL             GO:0006464~protein modification process    61
 ##          X.       PValue
 ## 1 60.621762 6.379867e-14
 ## 2 73.834197 1.095963e-13
 ## 3 38.082902 1.566126e-13
 ## 4  8.549223 1.076141e-11
 ## 5 16.321244 2.258979e-11
 ## 6 15.803109 2.958286e-11
 ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Genes
 ## 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ENSMUSG00000022126, ENSMUSG00000026395, ENSMUSG00000032253, ENSMUSG00000018501, ENSMUSG00000018707, ENSMUSG00000024639, ENSMUSG00000052707, ENSMUSG00000048047, ENSMUSG00000022263, ENSMUSG00000022641, ENSMUSG00000036698, ENSMUSG00000026648, ENSMUSG00000022772, ENSMUSG00000033624, ENSMUSG00000019769, ENSMUSG00000050310, ENSMUSG00000006262, ENSMUSG00000031659, ENSMUSG00000021279, ENSMUSG00000038914, ENSMUSG00000027109, ENSMUSG00000027878, ENSMUSG00000048234, ENSMUSG00000007655, ENSMUSG00000024759, ENSMUSG00000024242, ENSMUSG00000024383, ENSMUSG00000047036, ENSMUSG00000032525, ENSMUSG00000054640, ENSMUSG00000062627, ENSMUSG00000038679, ENSMUSG00000030660, ENSMUSG00000040054, ENSMUSG00000074305, ENSMUSG00000022521, ENSMUSG00000034160, ENSMUSG00000004609, ENSMUSG00000035021, ENSMUSG00000000532, ENSMUSG00000027130, ENSMUSG00000037926, ENSMUSG00000064127, ENSMUSG00000079487, ENSMUSG00000034462, ENSMUSG00000024236, ENSMUSG00000030655, ENSMUSG00000030657, ENSMUSG00000037400, ENSMUSG00000041852, ENSMUSG00000036155, ENSMUSG00000040455, ENSMUSG00000039087, ENSMUSG00000060131, ENSMUSG00000063077, ENSMUSG00000035248, ENSMUSG00000035247, ENSMUSG00000002413, ENSMUSG00000040481, ENSMUSG00000031010, ENSMUSG00000030786, ENSMUSG00000032217, ENSMUSG00000009292, ENSMUSG00000050619, ENSMUSG00000031540, ENSMUSG00000057110, ENSMUSG00000026923, ENSMUSG00000007817, ENSMUSG00000024294, ENSMUSG00000034480, ENSMUSG00000054387, ENSMUSG00000019726, ENSMUSG00000038664, ENSMUSG00000020640, ENSMUSG00000024290, ENSMUSG00000049470, ENSMUSG00000027009, ENSMUSG00000038766, ENSMUSG00000024298, ENSMUSG00000056342, ENSMUSG00000041235, ENSMUSG00000028028, ENSMUSG00000022533, ENSMUSG00000060012, ENSMUSG00000021910, ENSMUSG00000022141, ENSMUSG00000025261, ENSMUSG00000027678, ENSMUSG00000040943, ENSMUSG00000031529, ENSMUSG00000040152, ENSMUSG00000058881, ENSMUSG00000066036, ENSMUSG00000025531, ENSMUSG00000056608, ENSMUSG00000044042, ENSMUSG00000033792, ENSMUSG00000057133, ENSMUSG00000057230, ENSMUSG00000025326, ENSMUSG00000018076, ENSMUSG00000026131, ENSMUSG00000068284, ENSMUSG00000034297, ENSMUSG00000019841, ENSMUSG00000024073, ENSMUSG00000030287, ENSMUSG00000030557, ENSMUSG00000030180, ENSMUSG00000031229, ENSMUSG00000020275, ENSMUSG00000031731, ENSMUSG00000055204, ENSMUSG00000027263, ENSMUSG00000062949, ENSMUSG00000028385, ENSMUSG00000025969, ENSMUSG00000031441, ENSMUSG00000040761, ENSMUSG00000028053, ENSMUSG00000025060, ENSMUSG00000038384, ENSMUSG00000030451, ENSMUSG00000028842, ENSMUSG00000022708, ENSMUSG00000024054, ENSMUSG00000028649, ENSMUSG00000029238, ENSMUSG00000019820, ENSMUSG00000021451, ENSMUSG00000063108, ENSMUSG00000003847, ENSMUSG00000001998, ENSMUSG00000030059, ENSMUSG00000027398, ENSMUSG00000021738, ENSMUSG00000029505, ENSMUSG00000037876, ENSMUSG00000027598, ENSMUSG00000034377, ENSMUSG00000050931, ENSMUSG00000066232, ENSMUSG00000002028, ENSMUSG00000029313, ENSMUSG00000038369, ENSMUSG00000025044, ENSMUSG00000025949, ENSMUSG00000025571, ENSMUSG00000022822, ENSMUSG00000038886, ENSMUSG00000028760, ENSMUSG00000029004, ENSMUSG00000025429, ENSMUSG00000005871, ENSMUSG00000019907, ENSMUSG00000033004, ENSMUSG00000035493, ENSMUSG00000039219, ENSMUSG00000039218, ENSMUSG00000023852, ENSMUSG00000047793, ENSMUSG00000033943, ENSMUSG00000053007, ENSMUSG00000022812, ENSMUSG00000002489, ENSMUSG00000032410, ENSMUSG00000038872, ENSMUSG00000028556, ENSMUSG00000049076, ENSMUSG00000038056, ENSMUSG00000029202, ENSMUSG00000029104, ENSMUSG00000042390, ENSMUSG00000037487, ENSMUSG00000037486, ENSMUSG00000003226, ENSMUSG00000030213, ENSMUSG00000020170, ENSMUSG00000036273, ENSMUSG00000021413, ENSMUSG00000037234, ENSMUSG00000008730, ENSMUSG00000029471, ENSMUSG00000042284, ENSMUSG00000074519, ENSMUSG00000027340, ENSMUSG00000027829, ENSMUSG00000044791, ENSMUSG00000054008, ENSMUSG00000071796, ENSMUSG00000017291, ENSMUSG00000005893, ENSMUSG00000034342, ENSMUSG00000067336, ENSMUSG00000007880, ENSMUSG00000070889, ENSMUSG00000052414, ENSMUSG00000026872, ENSMUSG00000078942, ENSMUSG00000054823, ENSMUSG00000042333, ENSMUSG00000045482, ENSMUSG00000057672, ENSMUSG00000037112, ENSMUSG00000021709, ENSMUSG00000048118, ENSMUSG00000022974, ENSMUSG00000027365, ENSMUSG00000055024, ENSMUSG00000019943, ENSMUSG00000027363, ENSMUSG00000024542, ENSMUSG00000019947, ENSMUSG00000037111, ENSMUSG00000061436, ENSMUSG00000000058, ENSMUSG00000022973, ENSMUSG00000026596, ENSMUSG00000032855, ENSMUSG00000049658, ENSMUSG00000046876, ENSMUSG00000020315, ENSMUSG00000031295, ENSMUSG00000045962, ENSMUSG00000034218, ENSMUSG00000020715, ENSMUSG00000034636, ENSMUSG00000056536, ENSMUSG00000006740, ENSMUSG00000043019, ENSMUSG00000042744, ENSMUSG00000003119, ENSMUSG00000033705, ENSMUSG00000047888
 ## 2 ENSMUSG00000033777, ENSMUSG00000043940, ENSMUSG00000026395, ENSMUSG00000032253, ENSMUSG00000018501, ENSMUSG00000035954, ENSMUSG00000018707, ENSMUSG00000035181, ENSMUSG00000024639, ENSMUSG00000052707, ENSMUSG00000042599, ENSMUSG00000048047, ENSMUSG00000022263, ENSMUSG00000053580, ENSMUSG00000022641, ENSMUSG00000027312, ENSMUSG00000036698, ENSMUSG00000026648, ENSMUSG00000069892, ENSMUSG00000022961, ENSMUSG00000069895, ENSMUSG00000019769, ENSMUSG00000050310, ENSMUSG00000006262, ENSMUSG00000031659, ENSMUSG00000021279, ENSMUSG00000038914, ENSMUSG00000027109, ENSMUSG00000027878, ENSMUSG00000020300, ENSMUSG00000048234, ENSMUSG00000007655, ENSMUSG00000041977, ENSMUSG00000027508, ENSMUSG00000020709, ENSMUSG00000074582, ENSMUSG00000024759, ENSMUSG00000024242, ENSMUSG00000041702, ENSMUSG00000024383, ENSMUSG00000047036, ENSMUSG00000054640, ENSMUSG00000032525, ENSMUSG00000062627, ENSMUSG00000038679, ENSMUSG00000030660, ENSMUSG00000060657, ENSMUSG00000040054, ENSMUSG00000074305, ENSMUSG00000022521, ENSMUSG00000034160, ENSMUSG00000035021, ENSMUSG00000004609, ENSMUSG00000000532, ENSMUSG00000053877, ENSMUSG00000027130, ENSMUSG00000033499, ENSMUSG00000037926, ENSMUSG00000064127, ENSMUSG00000079487, ENSMUSG00000051344, ENSMUSG00000034462, ENSMUSG00000024236, ENSMUSG00000037697, ENSMUSG00000030655, ENSMUSG00000037400, ENSMUSG00000041852, ENSMUSG00000048154, ENSMUSG00000037965, ENSMUSG00000040455, ENSMUSG00000039087, ENSMUSG00000060131, ENSMUSG00000063077, ENSMUSG00000020021, ENSMUSG00000035248, ENSMUSG00000025241, ENSMUSG00000035247, ENSMUSG00000002413, ENSMUSG00000040481, ENSMUSG00000031010, ENSMUSG00000030786, ENSMUSG00000032217, ENSMUSG00000009292, ENSMUSG00000050619, ENSMUSG00000031540, ENSMUSG00000033237, ENSMUSG00000057110, ENSMUSG00000026923, ENSMUSG00000007817, ENSMUSG00000024294, ENSMUSG00000034480, ENSMUSG00000054387, ENSMUSG00000073902, ENSMUSG00000024290, ENSMUSG00000020640, ENSMUSG00000021374, ENSMUSG00000027009, ENSMUSG00000020948, ENSMUSG00000039477, ENSMUSG00000024298, ENSMUSG00000038766, ENSMUSG00000041235, ENSMUSG00000028028, ENSMUSG00000022533, ENSMUSG00000060012, ENSMUSG00000021910, ENSMUSG00000022141, ENSMUSG00000025261, ENSMUSG00000027678, ENSMUSG00000040943, ENSMUSG00000027677, ENSMUSG00000040565, ENSMUSG00000040152, ENSMUSG00000058881, ENSMUSG00000066036, ENSMUSG00000025531, ENSMUSG00000003316, ENSMUSG00000056608, ENSMUSG00000044042, ENSMUSG00000033792, ENSMUSG00000057133, ENSMUSG00000057230, ENSMUSG00000025326, ENSMUSG00000024483, ENSMUSG00000026131, ENSMUSG00000022565, ENSMUSG00000068284, ENSMUSG00000034297, ENSMUSG00000019841, ENSMUSG00000024073, ENSMUSG00000039834, ENSMUSG00000035133, ENSMUSG00000030557, ENSMUSG00000030287, ENSMUSG00000070733, ENSMUSG00000031229, ENSMUSG00000030180, ENSMUSG00000020275, ENSMUSG00000031731, ENSMUSG00000055204, ENSMUSG00000022139, ENSMUSG00000062949, ENSMUSG00000028385, ENSMUSG00000025969, ENSMUSG00000031441, ENSMUSG00000040761, ENSMUSG00000028053, ENSMUSG00000025060, ENSMUSG00000066440, ENSMUSG00000041268, ENSMUSG00000038384, ENSMUSG00000030451, ENSMUSG00000028842, ENSMUSG00000022708, ENSMUSG00000024054, ENSMUSG00000028649, ENSMUSG00000029238, ENSMUSG00000019820, ENSMUSG00000069833, ENSMUSG00000021451, ENSMUSG00000027580, ENSMUSG00000023830, ENSMUSG00000030084, ENSMUSG00000063108, ENSMUSG00000003847, ENSMUSG00000001998, ENSMUSG00000055670, ENSMUSG00000030059, ENSMUSG00000021733, ENSMUSG00000027398, ENSMUSG00000021738, ENSMUSG00000021068, ENSMUSG00000029505, ENSMUSG00000037876, ENSMUSG00000027598, ENSMUSG00000066232, ENSMUSG00000032846, ENSMUSG00000002028, ENSMUSG00000029313, ENSMUSG00000038369, ENSMUSG00000025044, ENSMUSG00000025949, ENSMUSG00000025571, ENSMUSG00000050919, ENSMUSG00000022822, ENSMUSG00000008200, ENSMUSG00000038886, ENSMUSG00000028760, ENSMUSG00000029004, ENSMUSG00000025429, ENSMUSG00000005871, ENSMUSG00000036499, ENSMUSG00000033004, ENSMUSG00000039219, ENSMUSG00000035493, ENSMUSG00000023852, ENSMUSG00000047793, ENSMUSG00000023845, ENSMUSG00000038608, ENSMUSG00000041258, ENSMUSG00000033943, ENSMUSG00000053007, ENSMUSG00000052085, ENSMUSG00000022812, ENSMUSG00000038872, ENSMUSG00000002489, ENSMUSG00000032410, ENSMUSG00000028556, ENSMUSG00000040522, ENSMUSG00000049076, ENSMUSG00000038056, ENSMUSG00000029202, ENSMUSG00000029104, ENSMUSG00000042390, ENSMUSG00000037487, ENSMUSG00000040524, ENSMUSG00000037486, ENSMUSG00000003226, ENSMUSG00000020841, ENSMUSG00000030213, ENSMUSG00000020170, ENSMUSG00000036273, ENSMUSG00000021413, ENSMUSG00000026987, ENSMUSG00000037234, ENSMUSG00000008730, ENSMUSG00000029471, ENSMUSG00000042284, ENSMUSG00000074519, ENSMUSG00000027340, ENSMUSG00000048271, ENSMUSG00000044791, ENSMUSG00000000386, ENSMUSG00000071796, ENSMUSG00000039782, ENSMUSG00000017291, ENSMUSG00000005893, ENSMUSG00000034342, ENSMUSG00000014547, ENSMUSG00000070390, ENSMUSG00000067336, ENSMUSG00000007880, ENSMUSG00000003233, ENSMUSG00000040407, ENSMUSG00000052414, ENSMUSG00000026872, ENSMUSG00000078942, ENSMUSG00000054823, ENSMUSG00000042333, ENSMUSG00000057672, ENSMUSG00000045482, ENSMUSG00000072501, ENSMUSG00000037112, ENSMUSG00000021709, ENSMUSG00000048118, ENSMUSG00000022974, ENSMUSG00000019943, ENSMUSG00000027365, ENSMUSG00000055024, ENSMUSG00000032560, ENSMUSG00000027363, ENSMUSG00000019947, ENSMUSG00000039967, ENSMUSG00000061436, ENSMUSG00000000058, ENSMUSG00000022973, ENSMUSG00000026596, ENSMUSG00000032855, ENSMUSG00000049658, ENSMUSG00000046876, ENSMUSG00000020315, ENSMUSG00000031295, ENSMUSG00000045962, ENSMUSG00000034218, ENSMUSG00000073664, ENSMUSG00000020715, ENSMUSG00000034636, ENSMUSG00000006740, ENSMUSG00000061755, ENSMUSG00000043019, ENSMUSG00000003119, ENSMUSG00000033705, ENSMUSG00000037857, ENSMUSG00000037652, ENSMUSG00000047888
 ## 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         ENSMUSG00000040761, ENSMUSG00000028053, ENSMUSG00000025060, ENSMUSG00000038384, ENSMUSG00000026395, ENSMUSG00000018501, ENSMUSG00000030451, ENSMUSG00000028842, ENSMUSG00000022708, ENSMUSG00000024639, ENSMUSG00000029238, ENSMUSG00000048047, ENSMUSG00000022263, ENSMUSG00000022641, ENSMUSG00000063108, ENSMUSG00000003847, ENSMUSG00000030059, ENSMUSG00000036698, ENSMUSG00000021738, ENSMUSG00000026648, ENSMUSG00000029505, ENSMUSG00000022772, ENSMUSG00000037876, ENSMUSG00000021279, ENSMUSG00000006262, ENSMUSG00000027598, ENSMUSG00000034377, ENSMUSG00000038914, ENSMUSG00000027109, ENSMUSG00000027878, ENSMUSG00000048234, ENSMUSG00000002028, ENSMUSG00000024242, ENSMUSG00000029313, ENSMUSG00000038369, ENSMUSG00000024383, ENSMUSG00000047036, ENSMUSG00000025949, ENSMUSG00000032525, ENSMUSG00000062627, ENSMUSG00000038679, ENSMUSG00000028760, ENSMUSG00000029004, ENSMUSG00000040054, ENSMUSG00000074305, ENSMUSG00000022521, ENSMUSG00000034160, ENSMUSG00000019907, ENSMUSG00000035021, ENSMUSG00000000532, ENSMUSG00000033004, ENSMUSG00000039219, ENSMUSG00000039218, ENSMUSG00000037926, ENSMUSG00000064127, ENSMUSG00000079487, ENSMUSG00000030655, ENSMUSG00000033943, ENSMUSG00000022812, ENSMUSG00000053007, ENSMUSG00000032410, ENSMUSG00000038872, ENSMUSG00000041852, ENSMUSG00000036155, ENSMUSG00000040455, ENSMUSG00000038056, ENSMUSG00000039087, ENSMUSG00000042390, ENSMUSG00000037487, ENSMUSG00000037486, ENSMUSG00000003226, ENSMUSG00000030213, ENSMUSG00000020170, ENSMUSG00000036273, ENSMUSG00000035248, ENSMUSG00000021413, ENSMUSG00000035247, ENSMUSG00000008730, ENSMUSG00000029471, ENSMUSG00000002413, ENSMUSG00000074519, ENSMUSG00000031010, ENSMUSG00000030786, ENSMUSG00000032217, ENSMUSG00000050619, ENSMUSG00000031540, ENSMUSG00000027829, ENSMUSG00000044791, ENSMUSG00000054008, ENSMUSG00000017291, ENSMUSG00000005893, ENSMUSG00000034342, ENSMUSG00000026923, ENSMUSG00000007817, ENSMUSG00000067336, ENSMUSG00000024294, ENSMUSG00000038664, ENSMUSG00000024290, ENSMUSG00000049470, ENSMUSG00000052414, ENSMUSG00000026872, ENSMUSG00000038766, ENSMUSG00000024298, ENSMUSG00000056342, ENSMUSG00000041235, ENSMUSG00000054823, ENSMUSG00000028028, ENSMUSG00000045482, ENSMUSG00000057672, ENSMUSG00000037112, ENSMUSG00000025261, ENSMUSG00000027678, ENSMUSG00000031529, ENSMUSG00000022974, ENSMUSG00000055024, ENSMUSG00000027365, ENSMUSG00000058881, ENSMUSG00000027363, ENSMUSG00000066036, ENSMUSG00000019947, ENSMUSG00000037111, ENSMUSG00000061436, ENSMUSG00000056608, ENSMUSG00000026596, ENSMUSG00000049658, ENSMUSG00000033792, ENSMUSG00000020315, ENSMUSG00000057230, ENSMUSG00000025326, ENSMUSG00000018076, ENSMUSG00000034297, ENSMUSG00000019841, ENSMUSG00000024073, ENSMUSG00000031295, ENSMUSG00000045962, ENSMUSG00000030557, ENSMUSG00000034218, ENSMUSG00000020715, ENSMUSG00000034636, ENSMUSG00000056536, ENSMUSG00000031229, ENSMUSG00000030180, ENSMUSG00000020275, ENSMUSG00000043019, ENSMUSG00000042744, ENSMUSG00000003119, ENSMUSG00000055204
 ## 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSMUSG00000002028, ENSMUSG00000022141, ENSMUSG00000025261, ENSMUSG00000028053, ENSMUSG00000048118, ENSMUSG00000038384, ENSMUSG00000055024, ENSMUSG00000062627, ENSMUSG00000018501, ENSMUSG00000037111, ENSMUSG00000040481, ENSMUSG00000024054, ENSMUSG00000029004, ENSMUSG00000031540, ENSMUSG00000044791, ENSMUSG00000056608, ENSMUSG00000040054, ENSMUSG00000022521, ENSMUSG00000005871, ENSMUSG00000035021, ENSMUSG00000057133, ENSMUSG00000039219, ENSMUSG00000023852, ENSMUSG00000007880, ENSMUSG00000026648, ENSMUSG00000029505, ENSMUSG00000037876, ENSMUSG00000030180, ENSMUSG00000032410, ENSMUSG00000041235, ENSMUSG00000054823, ENSMUSG00000038056, ENSMUSG00000045482
 ## 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         ENSMUSG00000030213, ENSMUSG00000036273, ENSMUSG00000020170, ENSMUSG00000021413, ENSMUSG00000025060, ENSMUSG00000035247, ENSMUSG00000029471, ENSMUSG00000008730, ENSMUSG00000026395, ENSMUSG00000030451, ENSMUSG00000002413, ENSMUSG00000024639, ENSMUSG00000031010, ENSMUSG00000031540, ENSMUSG00000044791, ENSMUSG00000054008, ENSMUSG00000017291, ENSMUSG00000022263, ENSMUSG00000024294, ENSMUSG00000067336, ENSMUSG00000038664, ENSMUSG00000024290, ENSMUSG00000029505, ENSMUSG00000006262, ENSMUSG00000021279, ENSMUSG00000027598, ENSMUSG00000028028, ENSMUSG00000057672, ENSMUSG00000045482, ENSMUSG00000002028, ENSMUSG00000037112, ENSMUSG00000024242, ENSMUSG00000025261, ENSMUSG00000024383, ENSMUSG00000031529, ENSMUSG00000027365, ENSMUSG00000055024, ENSMUSG00000062627, ENSMUSG00000061436, ENSMUSG00000029004, ENSMUSG00000040054, ENSMUSG00000022521, ENSMUSG00000026596, ENSMUSG00000074305, ENSMUSG00000034160, ENSMUSG00000033792, ENSMUSG00000019907, ENSMUSG00000020315, ENSMUSG00000057230, ENSMUSG00000000532, ENSMUSG00000025326, ENSMUSG00000037926, ENSMUSG00000024073, ENSMUSG00000045962, ENSMUSG00000034218, ENSMUSG00000020715, ENSMUSG00000022812, ENSMUSG00000056536, ENSMUSG00000042744, ENSMUSG00000036155, ENSMUSG00000003119, ENSMUSG00000038056, ENSMUSG00000037487
 ## 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSMUSG00000036273, ENSMUSG00000020170, ENSMUSG00000021413, ENSMUSG00000025060, ENSMUSG00000035247, ENSMUSG00000029471, ENSMUSG00000008730, ENSMUSG00000026395, ENSMUSG00000030451, ENSMUSG00000002413, ENSMUSG00000031010, ENSMUSG00000024639, ENSMUSG00000031540, ENSMUSG00000044791, ENSMUSG00000054008, ENSMUSG00000017291, ENSMUSG00000022263, ENSMUSG00000024294, ENSMUSG00000067336, ENSMUSG00000038664, ENSMUSG00000024290, ENSMUSG00000029505, ENSMUSG00000006262, ENSMUSG00000021279, ENSMUSG00000027598, ENSMUSG00000028028, ENSMUSG00000057672, ENSMUSG00000045482, ENSMUSG00000037112, ENSMUSG00000024242, ENSMUSG00000025261, ENSMUSG00000024383, ENSMUSG00000031529, ENSMUSG00000027365, ENSMUSG00000055024, ENSMUSG00000062627, ENSMUSG00000061436, ENSMUSG00000029004, ENSMUSG00000040054, ENSMUSG00000022521, ENSMUSG00000026596, ENSMUSG00000074305, ENSMUSG00000034160, ENSMUSG00000033792, ENSMUSG00000019907, ENSMUSG00000020315, ENSMUSG00000057230, ENSMUSG00000000532, ENSMUSG00000025326, ENSMUSG00000037926, ENSMUSG00000024073, ENSMUSG00000045962, ENSMUSG00000034218, ENSMUSG00000020715, ENSMUSG00000022812, ENSMUSG00000056536, ENSMUSG00000042744, ENSMUSG00000036155, ENSMUSG00000003119, ENSMUSG00000038056, ENSMUSG00000037487
 ##   List.Total Pop.Hits Pop.Total Fold.Enrichment   Bonferroni    Benjamini
 ## 1        275     9252     14219        1.307726 1.161848e-10 1.161848e-10
 ## 2        314    11445     15404        1.221611 4.646150e-11 4.646150e-11
 ## 3        275     4549     14219        1.670851 2.851075e-10 1.425537e-10
 ## 4        275      404     14219        4.223465 1.958573e-08 6.528578e-09
 ## 5        275     1320     14219        2.467760 4.111347e-08 1.027837e-08
 ## 6        275     1260     14219        2.503201 5.384086e-08 1.076817e-08
 ##            FDR
 ## 1 1.085465e-10
 ## 2 1.541323e-10
 ## 3 2.663647e-10
 ## 4 1.829850e-08
 ## 5 3.841136e-08
 ## 6 5.030227e-08

Pathway Analysis on DEC down-regulated intersected with E2 down-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_DOWN_E2_DOWN_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list2 , db.selected: KEGG_PATHWAY , analysis.type: Pathway , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_DEC_DOWN_E2_DOWN_genes_in_intersection.txt_DAVID_Pathway_KEGG_PATHWAY_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##       Category                               Term Count        X.
 ## 1 KEGG_PATHWAY                  mmu03010:Ribosome    71 13.894325
 ## 2 KEGG_PATHWAY mmu00190:Oxidative phosphorylation    47  9.197652
 ## 3 KEGG_PATHWAY       mmu05012:Parkinson's disease    43  8.414873
 ## 4 KEGG_PATHWAY      mmu05016:Huntington's disease    45  8.806262
 ## 5 KEGG_PATHWAY       mmu05010:Alzheimer's disease    41  8.023483
 ## 6 KEGG_PATHWAY                mmu03050:Proteasome    10  1.956947
 ##         PValue
 ## 1 1.271001e-84
 ## 2 1.146452e-32
 ## 3 1.633168e-27
 ## 4 2.172841e-23
 ## 5 9.394649e-20
 ## 6 8.211185e-05
 ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Genes
 ## 1 ENSMUSG00000007892, ENSMUSG00000062647, ENSMUSG00000047215, ENSMUSG00000000740, ENSMUSG00000025794, ENSMUSG00000071415, ENSMUSG00000062997, ENSMUSG00000079641, ENSMUSG00000046330, ENSMUSG00000037563, ENSMUSG00000059291, ENSMUSG00000060636, ENSMUSG00000048758, ENSMUSG00000022601, ENSMUSG00000025290, ENSMUSG00000025508, ENSMUSG00000028936, ENSMUSG00000047676, ENSMUSG00000034892, ENSMUSG00000047675, ENSMUSG00000063316, ENSMUSG00000003429, ENSMUSG00000037805, ENSMUSG00000024608, ENSMUSG00000030744, ENSMUSG00000028234, ENSMUSG00000038274, ENSMUSG00000039221, ENSMUSG00000008683, ENSMUSG00000008682, ENSMUSG00000073702, ENSMUSG00000012848, ENSMUSG00000067288, ENSMUSG00000017404, ENSMUSG00000057841, ENSMUSG00000040952, ENSMUSG00000038900, ENSMUSG00000006333, ENSMUSG00000060036, ENSMUSG00000061983, ENSMUSG00000061787, ENSMUSG00000043716, ENSMUSG00000041841, ENSMUSG00000030432, ENSMUSG00000060938, ENSMUSG00000049751, ENSMUSG00000067274, ENSMUSG00000028081, ENSMUSG00000058600, ENSMUSG00000025362, ENSMUSG00000061477, ENSMUSG00000036781, ENSMUSG00000049517, ENSMUSG00000063457, ENSMUSG00000057322, ENSMUSG00000052146, ENSMUSG00000008668, ENSMUSG00000046364, ENSMUSG00000062006, ENSMUSG00000020460, ENSMUSG00000039001, ENSMUSG00000044533, ENSMUSG00000032518, ENSMUSG00000062328, ENSMUSG00000045128, ENSMUSG00000059070, ENSMUSG00000057863, ENSMUSG00000041453, ENSMUSG00000009927, ENSMUSG00000031320, ENSMUSG00000028495, ENSMUSG00000003970, ENSMUSG00000079435
 ## 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         ENSMUSG00000022450, ENSMUSG00000063882, ENSMUSG00000029632, ENSMUSG00000038690, ENSMUSG00000050856, ENSMUSG00000035885, ENSMUSG00000027673, ENSMUSG00000038717, ENSMUSG00000030647, ENSMUSG00000003072, ENSMUSG00000033938, ENSMUSG00000018770, ENSMUSG00000021606, ENSMUSG00000004285, ENSMUSG00000022820, ENSMUSG00000000563, ENSMUSG00000006057, ENSMUSG00000061518, ENSMUSG00000062683, ENSMUSG00000028648, ENSMUSG00000026032, ENSMUSG00000041697, ENSMUSG00000044894, ENSMUSG00000014313, ENSMUSG00000035674, ENSMUSG00000059534, ENSMUSG00000046516, ENSMUSG00000024038, ENSMUSG00000023089, ENSMUSG00000024099, ENSMUSG00000059734, ENSMUSG00000016427, ENSMUSG00000014294, ENSMUSG00000020163, ENSMUSG00000016252, ENSMUSG00000040048, ENSMUSG00000034566, ENSMUSG00000026895, ENSMUSG00000041881, ENSMUSG00000037152, ENSMUSG00000032330, ENSMUSG00000039105, ENSMUSG00000031818, ENSMUSG00000002379, ENSMUSG00000022354, ENSMUSG00000036751, ENSMUSG00000017778
 ## 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         ENSMUSG00000022450, ENSMUSG00000063882, ENSMUSG00000029632, ENSMUSG00000028964, ENSMUSG00000035885, ENSMUSG00000027673, ENSMUSG00000030647, ENSMUSG00000003072, ENSMUSG00000033938, ENSMUSG00000018770, ENSMUSG00000021606, ENSMUSG00000022820, ENSMUSG00000000563, ENSMUSG00000006057, ENSMUSG00000061518, ENSMUSG00000062683, ENSMUSG00000028648, ENSMUSG00000026032, ENSMUSG00000041697, ENSMUSG00000044894, ENSMUSG00000014313, ENSMUSG00000035674, ENSMUSG00000059534, ENSMUSG00000024038, ENSMUSG00000023089, ENSMUSG00000024099, ENSMUSG00000059734, ENSMUSG00000016427, ENSMUSG00000014294, ENSMUSG00000020163, ENSMUSG00000016252, ENSMUSG00000020460, ENSMUSG00000040048, ENSMUSG00000034566, ENSMUSG00000026895, ENSMUSG00000041881, ENSMUSG00000037152, ENSMUSG00000032330, ENSMUSG00000031818, ENSMUSG00000028756, ENSMUSG00000022354, ENSMUSG00000036751, ENSMUSG00000017778
 ## 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSMUSG00000022450, ENSMUSG00000063882, ENSMUSG00000029632, ENSMUSG00000035885, ENSMUSG00000038489, ENSMUSG00000027673, ENSMUSG00000030647, ENSMUSG00000003072, ENSMUSG00000033938, ENSMUSG00000018770, ENSMUSG00000021606, ENSMUSG00000022820, ENSMUSG00000000563, ENSMUSG00000006057, ENSMUSG00000061518, ENSMUSG00000062683, ENSMUSG00000028648, ENSMUSG00000026032, ENSMUSG00000041697, ENSMUSG00000044894, ENSMUSG00000014313, ENSMUSG00000035674, ENSMUSG00000033020, ENSMUSG00000059534, ENSMUSG00000024038, ENSMUSG00000023089, ENSMUSG00000024099, ENSMUSG00000059734, ENSMUSG00000016427, ENSMUSG00000014294, ENSMUSG00000020163, ENSMUSG00000016252, ENSMUSG00000040048, ENSMUSG00000034566, ENSMUSG00000026895, ENSMUSG00000041881, ENSMUSG00000037152, ENSMUSG00000071662, ENSMUSG00000032330, ENSMUSG00000022982, ENSMUSG00000031818, ENSMUSG00000022354, ENSMUSG00000036751, ENSMUSG00000008036, ENSMUSG00000017778
 ## 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSMUSG00000022450, ENSMUSG00000063882, ENSMUSG00000029632, ENSMUSG00000035885, ENSMUSG00000027673, ENSMUSG00000030647, ENSMUSG00000003072, ENSMUSG00000033938, ENSMUSG00000018770, ENSMUSG00000021606, ENSMUSG00000022820, ENSMUSG00000000563, ENSMUSG00000006057, ENSMUSG00000061518, ENSMUSG00000062683, ENSMUSG00000028648, ENSMUSG00000026032, ENSMUSG00000041697, ENSMUSG00000044894, ENSMUSG00000014313, ENSMUSG00000035674, ENSMUSG00000036835, ENSMUSG00000059534, ENSMUSG00000024038, ENSMUSG00000023089, ENSMUSG00000024099, ENSMUSG00000059734, ENSMUSG00000016427, ENSMUSG00000014294, ENSMUSG00000020163, ENSMUSG00000016252, ENSMUSG00000040048, ENSMUSG00000034566, ENSMUSG00000026895, ENSMUSG00000041881, ENSMUSG00000037152, ENSMUSG00000032330, ENSMUSG00000031818, ENSMUSG00000022354, ENSMUSG00000036751, ENSMUSG00000017778
 ## 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ENSMUSG00000021024, ENSMUSG00000042541, ENSMUSG00000026750, ENSMUSG00000069744, ENSMUSG00000014769, ENSMUSG00000018286, ENSMUSG00000025487, ENSMUSG00000079197, ENSMUSG00000029649, ENSMUSG00000005779
 ##   List.Total Pop.Hits Pop.Total Fold.Enrichment   Bonferroni    Benjamini
 ## 1        231       89      5738       19.816042 1.499781e-82 1.499781e-82
 ## 2        231      130      5738        8.980553 1.352814e-30 6.764068e-31
 ## 3        231      133      5738        8.030921 1.927139e-25 6.423795e-26
 ## 4        231      183      5738        6.108154 2.563952e-21 6.409881e-22
 ## 5        231      182      5738        5.595785 1.108569e-17 2.217137e-18
 ## 6        231       47      5738        5.285070 9.642803e-03 1.613629e-03
 ##            FDR
 ## 1 1.454582e-81
 ## 2 1.312044e-29
 ## 3 1.869060e-24
 ## 4 2.486683e-20
 ## 5 1.075160e-16
 ## 6 9.393166e-02

Gene Ontology Analysis on DEC down-regulated intersected with E2 down-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_DOWN_E2_DOWN_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list2 , db.selected: GOTERM_MF_ALL , analysis.type: GO , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_DOWN_E2_DOWN_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list2 , db.selected: GOTERM_BP_ALL , analysis.type: GO , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_DEC_DOWN_E2_DOWN_genes_in_intersection.txt_DAVID_ALL_GO_GOTERM_MF_ALL_GOTERM_BP_ALL_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##        Category                                          Term Count
 ## 1 GOTERM_MF_ALL GO:0003735~structural constituent of ribosome    83
 ## 2 GOTERM_BP_ALL                        GO:0006412~translation    94
 ## 3 GOTERM_MF_ALL       GO:0005198~structural molecule activity    88
 ## 4 GOTERM_BP_ALL                   GO:0009987~cellular process   338
 ## 5 GOTERM_BP_ALL         GO:0044237~cellular metabolic process   253
 ## 6 GOTERM_BP_ALL GO:0044267~cellular protein metabolic process   135
 ##         X.       PValue
 ## 1 16.24266 3.032929e-93
 ## 2 18.39530 6.832557e-73
 ## 3 17.22114 1.082752e-53
 ## 4 66.14481 5.479370e-31
 ## 5 49.51076 7.433889e-26
 ## 6 26.41879 2.389420e-25
 ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   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                                                                                                     ENSMUSG00000007892, ENSMUSG00000047215, ENSMUSG00000000740, ENSMUSG00000025794, ENSMUSG00000071415, ENSMUSG00000062997, ENSMUSG00000079641, ENSMUSG00000046330, ENSMUSG00000037563, ENSMUSG00000059291, ENSMUSG00000060636, ENSMUSG00000020477, ENSMUSG00000029066, ENSMUSG00000048758, ENSMUSG00000023939, ENSMUSG00000022601, ENSMUSG00000025290, ENSMUSG00000025508, ENSMUSG00000036850, ENSMUSG00000028936, ENSMUSG00000047676, ENSMUSG00000034892, ENSMUSG00000047675, ENSMUSG00000063316, ENSMUSG00000003429, ENSMUSG00000037805, ENSMUSG00000024608, ENSMUSG00000030744, ENSMUSG00000045948, ENSMUSG00000028234, ENSMUSG00000038274, ENSMUSG00000039221, ENSMUSG00000008683, ENSMUSG00000008682, ENSMUSG00000073702, ENSMUSG00000012848, ENSMUSG00000067288, ENSMUSG00000017404, ENSMUSG00000074129, ENSMUSG00000057841, ENSMUSG00000030879, ENSMUSG00000040952, ENSMUSG00000038900, ENSMUSG00000021607, ENSMUSG00000049960, ENSMUSG00000006333, ENSMUSG00000060036, ENSMUSG00000028861, ENSMUSG00000061983, ENSMUSG00000054312, ENSMUSG00000061787, ENSMUSG00000058267, ENSMUSG00000043716, ENSMUSG00000024902, ENSMUSG00000041841, ENSMUSG00000030432, ENSMUSG00000060938, ENSMUSG00000049751, ENSMUSG00000067274, ENSMUSG00000028081, ENSMUSG00000058600, ENSMUSG00000025362, ENSMUSG00000061477, ENSMUSG00000036781, ENSMUSG00000049517, ENSMUSG00000063457, ENSMUSG00000057322, ENSMUSG00000008668, ENSMUSG00000039640, ENSMUSG00000046364, ENSMUSG00000062006, ENSMUSG00000020460, ENSMUSG00000039001, ENSMUSG00000044533, ENSMUSG00000032518, ENSMUSG00000062328, ENSMUSG00000045128, ENSMUSG00000010406, ENSMUSG00000059070, ENSMUSG00000057863, ENSMUSG00000041453, ENSMUSG00000028495, ENSMUSG00000031320, ENSMUSG00000003970, ENSMUSG00000079435
 ## 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSMUSG00000007892, ENSMUSG00000025967, ENSMUSG00000047215, ENSMUSG00000006941, ENSMUSG00000000740, ENSMUSG00000025794, ENSMUSG00000071415, ENSMUSG00000055762, ENSMUSG00000062997, ENSMUSG00000079641, ENSMUSG00000046330, ENSMUSG00000037563, ENSMUSG00000059291, ENSMUSG00000060636, ENSMUSG00000027613, ENSMUSG00000020477, ENSMUSG00000029066, ENSMUSG00000048758, ENSMUSG00000023939, ENSMUSG00000035530, ENSMUSG00000022601, ENSMUSG00000035202, ENSMUSG00000025290, ENSMUSG00000030871, ENSMUSG00000025508, ENSMUSG00000036850, ENSMUSG00000028936, ENSMUSG00000047676, ENSMUSG00000034892, ENSMUSG00000047675, ENSMUSG00000063316, ENSMUSG00000003429, ENSMUSG00000037805, ENSMUSG00000024608, ENSMUSG00000030744, ENSMUSG00000045948, ENSMUSG00000028234, ENSMUSG00000038274, ENSMUSG00000039221, ENSMUSG00000008683, ENSMUSG00000008682, ENSMUSG00000073702, ENSMUSG00000012848, ENSMUSG00000067288, ENSMUSG00000017404, ENSMUSG00000074129, ENSMUSG00000057841, ENSMUSG00000030879, ENSMUSG00000040952, ENSMUSG00000038900, ENSMUSG00000021607, ENSMUSG00000049960, ENSMUSG00000006333, ENSMUSG00000060036, ENSMUSG00000028861, ENSMUSG00000061983, ENSMUSG00000054312, ENSMUSG00000016554, ENSMUSG00000061787, ENSMUSG00000058267, ENSMUSG00000043716, ENSMUSG00000024902, ENSMUSG00000041841, ENSMUSG00000030432, ENSMUSG00000060938, ENSMUSG00000067274, ENSMUSG00000049751, ENSMUSG00000028081, ENSMUSG00000058600, ENSMUSG00000053565, ENSMUSG00000025362, ENSMUSG00000061477, ENSMUSG00000036781, ENSMUSG00000049517, ENSMUSG00000063457, ENSMUSG00000057322, ENSMUSG00000008668, ENSMUSG00000078812, ENSMUSG00000039640, ENSMUSG00000062006, ENSMUSG00000046364, ENSMUSG00000020460, ENSMUSG00000039001, ENSMUSG00000044533, ENSMUSG00000032518, ENSMUSG00000031029, ENSMUSG00000062328, ENSMUSG00000045128, ENSMUSG00000010406, ENSMUSG00000059070, ENSMUSG00000057863, ENSMUSG00000041453, ENSMUSG00000028495, ENSMUSG00000031320, ENSMUSG00000003970, ENSMUSG00000079435
 ## 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         ENSMUSG00000007892, ENSMUSG00000047215, ENSMUSG00000000740, ENSMUSG00000025794, ENSMUSG00000071415, ENSMUSG00000062997, ENSMUSG00000079641, ENSMUSG00000046330, ENSMUSG00000037563, ENSMUSG00000059291, ENSMUSG00000060636, ENSMUSG00000020477, ENSMUSG00000029066, ENSMUSG00000048758, ENSMUSG00000023939, ENSMUSG00000022601, ENSMUSG00000043091, ENSMUSG00000025290, ENSMUSG00000025508, ENSMUSG00000036850, ENSMUSG00000028936, ENSMUSG00000047676, ENSMUSG00000034892, ENSMUSG00000047675, ENSMUSG00000001506, ENSMUSG00000063316, ENSMUSG00000003429, ENSMUSG00000037805, ENSMUSG00000024608, ENSMUSG00000030744, ENSMUSG00000045948, ENSMUSG00000023484, ENSMUSG00000028234, ENSMUSG00000038274, ENSMUSG00000039221, ENSMUSG00000008683, ENSMUSG00000008682, ENSMUSG00000073702, ENSMUSG00000012848, ENSMUSG00000067288, ENSMUSG00000017404, ENSMUSG00000074129, ENSMUSG00000057841, ENSMUSG00000030879, ENSMUSG00000040952, ENSMUSG00000038900, ENSMUSG00000021607, ENSMUSG00000049960, ENSMUSG00000006333, ENSMUSG00000060036, ENSMUSG00000028861, ENSMUSG00000061983, ENSMUSG00000054312, ENSMUSG00000023004, ENSMUSG00000061787, ENSMUSG00000058267, ENSMUSG00000043716, ENSMUSG00000024902, ENSMUSG00000041841, ENSMUSG00000030432, ENSMUSG00000029661, ENSMUSG00000060938, ENSMUSG00000067274, ENSMUSG00000049751, ENSMUSG00000028081, ENSMUSG00000058600, ENSMUSG00000025362, ENSMUSG00000061477, ENSMUSG00000036781, ENSMUSG00000049517, ENSMUSG00000063457, ENSMUSG00000057322, ENSMUSG00000008668, ENSMUSG00000039640, ENSMUSG00000062006, ENSMUSG00000046364, ENSMUSG00000020460, ENSMUSG00000039001, ENSMUSG00000044533, ENSMUSG00000032518, ENSMUSG00000062328, ENSMUSG00000045128, ENSMUSG00000010406, ENSMUSG00000059070, ENSMUSG00000057863, ENSMUSG00000041453, ENSMUSG00000028495, ENSMUSG00000031320, ENSMUSG00000003970, ENSMUSG00000079435
 ## 4 ENSMUSG00000071866, ENSMUSG00000062647, ENSMUSG00000029177, ENSMUSG00000038690, ENSMUSG00000050856, ENSMUSG00000025794, ENSMUSG00000003072, ENSMUSG00000030647, ENSMUSG00000051234, ENSMUSG00000006058, ENSMUSG00000006057, ENSMUSG00000037563, ENSMUSG00000024369, ENSMUSG00000021265, ENSMUSG00000060636, ENSMUSG00000040824, ENSMUSG00000067847, ENSMUSG00000072941, ENSMUSG00000025487, ENSMUSG00000048758, ENSMUSG00000072949, ENSMUSG00000021102, ENSMUSG00000053898, ENSMUSG00000035439, ENSMUSG00000059734, ENSMUSG00000025508, ENSMUSG00000004610, ENSMUSG00000020307, ENSMUSG00000020903, ENSMUSG00000020308, ENSMUSG00000056209, ENSMUSG00000001506, ENSMUSG00000037805, ENSMUSG00000001131, ENSMUSG00000030122, ENSMUSG00000024608, ENSMUSG00000045948, ENSMUSG00000040048, ENSMUSG00000012848, ENSMUSG00000059325, ENSMUSG00000057841, ENSMUSG00000042043, ENSMUSG00000078427, ENSMUSG00000000149, ENSMUSG00000061360, ENSMUSG00000031762, ENSMUSG00000054312, ENSMUSG00000061787, ENSMUSG00000031662, ENSMUSG00000031375, ENSMUSG00000041841, ENSMUSG00000024902, ENSMUSG00000026750, ENSMUSG00000019997, ENSMUSG00000020241, ENSMUSG00000044005, ENSMUSG00000015804, ENSMUSG00000056399, ENSMUSG00000027133, ENSMUSG00000025362, ENSMUSG00000031765, ENSMUSG00000015806, ENSMUSG00000049517, ENSMUSG00000078812, ENSMUSG00000062515, ENSMUSG00000032288, ENSMUSG00000013822, ENSMUSG00000062006, ENSMUSG00000016252, ENSMUSG00000034566, ENSMUSG00000032518, ENSMUSG00000026895, ENSMUSG00000037152, ENSMUSG00000047459, ENSMUSG00000001020, ENSMUSG00000059070, ENSMUSG00000041453, ENSMUSG00000001025, ENSMUSG00000061286, ENSMUSG00000032112, ENSMUSG00000035242, ENSMUSG00000016344, ENSMUSG00000030357, ENSMUSG00000006941, ENSMUSG00000031157, ENSMUSG00000032602, ENSMUSG00000035478, ENSMUSG00000004558, ENSMUSG00000021660, ENSMUSG00000040681, ENSMUSG00000059291, ENSMUSG00000062683, ENSMUSG00000029545, ENSMUSG00000029642, ENSMUSG00000001056, ENSMUSG00000035674, ENSMUSG00000036835, ENSMUSG00000023939, ENSMUSG00000035530, ENSMUSG00000043091, ENSMUSG00000024099, ENSMUSG00000030871, ENSMUSG00000047676, ENSMUSG00000028936, ENSMUSG00000063931, ENSMUSG00000020219, ENSMUSG00000047675, ENSMUSG00000003429, ENSMUSG00000002768, ENSMUSG00000005161, ENSMUSG00000018339, ENSMUSG00000015092, ENSMUSG00000008683, ENSMUSG00000008682, ENSMUSG00000031754, ENSMUSG00000073702, ENSMUSG00000067288, ENSMUSG00000062963, ENSMUSG00000022354, ENSMUSG00000052738, ENSMUSG00000030879, ENSMUSG00000046432, ENSMUSG00000027404, ENSMUSG00000043866, ENSMUSG00000026511, ENSMUSG00000027673, ENSMUSG00000021917, ENSMUSG00000023004, ENSMUSG00000020108, ENSMUSG00000001289, ENSMUSG00000004849, ENSMUSG00000009549, ENSMUSG00000032231, ENSMUSG00000026032, ENSMUSG00000006442, ENSMUSG00000060938, ENSMUSG00000067274, ENSMUSG00000049751, ENSMUSG00000031807, ENSMUSG00000058600, ENSMUSG00000057322, ENSMUSG00000039450, ENSMUSG00000036820, ENSMUSG00000050708, ENSMUSG00000030750, ENSMUSG00000022037, ENSMUSG00000070436, ENSMUSG00000078713, ENSMUSG00000044533, ENSMUSG00000022234, ENSMUSG00000031029, ENSMUSG00000029538, ENSMUSG00000041126, ENSMUSG00000010406, ENSMUSG00000078348, ENSMUSG00000019054, ENSMUSG00000057863, ENSMUSG00000031320, ENSMUSG00000060803, ENSMUSG00000066442, ENSMUSG00000022450, ENSMUSG00000025967, ENSMUSG00000078974, ENSMUSG00000020473, ENSMUSG00000000740, ENSMUSG00000071415, ENSMUSG00000061315, ENSMUSG00000038717, ENSMUSG00000055762, ENSMUSG00000049775, ENSMUSG00000062997, ENSMUSG00000018770, ENSMUSG00000002043, ENSMUSG00000025068, ENSMUSG00000021453, ENSMUSG00000028648, ENSMUSG00000044894, ENSMUSG00000020477, ENSMUSG00000029810, ENSMUSG00000059534, ENSMUSG00000031068, ENSMUSG00000035202, ENSMUSG00000025290, ENSMUSG00000079037, ENSMUSG00000034892, ENSMUSG00000028062, ENSMUSG00000020485, ENSMUSG00000063316, ENSMUSG00000031590, ENSMUSG00000038374, ENSMUSG00000030744, ENSMUSG00000006728, ENSMUSG00000028234, ENSMUSG00000028832, ENSMUSG00000053317, ENSMUSG00000039221, ENSMUSG00000018585, ENSMUSG00000029038, ENSMUSG00000002233, ENSMUSG00000040952, ENSMUSG00000076617, ENSMUSG00000030057, ENSMUSG00000076612, ENSMUSG00000076613, ENSMUSG00000024309, ENSMUSG00000021607, ENSMUSG00000020766, ENSMUSG00000042770, ENSMUSG00000036748, ENSMUSG00000049960, ENSMUSG00000060860, ENSMUSG00000029632, ENSMUSG00000028861, ENSMUSG00000060036, ENSMUSG00000028964, ENSMUSG00000061983, ENSMUSG00000038489, ENSMUSG00000028367, ENSMUSG00000005354, ENSMUSG00000057278, ENSMUSG00000004285, ENSMUSG00000021606, ENSMUSG00000022820, ENSMUSG00000065990, ENSMUSG00000037601, ENSMUSG00000002477, ENSMUSG00000030432, ENSMUSG00000005732, ENSMUSG00000020695, ENSMUSG00000022415, ENSMUSG00000061477, ENSMUSG00000024038, ENSMUSG00000063457, ENSMUSG00000076437, ENSMUSG00000031059, ENSMUSG00000028044, ENSMUSG00000031848, ENSMUSG00000008668, ENSMUSG00000014294, ENSMUSG00000069744, ENSMUSG00000014769, ENSMUSG00000028851, ENSMUSG00000039001, ENSMUSG00000050014, ENSMUSG00000034353, ENSMUSG00000062328, ENSMUSG00000045128, ENSMUSG00000028756, ENSMUSG00000018567, ENSMUSG00000022174, ENSMUSG00000076432, ENSMUSG00000022427, ENSMUSG00000079435, ENSMUSG00000028651, ENSMUSG00000007892, ENSMUSG00000052429, ENSMUSG00000047215, ENSMUSG00000018293, ENSMUSG00000007891, ENSMUSG00000000782, ENSMUSG00000058779, ENSMUSG00000015013, ENSMUSG00000033938, ENSMUSG00000046330, ENSMUSG00000079641, ENSMUSG00000004268, ENSMUSG00000002289, ENSMUSG00000027613, ENSMUSG00000019929, ENSMUSG00000033020, ENSMUSG00000034345, ENSMUSG00000018286, ENSMUSG00000033735, ENSMUSG00000029066, ENSMUSG00000030413, ENSMUSG00000021411, ENSMUSG00000022601, ENSMUSG00000044475, ENSMUSG00000075706, ENSMUSG00000036850, ENSMUSG00000021024, ENSMUSG00000020180, ENSMUSG00000021025, ENSMUSG00000030804, ENSMUSG00000036181, ENSMUSG00000020736, ENSMUSG00000024914, ENSMUSG00000020186, ENSMUSG00000023484, ENSMUSG00000038274, ENSMUSG00000020440, ENSMUSG00000022400, ENSMUSG00000032171, ENSMUSG00000020444, ENSMUSG00000017404, ENSMUSG00000074129, ENSMUSG00000002379, ENSMUSG00000022098, ENSMUSG00000068220, ENSMUSG00000008036, ENSMUSG00000038900, ENSMUSG00000010376, ENSMUSG00000046865, ENSMUSG00000063882, ENSMUSG00000006333, ENSMUSG00000030814, ENSMUSG00000036256, ENSMUSG00000036199, ENSMUSG00000032959, ENSMUSG00000032369, ENSMUSG00000058267, ENSMUSG00000016554, ENSMUSG00000073676, ENSMUSG00000036390, ENSMUSG00000000563, ENSMUSG00000043716, ENSMUSG00000055839, ENSMUSG00000028081, ENSMUSG00000005779, ENSMUSG00000053565, ENSMUSG00000024844, ENSMUSG00000036781, ENSMUSG00000023089, ENSMUSG00000016427, ENSMUSG00000032966, ENSMUSG00000020163, ENSMUSG00000028998, ENSMUSG00000039640, ENSMUSG00000046364, ENSMUSG00000020460, ENSMUSG00000032554, ENSMUSG00000041881, ENSMUSG00000042541, ENSMUSG00000042747, ENSMUSG00000071662, ENSMUSG00000022982, ENSMUSG00000002395, ENSMUSG00000028495, ENSMUSG00000006095, ENSMUSG00000003970
 ## 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     ENSMUSG00000071866, ENSMUSG00000038690, ENSMUSG00000050856, ENSMUSG00000025794, ENSMUSG00000003072, ENSMUSG00000030647, ENSMUSG00000051234, ENSMUSG00000006058, ENSMUSG00000006057, ENSMUSG00000037563, ENSMUSG00000024369, ENSMUSG00000060636, ENSMUSG00000040824, ENSMUSG00000072941, ENSMUSG00000048758, ENSMUSG00000072949, ENSMUSG00000053898, ENSMUSG00000059734, ENSMUSG00000004610, ENSMUSG00000025508, ENSMUSG00000020307, ENSMUSG00000056209, ENSMUSG00000037805, ENSMUSG00000030122, ENSMUSG00000024608, ENSMUSG00000045948, ENSMUSG00000040048, ENSMUSG00000012848, ENSMUSG00000059325, ENSMUSG00000057841, ENSMUSG00000078427, ENSMUSG00000061360, ENSMUSG00000054312, ENSMUSG00000061787, ENSMUSG00000031375, ENSMUSG00000024902, ENSMUSG00000041841, ENSMUSG00000026750, ENSMUSG00000019997, ENSMUSG00000015804, ENSMUSG00000044005, ENSMUSG00000056399, ENSMUSG00000027133, ENSMUSG00000025362, ENSMUSG00000015806, ENSMUSG00000049517, ENSMUSG00000078812, ENSMUSG00000062515, ENSMUSG00000062006, ENSMUSG00000013822, ENSMUSG00000032288, ENSMUSG00000016252, ENSMUSG00000034566, ENSMUSG00000032518, ENSMUSG00000026895, ENSMUSG00000037152, ENSMUSG00000059070, ENSMUSG00000041453, ENSMUSG00000061286, ENSMUSG00000032112, ENSMUSG00000035242, ENSMUSG00000030357, ENSMUSG00000006941, ENSMUSG00000031157, ENSMUSG00000035478, ENSMUSG00000021660, ENSMUSG00000040681, ENSMUSG00000059291, ENSMUSG00000062683, ENSMUSG00000029545, ENSMUSG00000029642, ENSMUSG00000001056, ENSMUSG00000035674, ENSMUSG00000036835, ENSMUSG00000023939, ENSMUSG00000035530, ENSMUSG00000030871, ENSMUSG00000024099, ENSMUSG00000047676, ENSMUSG00000028936, ENSMUSG00000047675, ENSMUSG00000003429, ENSMUSG00000005161, ENSMUSG00000018339, ENSMUSG00000015092, ENSMUSG00000008683, ENSMUSG00000031754, ENSMUSG00000008682, ENSMUSG00000073702, ENSMUSG00000062963, ENSMUSG00000067288, ENSMUSG00000022354, ENSMUSG00000052738, ENSMUSG00000030879, ENSMUSG00000027404, ENSMUSG00000043866, ENSMUSG00000021917, ENSMUSG00000027673, ENSMUSG00000001289, ENSMUSG00000026032, ENSMUSG00000006442, ENSMUSG00000060938, ENSMUSG00000049751, ENSMUSG00000067274, ENSMUSG00000031807, ENSMUSG00000058600, ENSMUSG00000057322, ENSMUSG00000039450, ENSMUSG00000036820, ENSMUSG00000030750, ENSMUSG00000044533, ENSMUSG00000022234, ENSMUSG00000031029, ENSMUSG00000029538, ENSMUSG00000010406, ENSMUSG00000078348, ENSMUSG00000057863, ENSMUSG00000031320, ENSMUSG00000060803, ENSMUSG00000066442, ENSMUSG00000022450, ENSMUSG00000025967, ENSMUSG00000020473, ENSMUSG00000000740, ENSMUSG00000071415, ENSMUSG00000061315, ENSMUSG00000038717, ENSMUSG00000055762, ENSMUSG00000062997, ENSMUSG00000018770, ENSMUSG00000025068, ENSMUSG00000021453, ENSMUSG00000028648, ENSMUSG00000044894, ENSMUSG00000020477, ENSMUSG00000059534, ENSMUSG00000035202, ENSMUSG00000025290, ENSMUSG00000079037, ENSMUSG00000034892, ENSMUSG00000028062, ENSMUSG00000020485, ENSMUSG00000063316, ENSMUSG00000031590, ENSMUSG00000038374, ENSMUSG00000030744, ENSMUSG00000006728, ENSMUSG00000028234, ENSMUSG00000039221, ENSMUSG00000029038, ENSMUSG00000040952, ENSMUSG00000076617, ENSMUSG00000030057, ENSMUSG00000024309, ENSMUSG00000042770, ENSMUSG00000020766, ENSMUSG00000021607, ENSMUSG00000036748, ENSMUSG00000049960, ENSMUSG00000060860, ENSMUSG00000029632, ENSMUSG00000028861, ENSMUSG00000060036, ENSMUSG00000028964, ENSMUSG00000061983, ENSMUSG00000038489, ENSMUSG00000028367, ENSMUSG00000005354, ENSMUSG00000057278, ENSMUSG00000004285, ENSMUSG00000021606, ENSMUSG00000022820, ENSMUSG00000065990, ENSMUSG00000037601, ENSMUSG00000002477, ENSMUSG00000030432, ENSMUSG00000061477, ENSMUSG00000024038, ENSMUSG00000063457, ENSMUSG00000031059, ENSMUSG00000031848, ENSMUSG00000008668, ENSMUSG00000014294, ENSMUSG00000069744, ENSMUSG00000014769, ENSMUSG00000039001, ENSMUSG00000050014, ENSMUSG00000062328, ENSMUSG00000045128, ENSMUSG00000028756, ENSMUSG00000079435, ENSMUSG00000028651, ENSMUSG00000007892, ENSMUSG00000052429, ENSMUSG00000047215, ENSMUSG00000007891, ENSMUSG00000000782, ENSMUSG00000033938, ENSMUSG00000046330, ENSMUSG00000079641, ENSMUSG00000004268, ENSMUSG00000027613, ENSMUSG00000019929, ENSMUSG00000033020, ENSMUSG00000034345, ENSMUSG00000018286, ENSMUSG00000033735, ENSMUSG00000029066, ENSMUSG00000022601, ENSMUSG00000044475, ENSMUSG00000075706, ENSMUSG00000036850, ENSMUSG00000021024, ENSMUSG00000020180, ENSMUSG00000030804, ENSMUSG00000020736, ENSMUSG00000024914, ENSMUSG00000038274, ENSMUSG00000022400, ENSMUSG00000032171, ENSMUSG00000020444, ENSMUSG00000017404, ENSMUSG00000074129, ENSMUSG00000002379, ENSMUSG00000038900, ENSMUSG00000010376, ENSMUSG00000046865, ENSMUSG00000063882, ENSMUSG00000006333, ENSMUSG00000036199, ENSMUSG00000058267, ENSMUSG00000016554, ENSMUSG00000073676, ENSMUSG00000000563, ENSMUSG00000055839, ENSMUSG00000043716, ENSMUSG00000028081, ENSMUSG00000005779, ENSMUSG00000053565, ENSMUSG00000024844, ENSMUSG00000036781, ENSMUSG00000023089, ENSMUSG00000016427, ENSMUSG00000032966, ENSMUSG00000020163, ENSMUSG00000039640, ENSMUSG00000046364, ENSMUSG00000020460, ENSMUSG00000041881, ENSMUSG00000042541, ENSMUSG00000042747, ENSMUSG00000071662, ENSMUSG00000022982, ENSMUSG00000028495, ENSMUSG00000003970
 ## 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ENSMUSG00000071866, ENSMUSG00000025967, ENSMUSG00000025794, ENSMUSG00000000740, ENSMUSG00000071415, ENSMUSG00000055762, ENSMUSG00000062997, ENSMUSG00000051234, ENSMUSG00000037563, ENSMUSG00000060636, ENSMUSG00000021453, ENSMUSG00000020477, ENSMUSG00000048758, ENSMUSG00000035202, ENSMUSG00000025290, ENSMUSG00000025508, ENSMUSG00000020307, ENSMUSG00000034892, ENSMUSG00000028062, ENSMUSG00000063316, ENSMUSG00000037805, ENSMUSG00000030122, ENSMUSG00000030744, ENSMUSG00000006728, ENSMUSG00000024608, ENSMUSG00000045948, ENSMUSG00000028234, ENSMUSG00000039221, ENSMUSG00000012848, ENSMUSG00000059325, ENSMUSG00000057841, ENSMUSG00000076617, ENSMUSG00000040952, ENSMUSG00000021607, ENSMUSG00000024309, ENSMUSG00000049960, ENSMUSG00000036748, ENSMUSG00000060860, ENSMUSG00000028861, ENSMUSG00000060036, ENSMUSG00000061983, ENSMUSG00000054312, ENSMUSG00000061787, ENSMUSG00000065990, ENSMUSG00000031375, ENSMUSG00000041841, ENSMUSG00000024902, ENSMUSG00000030432, ENSMUSG00000026750, ENSMUSG00000056399, ENSMUSG00000061477, ENSMUSG00000025362, ENSMUSG00000049517, ENSMUSG00000063457, ENSMUSG00000008668, ENSMUSG00000069744, ENSMUSG00000014769, ENSMUSG00000078812, ENSMUSG00000062006, ENSMUSG00000039001, ENSMUSG00000032518, ENSMUSG00000062328, ENSMUSG00000045128, ENSMUSG00000059070, ENSMUSG00000028756, ENSMUSG00000041453, ENSMUSG00000028651, ENSMUSG00000079435, ENSMUSG00000007892, ENSMUSG00000052429, ENSMUSG00000047215, ENSMUSG00000030357, ENSMUSG00000006941, ENSMUSG00000035478, ENSMUSG00000079641, ENSMUSG00000046330, ENSMUSG00000059291, ENSMUSG00000027613, ENSMUSG00000019929, ENSMUSG00000018286, ENSMUSG00000036835, ENSMUSG00000029066, ENSMUSG00000023939, ENSMUSG00000022601, ENSMUSG00000035530, ENSMUSG00000036850, ENSMUSG00000030871, ENSMUSG00000021024, ENSMUSG00000028936, ENSMUSG00000047676, ENSMUSG00000047675, ENSMUSG00000003429, ENSMUSG00000005161, ENSMUSG00000038274, ENSMUSG00000022400, ENSMUSG00000008683, ENSMUSG00000032171, ENSMUSG00000008682, ENSMUSG00000073702, ENSMUSG00000067288, ENSMUSG00000062963, ENSMUSG00000017404, ENSMUSG00000074129, ENSMUSG00000030879, ENSMUSG00000038900, ENSMUSG00000010376, ENSMUSG00000006333, ENSMUSG00000021917, ENSMUSG00000016554, ENSMUSG00000058267, ENSMUSG00000073676, ENSMUSG00000001289, ENSMUSG00000043716, ENSMUSG00000055839, ENSMUSG00000060938, ENSMUSG00000067274, ENSMUSG00000049751, ENSMUSG00000028081, ENSMUSG00000005779, ENSMUSG00000053565, ENSMUSG00000058600, ENSMUSG00000036781, ENSMUSG00000057322, ENSMUSG00000032966, ENSMUSG00000039640, ENSMUSG00000046364, ENSMUSG00000020460, ENSMUSG00000044533, ENSMUSG00000022234, ENSMUSG00000031029, ENSMUSG00000042747, ENSMUSG00000022982, ENSMUSG00000010406, ENSMUSG00000028495, ENSMUSG00000031320, ENSMUSG00000057863, ENSMUSG00000003970
 ##   List.Total Pop.Hits Pop.Total Fold.Enrichment   Bonferroni    Benjamini
 ## 1        380      151     15404       22.281840 1.288995e-90 1.288995e-90
 ## 2        373      319     14219       11.233042 1.022150e-69 1.022150e-69
 ## 3        380      450     15404        7.927205 4.601697e-51 2.300849e-51
 ## 4        373     9252     14219        1.392648 8.197138e-28 4.098569e-28
 ## 5        373     5857     14219        1.646666 1.112110e-22 3.707033e-23
 ## 6        373     2088     14219        2.464697 3.574572e-22 8.936430e-23
 ##            FDR
 ## 1 4.267656e-90
 ## 2 1.134887e-69
 ## 3 1.523548e-50
 ## 4 9.101232e-28
 ## 5 1.234769e-22
 ## 6 3.968826e-22

Pathway Analysis on DEC down-regulated intersected with E2 up-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_DOWN_E2_UP_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list3 , db.selected: KEGG_PATHWAY , analysis.type: Pathway , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_DEC_DOWN_E2_UP_genes_in_intersection.txt_DAVID_Pathway_KEGG_PATHWAY_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##       Category                                            Term Count X.
 ## 1 KEGG_PATHWAY mmu04060:Cytokine-cytokine receptor interaction     4 40
 ##         PValue
 ## 1 0.0007128032
 ##                                                                            Genes
 ## 1 ENSMUSG00000026070, ENSMUSG00000004296, ENSMUSG00000018752, ENSMUSG00000017652
 ##   List.Total Pop.Hits Pop.Total Fold.Enrichment Bonferroni  Benjamini
 ## 1          6      244      5738         15.6776 0.01063886 0.01063886
 ##        FDR
 ## 1 0.494533

Gene Ontology Analysis on DEC down-regulated intersected with E2 up-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_DOWN_E2_UP_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list3 , db.selected: GOTERM_MF_ALL , analysis.type: GO , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_DOWN_E2_UP_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list3 , db.selected: GOTERM_BP_ALL , analysis.type: GO , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_DEC_DOWN_E2_UP_genes_in_intersection.txt_DAVID_ALL_GO_GOTERM_MF_ALL_GOTERM_BP_ALL_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##  [1] Category        Term            Count           X.             
 ##  [5] PValue          Genes           List.Total      Pop.Hits       
 ##  [9] Pop.Total       Fold.Enrichment Bonferroni      Benjamini      
 ## [13] FDR            
 ## <0 rows> (or 0-length row.names)

Pathway Analysis on DEC up-regulated intersected with E2 down-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_UP_E2_DOWN_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list4 , db.selected: KEGG_PATHWAY , analysis.type: Pathway , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_DEC_UP_E2_DOWN_genes_in_intersection.txt_DAVID_Pathway_KEGG_PATHWAY_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##  [1] Category        Term            Count           X.             
 ##  [5] PValue          Genes           List.Total      Pop.Hits       
 ##  [9] Pop.Total       Fold.Enrichment Bonferroni      Benjamini      
 ## [13] FDR            
 ## <0 rows> (or 0-length row.names)

Gene Ontology Analysis on DEC up-regulated intersected with E2 down-regulated genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_UP_E2_DOWN_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list4 , db.selected: GOTERM_MF_ALL , analysis.type: GO , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/DEC_UP_E2_DOWN_genes_in_intersection.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list4 , db.selected: GOTERM_BP_ALL , analysis.type: GO , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_DEC_UP_E2_DOWN_genes_in_intersection.txt_DAVID_ALL_GO_GOTERM_MF_ALL_GOTERM_BP_ALL_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##        Category                                     Term Count        X.
 ## 1 GOTERM_BP_ALL               GO:0006955~immune response    18 21.428571
 ## 2 GOTERM_BP_ALL         GO:0002376~immune system process    19 22.619048
 ## 3 GOTERM_BP_ALL          GO:0050896~response to stimulus    29 34.523810
 ## 4 GOTERM_BP_ALL             GO:0009615~response to virus     6  7.142857
 ## 5 GOTERM_MF_ALL GO:0032555~purine ribonucleotide binding    22 26.190476
 ## 6 GOTERM_MF_ALL        GO:0032553~ribonucleotide binding    22 26.190476
 ##         PValue
 ## 1 3.939597e-13
 ## 2 1.531680e-10
 ## 3 1.836444e-09
 ## 4 8.156406e-06
 ## 5 1.412989e-05
 ## 6 1.412989e-05
 ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Genes
 ## 1                                                                                                                                                                                                                             ENSMUSG00000032661, ENSMUSG00000046879, ENSMUSG00000056116, ENSMUSG00000015947, ENSMUSG00000040296, ENSMUSG00000070427, ENSMUSG00000026656, ENSMUSG00000020641, ENSMUSG00000052776, ENSMUSG00000032690, ENSMUSG00000028270, ENSMUSG00000035373, ENSMUSG00000055172, ENSMUSG00000029561, ENSMUSG00000025498, ENSMUSG00000066861, ENSMUSG00000035385, ENSMUSG00000017830
 ## 2                                                                                                                                                                                                         ENSMUSG00000032661, ENSMUSG00000046879, ENSMUSG00000056116, ENSMUSG00000015947, ENSMUSG00000040296, ENSMUSG00000070427, ENSMUSG00000026656, ENSMUSG00000020641, ENSMUSG00000052776, ENSMUSG00000038418, ENSMUSG00000032690, ENSMUSG00000028270, ENSMUSG00000035373, ENSMUSG00000055172, ENSMUSG00000029561, ENSMUSG00000025498, ENSMUSG00000066861, ENSMUSG00000035385, ENSMUSG00000017830
 ## 3 ENSMUSG00000032661, ENSMUSG00000046879, ENSMUSG00000015947, ENSMUSG00000040296, ENSMUSG00000070427, ENSMUSG00000026656, ENSMUSG00000052776, ENSMUSG00000006782, ENSMUSG00000038418, ENSMUSG00000026104, ENSMUSG00000028270, ENSMUSG00000035373, ENSMUSG00000029561, ENSMUSG00000035692, ENSMUSG00000021871, ENSMUSG00000073489, ENSMUSG00000056116, ENSMUSG00000079017, ENSMUSG00000020641, ENSMUSG00000032690, ENSMUSG00000021250, ENSMUSG00000055172, ENSMUSG00000025492, ENSMUSG00000078920, ENSMUSG00000019987, ENSMUSG00000025498, ENSMUSG00000066861, ENSMUSG00000017830, ENSMUSG00000035385
 ## 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ENSMUSG00000079017, ENSMUSG00000040296, ENSMUSG00000020641, ENSMUSG00000035692, ENSMUSG00000025498, ENSMUSG00000052776
 ## 5                                                                                                                                             ENSMUSG00000032661, ENSMUSG00000035208, ENSMUSG00000082292, ENSMUSG00000046879, ENSMUSG00000037820, ENSMUSG00000040296, ENSMUSG00000078763, ENSMUSG00000000204, ENSMUSG00000052776, ENSMUSG00000032690, ENSMUSG00000072620, ENSMUSG00000028270, ENSMUSG00000027078, ENSMUSG00000078920, ENSMUSG00000029561, ENSMUSG00000020638, ENSMUSG00000066861, ENSMUSG00000027293, ENSMUSG00000060519, ENSMUSG00000078853, ENSMUSG00000017830, ENSMUSG00000069874
 ## 6                                                                                                                                             ENSMUSG00000032661, ENSMUSG00000035208, ENSMUSG00000082292, ENSMUSG00000046879, ENSMUSG00000037820, ENSMUSG00000040296, ENSMUSG00000078763, ENSMUSG00000000204, ENSMUSG00000052776, ENSMUSG00000032690, ENSMUSG00000072620, ENSMUSG00000028270, ENSMUSG00000027078, ENSMUSG00000078920, ENSMUSG00000029561, ENSMUSG00000020638, ENSMUSG00000066861, ENSMUSG00000027293, ENSMUSG00000060519, ENSMUSG00000078853, ENSMUSG00000017830, ENSMUSG00000069874
 ##   List.Total Pop.Hits Pop.Total Fold.Enrichment   Bonferroni    Benjamini
 ## 1         53      471     14219       10.252854 2.702203e-10 2.702203e-10
 ## 2         53      791     14219        6.444219 1.050732e-07 5.253660e-08
 ## 3         53     2437     14219        3.192535 1.259800e-06 4.199334e-07
 ## 4         53       76     14219       21.180238 5.579692e-03 1.397851e-03
 ## 5         68     1796     15404        2.774859 2.032649e-03 2.032649e-03
 ## 6         68     1796     15404        2.774859 2.032649e-03 2.032649e-03
 ##            FDR
 ## 1 5.924261e-10
 ## 2 2.303620e-07
 ## 3 2.761980e-06
 ## 4 1.226639e-02
 ## 5 1.675292e-02
 ## 6 1.675292e-02

Pathway Analysis on E2 up-regulated not in DEC genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/genes_in_E2_UP_not_in_DEC.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list5 , db.selected: KEGG_PATHWAY , analysis.type: Pathway , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_genes_in_E2_UP_not_in_DEC.txt_DAVID_Pathway_KEGG_PATHWAY_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##       Category                                               Term Count
 ## 1 KEGG_PATHWAY                            mmu04510:Focal adhesion    14
 ## 2 KEGG_PATHWAY                mmu04630:Jak-STAT signaling pathway    11
 ## 3 KEGG_PATHWAY          mmu04810:Regulation of actin cytoskeleton    13
 ## 4 KEGG_PATHWAY mmu04960:Aldosterone-regulated sodium reabsorption     6
 ##         X.       PValue
 ## 1 3.349282 0.0001113010
 ## 2 2.631579 0.0007063283
 ## 3 3.110048 0.0009870693
 ## 4 1.435407 0.0012390469
 ##                                                                                                                                                                                                                                                                                    Genes
 ## 1 ENSMUSG00000020689, ENSMUSG00000001281, ENSMUSG00000024456, ENSMUSG00000019699, ENSMUSG00000009376, ENSMUSG00000000555, ENSMUSG00000020573, ENSMUSG00000001930, ENSMUSG00000021823, ENSMUSG00000020580, ENSMUSG00000052889, ENSMUSG00000024122, ENSMUSG00000025321, ENSMUSG00000032000
 ## 2                                                             ENSMUSG00000020573, ENSMUSG00000059326, ENSMUSG00000024789, ENSMUSG00000040663, ENSMUSG00000022637, ENSMUSG00000021756, ENSMUSG00000071714, ENSMUSG00000019699, ENSMUSG00000030745, ENSMUSG00000071713, ENSMUSG00000003882
 ## 3                     ENSMUSG00000020689, ENSMUSG00000031133, ENSMUSG00000020573, ENSMUSG00000037946, ENSMUSG00000021823, ENSMUSG00000030789, ENSMUSG00000020580, ENSMUSG00000001281, ENSMUSG00000024456, ENSMUSG00000059495, ENSMUSG00000025321, ENSMUSG00000000555, ENSMUSG00000000157
 ## 4                                                                                                                                                                 ENSMUSG00000020573, ENSMUSG00000040907, ENSMUSG00000052889, ENSMUSG00000024122, ENSMUSG00000005534, ENSMUSG00000019970
 ##   List.Total Pop.Hits Pop.Total Fold.Enrichment Bonferroni  Benjamini
 ## 1        113      198      5738        3.590417 0.01238902 0.01238902
 ## 2        113      152      5738        3.674779 0.07608640 0.03879576
 ## 3        113      217      5738        3.042046 0.10470889 0.03619741
 ## 4        113       42      5738        7.254109 0.12964952 0.03411918
 ##         FDR
 ## 1 0.1260958
 ## 2 0.7977624
 ## 3 1.1132303
 ## 4 1.3955978

Gene Ontology on E2 up-regulated not in DEC genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/genes_in_E2_UP_not_in_DEC.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list5 , db.selected: GOTERM_MF_ALL , analysis.type: GO , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/genes_in_E2_UP_not_in_DEC.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list5 , db.selected: GOTERM_BP_ALL , analysis.type: GO , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_genes_in_E2_UP_not_in_DEC.txt_DAVID_ALL_GO_GOTERM_MF_ALL_GOTERM_BP_ALL_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##        Category                                         Term Count
 ## 1 GOTERM_BP_ALL GO:0009966~regulation of signal transduction    42
 ## 2 GOTERM_BP_ALL  GO:0010646~regulation of cell communication    45
 ## 3 GOTERM_MF_ALL                       GO:0005524~ATP binding    67
 ## 4 GOTERM_MF_ALL     GO:0032559~adenyl ribonucleotide binding    67
 ## 5 GOTERM_MF_ALL         GO:0030554~adenyl nucleotide binding    68
 ## 6 GOTERM_MF_ALL         GO:0001883~purine nucleoside binding    68
 ##         X.       PValue
 ## 1 10.04785 1.227286e-09
 ## 2 10.76555 3.760833e-09
 ## 3 16.02871 4.511255e-09
 ## 4 16.02871 7.131723e-09
 ## 5 16.26794 2.160112e-08
 ## 6 16.26794 3.007909e-08
 ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Genes
 ## 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         ENSMUSG00000053754, ENSMUSG00000031133, ENSMUSG00000034593, ENSMUSG00000028030, ENSMUSG00000029094, ENSMUSG00000019852, ENSMUSG00000027652, ENSMUSG00000022508, ENSMUSG00000037706, ENSMUSG00000021948, ENSMUSG00000036473, ENSMUSG00000025199, ENSMUSG00000059495, ENSMUSG00000022749, ENSMUSG00000074272, ENSMUSG00000027276, ENSMUSG00000031391, ENSMUSG00000028991, ENSMUSG00000031709, ENSMUSG00000037946, ENSMUSG00000020716, ENSMUSG00000054051, ENSMUSG00000024789, ENSMUSG00000022637, ENSMUSG00000021756, ENSMUSG00000018909, ENSMUSG00000027660, ENSMUSG00000037552, ENSMUSG00000024451, ENSMUSG00000021670, ENSMUSG00000032413, ENSMUSG00000009376, ENSMUSG00000020859, ENSMUSG00000001588, ENSMUSG00000057530, ENSMUSG00000037533, ENSMUSG00000035236, ENSMUSG00000070565, ENSMUSG00000038520, ENSMUSG00000021027, ENSMUSG00000034610, ENSMUSG00000024163
 ## 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                             ENSMUSG00000053754, ENSMUSG00000031133, ENSMUSG00000034593, ENSMUSG00000036098, ENSMUSG00000028030, ENSMUSG00000029094, ENSMUSG00000019852, ENSMUSG00000027652, ENSMUSG00000022508, ENSMUSG00000031393, ENSMUSG00000037868, ENSMUSG00000037706, ENSMUSG00000021948, ENSMUSG00000036473, ENSMUSG00000025199, ENSMUSG00000059495, ENSMUSG00000022749, ENSMUSG00000074272, ENSMUSG00000027276, ENSMUSG00000031391, ENSMUSG00000028991, ENSMUSG00000020716, ENSMUSG00000031709, ENSMUSG00000037946, ENSMUSG00000054051, ENSMUSG00000024789, ENSMUSG00000022637, ENSMUSG00000021756, ENSMUSG00000018909, ENSMUSG00000027660, ENSMUSG00000037552, ENSMUSG00000024451, ENSMUSG00000021670, ENSMUSG00000032413, ENSMUSG00000009376, ENSMUSG00000020859, ENSMUSG00000001588, ENSMUSG00000057530, ENSMUSG00000037533, ENSMUSG00000035236, ENSMUSG00000070565, ENSMUSG00000038520, ENSMUSG00000021027, ENSMUSG00000034610, ENSMUSG00000024163
 ## 3                     ENSMUSG00000053754, ENSMUSG00000007613, ENSMUSG00000060671, ENSMUSG00000020788, ENSMUSG00000048279, ENSMUSG00000021559, ENSMUSG00000026944, ENSMUSG00000022504, ENSMUSG00000022360, ENSMUSG00000020573, ENSMUSG00000026437, ENSMUSG00000022994, ENSMUSG00000021820, ENSMUSG00000025757, ENSMUSG00000052889, ENSMUSG00000031314, ENSMUSG00000025199, ENSMUSG00000042249, ENSMUSG00000020334, ENSMUSG00000019970, ENSMUSG00000004815, ENSMUSG00000054051, ENSMUSG00000058997, ENSMUSG00000024789, ENSMUSG00000021375, ENSMUSG00000036086, ENSMUSG00000022672, ENSMUSG00000026409, ENSMUSG00000020580, ENSMUSG00000024122, ENSMUSG00000031303, ENSMUSG00000042046, ENSMUSG00000024843, ENSMUSG00000034593, ENSMUSG00000028030, ENSMUSG00000040907, ENSMUSG00000025574, ENSMUSG00000030761, ENSMUSG00000024943, ENSMUSG00000000823, ENSMUSG00000035722, ENSMUSG00000038844, ENSMUSG00000005534, ENSMUSG00000024948, ENSMUSG00000024130, ENSMUSG00000033526, ENSMUSG00000009418, ENSMUSG00000021948, ENSMUSG00000039585, ENSMUSG00000023088, ENSMUSG00000028991, ENSMUSG00000029186, ENSMUSG00000036875, ENSMUSG00000001630, ENSMUSG00000041415, ENSMUSG00000038970, ENSMUSG00000019699, ENSMUSG00000040661, ENSMUSG00000040021, ENSMUSG00000024070, ENSMUSG00000009376, ENSMUSG00000023809, ENSMUSG00000001588, ENSMUSG00000020273, ENSMUSG00000005102, ENSMUSG00000037795, ENSMUSG00000041642
 ## 4                     ENSMUSG00000053754, ENSMUSG00000007613, ENSMUSG00000060671, ENSMUSG00000020788, ENSMUSG00000048279, ENSMUSG00000021559, ENSMUSG00000026944, ENSMUSG00000022504, ENSMUSG00000022360, ENSMUSG00000020573, ENSMUSG00000026437, ENSMUSG00000022994, ENSMUSG00000021820, ENSMUSG00000025757, ENSMUSG00000052889, ENSMUSG00000031314, ENSMUSG00000025199, ENSMUSG00000042249, ENSMUSG00000020334, ENSMUSG00000019970, ENSMUSG00000004815, ENSMUSG00000054051, ENSMUSG00000058997, ENSMUSG00000024789, ENSMUSG00000021375, ENSMUSG00000036086, ENSMUSG00000022672, ENSMUSG00000026409, ENSMUSG00000020580, ENSMUSG00000024122, ENSMUSG00000031303, ENSMUSG00000042046, ENSMUSG00000024843, ENSMUSG00000034593, ENSMUSG00000028030, ENSMUSG00000040907, ENSMUSG00000025574, ENSMUSG00000030761, ENSMUSG00000024943, ENSMUSG00000000823, ENSMUSG00000035722, ENSMUSG00000038844, ENSMUSG00000005534, ENSMUSG00000024948, ENSMUSG00000024130, ENSMUSG00000033526, ENSMUSG00000009418, ENSMUSG00000021948, ENSMUSG00000039585, ENSMUSG00000023088, ENSMUSG00000028991, ENSMUSG00000029186, ENSMUSG00000036875, ENSMUSG00000001630, ENSMUSG00000041415, ENSMUSG00000038970, ENSMUSG00000019699, ENSMUSG00000040661, ENSMUSG00000040021, ENSMUSG00000024070, ENSMUSG00000009376, ENSMUSG00000023809, ENSMUSG00000001588, ENSMUSG00000020273, ENSMUSG00000005102, ENSMUSG00000037795, ENSMUSG00000041642
 ## 5 ENSMUSG00000053754, ENSMUSG00000007613, ENSMUSG00000060671, ENSMUSG00000020788, ENSMUSG00000048279, ENSMUSG00000021559, ENSMUSG00000026944, ENSMUSG00000022504, ENSMUSG00000022360, ENSMUSG00000020573, ENSMUSG00000026437, ENSMUSG00000022994, ENSMUSG00000021820, ENSMUSG00000025757, ENSMUSG00000052889, ENSMUSG00000031314, ENSMUSG00000025199, ENSMUSG00000042249, ENSMUSG00000020334, ENSMUSG00000019970, ENSMUSG00000004815, ENSMUSG00000054051, ENSMUSG00000058997, ENSMUSG00000024789, ENSMUSG00000021375, ENSMUSG00000036086, ENSMUSG00000022672, ENSMUSG00000020250, ENSMUSG00000026409, ENSMUSG00000020580, ENSMUSG00000024122, ENSMUSG00000031303, ENSMUSG00000042046, ENSMUSG00000024843, ENSMUSG00000034593, ENSMUSG00000028030, ENSMUSG00000040907, ENSMUSG00000025574, ENSMUSG00000030761, ENSMUSG00000024943, ENSMUSG00000000823, ENSMUSG00000035722, ENSMUSG00000038844, ENSMUSG00000005534, ENSMUSG00000024948, ENSMUSG00000024130, ENSMUSG00000033526, ENSMUSG00000009418, ENSMUSG00000021948, ENSMUSG00000039585, ENSMUSG00000023088, ENSMUSG00000028991, ENSMUSG00000029186, ENSMUSG00000036875, ENSMUSG00000001630, ENSMUSG00000041415, ENSMUSG00000038970, ENSMUSG00000019699, ENSMUSG00000040661, ENSMUSG00000040021, ENSMUSG00000024070, ENSMUSG00000009376, ENSMUSG00000023809, ENSMUSG00000001588, ENSMUSG00000020273, ENSMUSG00000005102, ENSMUSG00000037795, ENSMUSG00000041642
 ## 6 ENSMUSG00000053754, ENSMUSG00000007613, ENSMUSG00000060671, ENSMUSG00000020788, ENSMUSG00000048279, ENSMUSG00000021559, ENSMUSG00000026944, ENSMUSG00000022504, ENSMUSG00000022360, ENSMUSG00000020573, ENSMUSG00000026437, ENSMUSG00000022994, ENSMUSG00000021820, ENSMUSG00000025757, ENSMUSG00000052889, ENSMUSG00000031314, ENSMUSG00000025199, ENSMUSG00000042249, ENSMUSG00000020334, ENSMUSG00000019970, ENSMUSG00000004815, ENSMUSG00000054051, ENSMUSG00000058997, ENSMUSG00000024789, ENSMUSG00000021375, ENSMUSG00000036086, ENSMUSG00000022672, ENSMUSG00000020250, ENSMUSG00000026409, ENSMUSG00000020580, ENSMUSG00000024122, ENSMUSG00000031303, ENSMUSG00000042046, ENSMUSG00000024843, ENSMUSG00000034593, ENSMUSG00000028030, ENSMUSG00000040907, ENSMUSG00000025574, ENSMUSG00000030761, ENSMUSG00000024943, ENSMUSG00000000823, ENSMUSG00000035722, ENSMUSG00000038844, ENSMUSG00000005534, ENSMUSG00000024948, ENSMUSG00000024130, ENSMUSG00000033526, ENSMUSG00000009418, ENSMUSG00000021948, ENSMUSG00000039585, ENSMUSG00000023088, ENSMUSG00000028991, ENSMUSG00000029186, ENSMUSG00000036875, ENSMUSG00000001630, ENSMUSG00000041415, ENSMUSG00000038970, ENSMUSG00000019699, ENSMUSG00000040661, ENSMUSG00000040021, ENSMUSG00000024070, ENSMUSG00000009376, ENSMUSG00000023809, ENSMUSG00000001588, ENSMUSG00000020273, ENSMUSG00000005102, ENSMUSG00000037795, ENSMUSG00000041642
 ##   List.Total Pop.Hits Pop.Total Fold.Enrichment   Bonferroni    Benjamini
 ## 1        310      661     14219        2.914440 2.385842e-06 2.385842e-06
 ## 2        310      771     14219        2.677106 7.311032e-06 3.655523e-06
 ## 3        337     1443     15404        2.122326 2.530811e-06 2.530811e-06
 ## 4        337     1460     15404        2.097614 4.000888e-06 2.000446e-06
 ## 5        337     1535     15404        2.024903 1.211815e-05 4.039401e-06
 ## 6        337     1548     15404        2.007898 1.687423e-05 4.218584e-06
 ##            FDR
 ## 1 2.103102e-06
 ## 2 6.444635e-06
 ## 3 6.601481e-06
 ## 4 1.043610e-05
 ## 5 3.160968e-05
 ## 6 4.401580e-05

Pathway Analysis on DEC up-regulated not in E2 genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/genes_in_DEC_UP_not_in_E2.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list6 , db.selected: KEGG_PATHWAY , analysis.type: Pathway , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_genes_in_DEC_UP_not_in_E2.txt_DAVID_Pathway_KEGG_PATHWAY_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##       Category                                            Term Count
 ## 1 KEGG_PATHWAY   mmu04620:Toll-like receptor signaling pathway    18
 ## 2 KEGG_PATHWAY    mmu04621:NOD-like receptor signaling pathway    11
 ## 3 KEGG_PATHWAY  mmu04622:RIG-I-like receptor signaling pathway     9
 ## 4 KEGG_PATHWAY          mmu04623:Cytosolic DNA-sensing pathway     8
 ## 5 KEGG_PATHWAY mmu04060:Cytokine-cytokine receptor interaction    17
 ##         X.       PValue
 ## 1 3.742204 2.617753e-09
 ## 2 2.286902 1.021687e-05
 ## 3 1.871102 7.561990e-04
 ## 4 1.663202 1.010135e-03
 ## 5 3.534304 1.956391e-03
 ##                                                                                                                                                                                                                                                                                                                                                                    Genes
 ## 1 ENSMUSG00000021408, ENSMUSG00000039005, ENSMUSG00000042349, ENSMUSG00000044583, ENSMUSG00000018930, ENSMUSG00000032508, ENSMUSG00000020115, ENSMUSG00000024401, ENSMUSG00000024235, ENSMUSG00000039936, ENSMUSG00000027995, ENSMUSG00000034855, ENSMUSG00000045322, ENSMUSG00000031639, ENSMUSG00000051439, ENSMUSG00000032041, ENSMUSG00000026029, ENSMUSG00000044827
 ## 2                                                                                                                                             ENSMUSG00000039193, ENSMUSG00000078945, ENSMUSG00000058427, ENSMUSG00000025860, ENSMUSG00000024401, ENSMUSG00000022534, ENSMUSG00000038058, ENSMUSG00000026029, ENSMUSG00000071203, ENSMUSG00000032691, ENSMUSG00000025888
 ## 3                                                                                                                                                                                     ENSMUSG00000039285, ENSMUSG00000034855, ENSMUSG00000042349, ENSMUSG00000026896, ENSMUSG00000000275, ENSMUSG00000021408, ENSMUSG00000024401, ENSMUSG00000020115, ENSMUSG00000026029
 ## 4                                                                                                                                                                                                         ENSMUSG00000034855, ENSMUSG00000027951, ENSMUSG00000042349, ENSMUSG00000021408, ENSMUSG00000018930, ENSMUSG00000020115, ENSMUSG00000037860, ENSMUSG00000025888
 ## 5                     ENSMUSG00000028362, ENSMUSG00000018930, ENSMUSG00000024401, ENSMUSG00000028864, ENSMUSG00000027947, ENSMUSG00000026180, ENSMUSG00000057722, ENSMUSG00000034855, ENSMUSG00000028859, ENSMUSG00000023951, ENSMUSG00000032440, ENSMUSG00000058427, ENSMUSG00000024621, ENSMUSG00000062960, ENSMUSG00000049103, ENSMUSG00000039304, ENSMUSG00000079227
 ##   List.Total Pop.Hits Pop.Total Fold.Enrichment   Bonferroni    Benjamini
 ## 1        169       99      5738        6.173211 3.376900e-07 3.376900e-07
 ## 2        169       62      5738        6.023860 1.317114e-03 6.587742e-04
 ## 3        169       68      5738        4.493735 9.297618e-02 3.200548e-02
 ## 4        169       55      5738        4.938569 1.222322e-01 3.206787e-02
 ## 5        169      244      5738        2.365554 2.232383e-01 4.926921e-02
 ##            FDR
 ## 1 3.044309e-06
 ## 2 1.188103e-02
 ## 3 8.758938e-01
 ## 4 1.168447e+00
 ## 5 2.251671e+00

Gene Ontology Analysis on DEC up-regulated not in E2 genes

Here starts the automatically generated code

You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/genes_in_DEC_UP_not_in_E2.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list6 , db.selected: GOTERM_MF_ALL , analysis.type: GO , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis You chose the following file: /RNASeqGUI_Projects/BMDC_analysis/Results/genes_in_DEC_UP_not_in_E2.txt , gene.list.position: 1 , gene.identifier: ENSEMBL_GENE_ID , list.type: Gene , list.name: list6 , db.selected: GOTERM_BP_ALL , analysis.type: GO , specie:

, filter: TRUE , filter.column: Benjamini , filter.threshold: 0.05 , Project: BMDC_analysis

This R code has been run:

 print(getwd())
 ## [1] "/RNASeqGUI_Projects/BMDC_analysis/Logs"
 db <- InitDb(db.name='david_genes_in_DEC_UP_not_in_E2.txt_DAVID_ALL_GO_GOTERM_MF_ALL_GOTERM_BP_ALL_db' , db.path='cache')
 #results.data.frame <- DavidAnalysis(file.name, gene.list.position, gene.identifier, list.type, list.name, out.file.name, db.selected, analysis.type, Project)
 results.data.frame <- LoadCachedObject(db, 'resultsfinal_key')
 print('First lines of the results:')
 ## [1] "First lines of the results:"
 print(head(results.data.frame))
 ##        Category                                   Term Count        X.
 ## 1 GOTERM_BP_ALL       GO:0002376~immune system process    69 14.345114
 ## 2 GOTERM_BP_ALL             GO:0006955~immune response    49 10.187110
 ## 3 GOTERM_BP_ALL       GO:0006954~inflammatory response    29  6.029106
 ## 4 GOTERM_BP_ALL  GO:0051707~response to other organism    29  6.029106
 ## 5 GOTERM_BP_ALL            GO:0006952~defense response    39  8.108108
 ## 6 GOTERM_BP_ALL GO:0009607~response to biotic stimulus    31  6.444906
 ##         PValue
 ## 1 2.860003e-21
 ## 2 4.784466e-18
 ## 3 5.383090e-13
 ## 4 1.959847e-12
 ## 5 6.822472e-12
 ## 6 7.103107e-11
 ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Genes
 ## 1 ENSMUSG00000001166, ENSMUSG00000038518, ENSMUSG00000040274, ENSMUSG00000039361, ENSMUSG00000030830, ENSMUSG00000002699, ENSMUSG00000032508, ENSMUSG00000021109, ENSMUSG00000021624, ENSMUSG00000032440, ENSMUSG00000041827, ENSMUSG00000029379, ENSMUSG00000032041, ENSMUSG00000079227, ENSMUSG00000032737, ENSMUSG00000027111, ENSMUSG00000052593, ENSMUSG00000023341, ENSMUSG00000029468, ENSMUSG00000029605, ENSMUSG00000079362, ENSMUSG00000020115, ENSMUSG00000008496, ENSMUSG00000079363, ENSMUSG00000036381, ENSMUSG00000031596, ENSMUSG00000034855, ENSMUSG00000031103, ENSMUSG00000062960, ENSMUSG00000058818, ENSMUSG00000021583, ENSMUSG00000038527, ENSMUSG00000026029, ENSMUSG00000037321, ENSMUSG00000024164, ENSMUSG00000034330, ENSMUSG00000044827, ENSMUSG00000044583, ENSMUSG00000028362, ENSMUSG00000018930, ENSMUSG00000028268, ENSMUSG00000022148, ENSMUSG00000024401, ENSMUSG00000029298, ENSMUSG00000036986, ENSMUSG00000018654, ENSMUSG00000023951, ENSMUSG00000070034, ENSMUSG00000051439, ENSMUSG00000049103, ENSMUSG00000027639, ENSMUSG00000042228, ENSMUSG00000047798, ENSMUSG00000040253, ENSMUSG00000039005, ENSMUSG00000053835, ENSMUSG00000039936, ENSMUSG00000027995, ENSMUSG00000030142, ENSMUSG00000032691, ENSMUSG00000045322, ENSMUSG00000028859, ENSMUSG00000026896, ENSMUSG00000058427, ENSMUSG00000031639, ENSMUSG00000078921, ENSMUSG00000006818, ENSMUSG00000039304, ENSMUSG00000040264
 ## 2                                                                                                                                                                                                                                                                                                                                                                                                                 ENSMUSG00000001166, ENSMUSG00000032508, ENSMUSG00000021624, ENSMUSG00000041827, ENSMUSG00000029379, ENSMUSG00000032041, ENSMUSG00000079227, ENSMUSG00000032737, ENSMUSG00000052593, ENSMUSG00000023341, ENSMUSG00000029605, ENSMUSG00000020115, ENSMUSG00000079362, ENSMUSG00000008496, ENSMUSG00000079363, ENSMUSG00000036381, ENSMUSG00000034855, ENSMUSG00000021583, ENSMUSG00000058818, ENSMUSG00000038527, ENSMUSG00000034330, ENSMUSG00000024164, ENSMUSG00000037321, ENSMUSG00000044827, ENSMUSG00000044583, ENSMUSG00000018930, ENSMUSG00000028362, ENSMUSG00000028268, ENSMUSG00000024401, ENSMUSG00000029298, ENSMUSG00000023951, ENSMUSG00000070034, ENSMUSG00000049103, ENSMUSG00000051439, ENSMUSG00000027639, ENSMUSG00000047798, ENSMUSG00000040253, ENSMUSG00000039005, ENSMUSG00000053835, ENSMUSG00000027995, ENSMUSG00000030142, ENSMUSG00000032691, ENSMUSG00000045322, ENSMUSG00000026896, ENSMUSG00000031639, ENSMUSG00000058427, ENSMUSG00000078921, ENSMUSG00000039304, ENSMUSG00000040264
 ## 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSMUSG00000035356, ENSMUSG00000044583, ENSMUSG00000018930, ENSMUSG00000032508, ENSMUSG00000024401, ENSMUSG00000021109, ENSMUSG00000042286, ENSMUSG00000021624, ENSMUSG00000051439, ENSMUSG00000049103, ENSMUSG00000026193, ENSMUSG00000029379, ENSMUSG00000032041, ENSMUSG00000079227, ENSMUSG00000039005, ENSMUSG00000029468, ENSMUSG00000022534, ENSMUSG00000027995, ENSMUSG00000032691, ENSMUSG00000031596, ENSMUSG00000034855, ENSMUSG00000045322, ENSMUSG00000039193, ENSMUSG00000040026, ENSMUSG00000058427, ENSMUSG00000031639, ENSMUSG00000038527, ENSMUSG00000024164, ENSMUSG00000044827
 ## 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 ENSMUSG00000044583, ENSMUSG00000020227, ENSMUSG00000032508, ENSMUSG00000024401, ENSMUSG00000042286, ENSMUSG00000070034, ENSMUSG00000027639, ENSMUSG00000051439, ENSMUSG00000032041, ENSMUSG00000079227, ENSMUSG00000039005, ENSMUSG00000023341, ENSMUSG00000029468, ENSMUSG00000028874, ENSMUSG00000029605, ENSMUSG00000024079, ENSMUSG00000020115, ENSMUSG00000027995, ENSMUSG00000032691, ENSMUSG00000045322, ENSMUSG00000026896, ENSMUSG00000039193, ENSMUSG00000003283, ENSMUSG00000031639, ENSMUSG00000078921, ENSMUSG00000038058, ENSMUSG00000029826, ENSMUSG00000034330, ENSMUSG00000044827
 ## 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         ENSMUSG00000035356, ENSMUSG00000044583, ENSMUSG00000018930, ENSMUSG00000024401, ENSMUSG00000032508, ENSMUSG00000021109, ENSMUSG00000042286, ENSMUSG00000021624, ENSMUSG00000070034, ENSMUSG00000027639, ENSMUSG00000051439, ENSMUSG00000049103, ENSMUSG00000019979, ENSMUSG00000029379, ENSMUSG00000026193, ENSMUSG00000032041, ENSMUSG00000079227, ENSMUSG00000039005, ENSMUSG00000023341, ENSMUSG00000029468, ENSMUSG00000028874, ENSMUSG00000022534, ENSMUSG00000020115, ENSMUSG00000027995, ENSMUSG00000031596, ENSMUSG00000032691, ENSMUSG00000034855, ENSMUSG00000045322, ENSMUSG00000039193, ENSMUSG00000026896, ENSMUSG00000040026, ENSMUSG00000003283, ENSMUSG00000058427, ENSMUSG00000031639, ENSMUSG00000038058, ENSMUSG00000038527, ENSMUSG00000037321, ENSMUSG00000024164, ENSMUSG00000044827
 ## 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         ENSMUSG00000044583, ENSMUSG00000020227, ENSMUSG00000070348, ENSMUSG00000032508, ENSMUSG00000024401, ENSMUSG00000059108, ENSMUSG00000042286, ENSMUSG00000070034, ENSMUSG00000027639, ENSMUSG00000051439, ENSMUSG00000032041, ENSMUSG00000079227, ENSMUSG00000039005, ENSMUSG00000023341, ENSMUSG00000029468, ENSMUSG00000028874, ENSMUSG00000029605, ENSMUSG00000024079, ENSMUSG00000020115, ENSMUSG00000027995, ENSMUSG00000032691, ENSMUSG00000045322, ENSMUSG00000026896, ENSMUSG00000039193, ENSMUSG00000003283, ENSMUSG00000031639, ENSMUSG00000078921, ENSMUSG00000038058, ENSMUSG00000029826, ENSMUSG00000034330, ENSMUSG00000044827
 ##   List.Total Pop.Hits Pop.Total Fold.Enrichment   Bonferroni    Benjamini
 ## 1        336      791     14219        3.691496 5.702845e-18 5.702845e-18
 ## 2        336      471     14219        4.402557 9.540225e-15 4.770113e-15
 ## 3        336      225     14219        5.454378 1.073464e-09 3.578214e-10
 ## 4        336      237     14219        5.178207 3.907994e-09 9.769985e-10
 ## 5        336      448     14219        3.683972 1.360393e-08 2.720786e-09
 ## 6        336      314     14219        4.177936 1.416359e-07 2.360599e-08
 ##            FDR
 ## 1 4.915537e-18
 ## 2 8.223146e-15
 ## 3 9.252710e-10
 ## 4 3.368472e-09
 ## 5 1.172583e-08
 ## 6 1.220824e-07

Heat maps

After functional analysis results inspection, heatmaps for most relevant pathways has been produced using the suited Gage functionality.

Cytokine-Cytokine interaction pathway on E2 results

Here starts the automatically generated code

You chose the following Pathway/GO file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_results_NOISeq_Gage_Analysis/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_results_NOISeq_Pathway_result.txt ,geneSet1: TRUE ,geneSet2: FALSE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE , num_of_path: 4 ,countFile: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt

This R code has been run:

 geneSet1 <- TRUE
 geneSet2 <-  FALSE
 db <- InitDb(db.name = paste(geneSet1,geneSet2,'kegggoheatmap_db',sep='_'), db.path=file.path('cache'))
 res <- LoadCachedObject(db, 'res_key')
 Project <- LoadCachedObject(db, 'project_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dme <- LoadCachedObject(db, 'dme_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 num_of_path <- LoadCachedObject(db, 'num_of_path_key')
 countFile <- LoadCachedObject(db, 'countFile_key')
 control <- LoadCachedObject(db, 'control_key')
 treated <- LoadCachedObject(db, 'treated_key')
 res=read.table(res, row.names=1, header=TRUE, sep='')
 counts=read.table(countFile, row.names=1, header=TRUE)
 print('Pathway list selected:')
 ## [1] "Pathway list selected:"
 print(head(res,10))
 ##                                                    p.geomean stat.mean
 ## mmu05160 Hepatitis C                            2.981657e-07  4.992401
 ## mmu05164 Influenza A                            4.379829e-07  4.917634
 ## mmu05168 Herpes simplex infection               4.526240e-07  4.911191
 ## mmu05162 Measles                                1.773026e-05  4.135226
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03  3.011642
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03  2.624313
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02  1.891229
 ## mmu05161 Hepatitis B                            3.748099e-02  1.780697
 ## mmu04668 TNF signaling pathway                  4.778172e-02  1.666753
 ## mmu05203 Viral carcinogenesis                   7.479971e-02  1.440948
 ##                                                        p.val        q.val
 ## mmu05160 Hepatitis C                            2.981657e-07 1.025948e-05
 ## mmu05164 Influenza A                            4.379829e-07 1.025948e-05
 ## mmu05168 Herpes simplex infection               4.526240e-07 1.025948e-05
 ## mmu05162 Measles                                1.773026e-05 3.014144e-04
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03 1.766902e-02
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03 4.920026e-02
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02 2.845983e-01
 ## mmu05161 Hepatitis B                            3.748099e-02 3.185884e-01
 ## mmu04668 TNF signaling pathway                  4.778172e-02 3.610174e-01
 ## mmu05203 Viral carcinogenesis                   7.479971e-02 5.086380e-01
 ##                                                 set.size         exp1
 ## mmu05160 Hepatitis C                                  23 2.981657e-07
 ## mmu05164 Influenza A                                  37 4.379829e-07
 ## mmu05168 Herpes simplex infection                     40 4.526240e-07
 ## mmu05162 Measles                                      29 1.773026e-05
 ## mmu04622 RIG-I-like receptor signaling pathway        13 1.299193e-03
 ## mmu04623 Cytosolic DNA-sensing pathway                13 4.341199e-03
 ## mmu04060 Cytokine-cytokine receptor interaction       23 2.929689e-02
 ## mmu05161 Hepatitis B                                  22 3.748099e-02
 ## mmu04668 TNF signaling pathway                        19 4.778172e-02
 ## mmu05203 Viral carcinogenesis                         21 7.479971e-02
 print('Count file selected:')
 ## [1] "Count file selected:"
 print(head(counts))
 ##                        DEC_1     DEC_2      E2_1      E2_2     UNTR_1
 ## ENSMUSG00000063889 1386.2100 1296.7846 1840.5502 1797.3179 1927.78005
 ## ENSMUSG00000024231 1618.9080 1662.3452 1738.6008 1696.5656 1716.66789
 ## ENSMUSG00000024232  144.2284  159.8542  177.8831  178.9201  214.62411
 ## ENSMUSG00000073647  139.4464  131.0292  126.9828  108.9937   78.76874
 ## ENSMUSG00000024235 1926.7159 1819.7991 1337.5621 1318.2114 1214.82938
 ## ENSMUSG00000024234  913.4006  860.6585  927.9543  923.3442  924.83273
 ##                        UNTR_2
 ## ENSMUSG00000063889 1927.03014
 ## ENSMUSG00000024231 1643.01010
 ## ENSMUSG00000024232  230.77168
 ## ENSMUSG00000073647   67.22854
 ## ENSMUSG00000024235 1255.74910
 ## ENSMUSG00000024234  960.87190
 counts <- LoadCachedObject(db, 'counts_key')
 if (geneSet1 == TRUE){ #kegg
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 if(dre==TRUE){ks=kegg.gsets(species = 'dre', id.type = 'entrez')}
 kegg.gs=ks$kg.sets
 gs = rownames(res)[num_of_path]
 print('Path selected:')
 print(gs)
 outname = gsub(' |:|/', '_', substr(gs, 0, 100))
 if(hsa==TRUE){results1 <- getBM(attributes = c('entrezgene','hgnc_symbol'), mart=ensembl)}
 if(mmu==TRUE){results1 <- getBM(attributes = c('entrezgene','mgi_symbol') , mart=ensembl)}
 if(dme==TRUE){results1 <- getBM(attributes = c('entrezgene','bdgp_symbol'), mart=ensembl)}
 if(dre==TRUE){results1 <- getBM(attributes = c('entrezgene','zfin_symbol'), mart=ensembl)}
 kegg.gs[[gs]] <- mol.sum(mol.data = kegg.gs[[gs]], id.map = results1, sum.method = 'mean')
 #gage::geneData(genes=rownames(kegg.gs[[gs]]),exprs=log(counts+1),ref=control,samp=treated,
 #outname=outname,txt=T,heatmap=T,limit=3,scatterplot=T)
 }
 ## [1] "Path selected:"
 ## [1] "mmu04621 NOD-like receptor signaling pathway"
 if (geneSet2 == TRUE){ #GO
 data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets
 gs = rownames(res)[num_of_path]
 outname = gsub(' |:|/', '_', substr(gs, 12, 100))
 if(hsa==TRUE){results1 <- getBM(attributes = c('entrezgene','hgnc_symbol'), mart=ensembl)}
 if(mmu==TRUE){results1 <- getBM(attributes = c('entrezgene','mgi_symbol') , mart=ensembl)}
 if(dme==TRUE){results1 <- getBM(attributes = c('entrezgene','bdgp_symbol'), mart=ensembl)}
 if(dre==TRUE){results1 <- getBM(attributes = c('entrezgene','zfin_symbol'), mart=ensembl)}
 go.gs[[gs]] <- mol.sum(mol.data = go.gs[[gs]], id.map = results1, sum.method = 'mean')
 #gage::geneData(genes=rownames(go.gs[[gs]]),exprs=log(counts+1),ref=control,samp=treated,
 #outname=outname,txt=T,heatmap=T,limit=3,scatterplot=T)
 }

ECM receptor interaction pathway on E2 results

Here starts the automatically generated code

You chose the following Pathway/GO file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_results_NOISeq_Gage_Analysis/Proportion_counts_FeatureCounts.txt_UQUA.txt_E2_vs_UNTR.txt_results_NOISeq_Pathway_result.txt ,geneSet1: TRUE ,geneSet2: FALSE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE , num_of_path: 2 ,countFile: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt

This R code has been run:

 geneSet1 <- TRUE
 geneSet2 <-  FALSE
 db <- InitDb(db.name = paste(geneSet1,geneSet2,'kegggoheatmap_db',sep='_'), db.path=file.path('cache'))
 res <- LoadCachedObject(db, 'res_key')
 Project <- LoadCachedObject(db, 'project_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dme <- LoadCachedObject(db, 'dme_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 num_of_path <- LoadCachedObject(db, 'num_of_path_key')
 countFile <- LoadCachedObject(db, 'countFile_key')
 control <- LoadCachedObject(db, 'control_key')
 treated <- LoadCachedObject(db, 'treated_key')
 res=read.table(res, row.names=1, header=TRUE, sep='')
 counts=read.table(countFile, row.names=1, header=TRUE)
 print('Pathway list selected:')
 ## [1] "Pathway list selected:"
 print(head(res,10))
 ##                                                    p.geomean stat.mean
 ## mmu05160 Hepatitis C                            2.981657e-07  4.992401
 ## mmu05164 Influenza A                            4.379829e-07  4.917634
 ## mmu05168 Herpes simplex infection               4.526240e-07  4.911191
 ## mmu05162 Measles                                1.773026e-05  4.135226
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03  3.011642
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03  2.624313
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02  1.891229
 ## mmu05161 Hepatitis B                            3.748099e-02  1.780697
 ## mmu04668 TNF signaling pathway                  4.778172e-02  1.666753
 ## mmu05203 Viral carcinogenesis                   7.479971e-02  1.440948
 ##                                                        p.val        q.val
 ## mmu05160 Hepatitis C                            2.981657e-07 1.025948e-05
 ## mmu05164 Influenza A                            4.379829e-07 1.025948e-05
 ## mmu05168 Herpes simplex infection               4.526240e-07 1.025948e-05
 ## mmu05162 Measles                                1.773026e-05 3.014144e-04
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03 1.766902e-02
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03 4.920026e-02
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02 2.845983e-01
 ## mmu05161 Hepatitis B                            3.748099e-02 3.185884e-01
 ## mmu04668 TNF signaling pathway                  4.778172e-02 3.610174e-01
 ## mmu05203 Viral carcinogenesis                   7.479971e-02 5.086380e-01
 ##                                                 set.size         exp1
 ## mmu05160 Hepatitis C                                  23 2.981657e-07
 ## mmu05164 Influenza A                                  37 4.379829e-07
 ## mmu05168 Herpes simplex infection                     40 4.526240e-07
 ## mmu05162 Measles                                      29 1.773026e-05
 ## mmu04622 RIG-I-like receptor signaling pathway        13 1.299193e-03
 ## mmu04623 Cytosolic DNA-sensing pathway                13 4.341199e-03
 ## mmu04060 Cytokine-cytokine receptor interaction       23 2.929689e-02
 ## mmu05161 Hepatitis B                                  22 3.748099e-02
 ## mmu04668 TNF signaling pathway                        19 4.778172e-02
 ## mmu05203 Viral carcinogenesis                         21 7.479971e-02
 print('Count file selected:')
 ## [1] "Count file selected:"
 print(head(counts))
 ##                        DEC_1     DEC_2      E2_1      E2_2     UNTR_1
 ## ENSMUSG00000063889 1386.2100 1296.7846 1840.5502 1797.3179 1927.78005
 ## ENSMUSG00000024231 1618.9080 1662.3452 1738.6008 1696.5656 1716.66789
 ## ENSMUSG00000024232  144.2284  159.8542  177.8831  178.9201  214.62411
 ## ENSMUSG00000073647  139.4464  131.0292  126.9828  108.9937   78.76874
 ## ENSMUSG00000024235 1926.7159 1819.7991 1337.5621 1318.2114 1214.82938
 ## ENSMUSG00000024234  913.4006  860.6585  927.9543  923.3442  924.83273
 ##                        UNTR_2
 ## ENSMUSG00000063889 1927.03014
 ## ENSMUSG00000024231 1643.01010
 ## ENSMUSG00000024232  230.77168
 ## ENSMUSG00000073647   67.22854
 ## ENSMUSG00000024235 1255.74910
 ## ENSMUSG00000024234  960.87190
 counts <- LoadCachedObject(db, 'counts_key')
 if (geneSet1 == TRUE){ #kegg
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 if(dre==TRUE){ks=kegg.gsets(species = 'dre', id.type = 'entrez')}
 kegg.gs=ks$kg.sets
 gs = rownames(res)[num_of_path]
 print('Path selected:')
 print(gs)
 outname = gsub(' |:|/', '_', substr(gs, 0, 100))
 if(hsa==TRUE){results1 <- getBM(attributes = c('entrezgene','hgnc_symbol'), mart=ensembl)}
 if(mmu==TRUE){results1 <- getBM(attributes = c('entrezgene','mgi_symbol') , mart=ensembl)}
 if(dme==TRUE){results1 <- getBM(attributes = c('entrezgene','bdgp_symbol'), mart=ensembl)}
 if(dre==TRUE){results1 <- getBM(attributes = c('entrezgene','zfin_symbol'), mart=ensembl)}
 kegg.gs[[gs]] <- mol.sum(mol.data = kegg.gs[[gs]], id.map = results1, sum.method = 'mean')
 #gage::geneData(genes=rownames(kegg.gs[[gs]]),exprs=log(counts+1),ref=control,samp=treated,
 #outname=outname,txt=T,heatmap=T,limit=3,scatterplot=T)
 }
 ## [1] "Path selected:"
 ## [1] "mmu04621 NOD-like receptor signaling pathway"
 if (geneSet2 == TRUE){ #GO
 data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets
 gs = rownames(res)[num_of_path]
 outname = gsub(' |:|/', '_', substr(gs, 12, 100))
 if(hsa==TRUE){results1 <- getBM(attributes = c('entrezgene','hgnc_symbol'), mart=ensembl)}
 if(mmu==TRUE){results1 <- getBM(attributes = c('entrezgene','mgi_symbol') , mart=ensembl)}
 if(dme==TRUE){results1 <- getBM(attributes = c('entrezgene','bdgp_symbol'), mart=ensembl)}
 if(dre==TRUE){results1 <- getBM(attributes = c('entrezgene','zfin_symbol'), mart=ensembl)}
 go.gs[[gs]] <- mol.sum(mol.data = go.gs[[gs]], id.map = results1, sum.method = 'mean')
 #gage::geneData(genes=rownames(go.gs[[gs]]),exprs=log(counts+1),ref=control,samp=treated,
 #outname=outname,txt=T,heatmap=T,limit=3,scatterplot=T)
 }

RIG-I pathway on DEC results

Here starts the automatically generated code

You chose the following Pathway/GO file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq_Gage_Analysis/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq_Pathway_result.txt ,geneSet1: TRUE ,geneSet2: FALSE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE , num_of_path: 5 ,countFile: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt

This R code has been run:

 geneSet1 <- TRUE
 geneSet2 <-  FALSE
 db <- InitDb(db.name = paste(geneSet1,geneSet2,'kegggoheatmap_db',sep='_'), db.path=file.path('cache'))
 res <- LoadCachedObject(db, 'res_key')
 Project <- LoadCachedObject(db, 'project_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dme <- LoadCachedObject(db, 'dme_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 num_of_path <- LoadCachedObject(db, 'num_of_path_key')
 countFile <- LoadCachedObject(db, 'countFile_key')
 control <- LoadCachedObject(db, 'control_key')
 treated <- LoadCachedObject(db, 'treated_key')
 res=read.table(res, row.names=1, header=TRUE, sep='')
 counts=read.table(countFile, row.names=1, header=TRUE)
 print('Pathway list selected:')
 ## [1] "Pathway list selected:"
 print(head(res,10))
 ##                                                    p.geomean stat.mean
 ## mmu05160 Hepatitis C                            2.981657e-07  4.992401
 ## mmu05164 Influenza A                            4.379829e-07  4.917634
 ## mmu05168 Herpes simplex infection               4.526240e-07  4.911191
 ## mmu05162 Measles                                1.773026e-05  4.135226
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03  3.011642
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03  2.624313
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02  1.891229
 ## mmu05161 Hepatitis B                            3.748099e-02  1.780697
 ## mmu04668 TNF signaling pathway                  4.778172e-02  1.666753
 ## mmu05203 Viral carcinogenesis                   7.479971e-02  1.440948
 ##                                                        p.val        q.val
 ## mmu05160 Hepatitis C                            2.981657e-07 1.025948e-05
 ## mmu05164 Influenza A                            4.379829e-07 1.025948e-05
 ## mmu05168 Herpes simplex infection               4.526240e-07 1.025948e-05
 ## mmu05162 Measles                                1.773026e-05 3.014144e-04
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03 1.766902e-02
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03 4.920026e-02
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02 2.845983e-01
 ## mmu05161 Hepatitis B                            3.748099e-02 3.185884e-01
 ## mmu04668 TNF signaling pathway                  4.778172e-02 3.610174e-01
 ## mmu05203 Viral carcinogenesis                   7.479971e-02 5.086380e-01
 ##                                                 set.size         exp1
 ## mmu05160 Hepatitis C                                  23 2.981657e-07
 ## mmu05164 Influenza A                                  37 4.379829e-07
 ## mmu05168 Herpes simplex infection                     40 4.526240e-07
 ## mmu05162 Measles                                      29 1.773026e-05
 ## mmu04622 RIG-I-like receptor signaling pathway        13 1.299193e-03
 ## mmu04623 Cytosolic DNA-sensing pathway                13 4.341199e-03
 ## mmu04060 Cytokine-cytokine receptor interaction       23 2.929689e-02
 ## mmu05161 Hepatitis B                                  22 3.748099e-02
 ## mmu04668 TNF signaling pathway                        19 4.778172e-02
 ## mmu05203 Viral carcinogenesis                         21 7.479971e-02
 print('Count file selected:')
 ## [1] "Count file selected:"
 print(head(counts))
 ##                        DEC_1     DEC_2      E2_1      E2_2     UNTR_1
 ## ENSMUSG00000063889 1386.2100 1296.7846 1840.5502 1797.3179 1927.78005
 ## ENSMUSG00000024231 1618.9080 1662.3452 1738.6008 1696.5656 1716.66789
 ## ENSMUSG00000024232  144.2284  159.8542  177.8831  178.9201  214.62411
 ## ENSMUSG00000073647  139.4464  131.0292  126.9828  108.9937   78.76874
 ## ENSMUSG00000024235 1926.7159 1819.7991 1337.5621 1318.2114 1214.82938
 ## ENSMUSG00000024234  913.4006  860.6585  927.9543  923.3442  924.83273
 ##                        UNTR_2
 ## ENSMUSG00000063889 1927.03014
 ## ENSMUSG00000024231 1643.01010
 ## ENSMUSG00000024232  230.77168
 ## ENSMUSG00000073647   67.22854
 ## ENSMUSG00000024235 1255.74910
 ## ENSMUSG00000024234  960.87190
 counts <- LoadCachedObject(db, 'counts_key')
 if (geneSet1 == TRUE){ #kegg
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 if(dre==TRUE){ks=kegg.gsets(species = 'dre', id.type = 'entrez')}
 kegg.gs=ks$kg.sets
 gs = rownames(res)[num_of_path]
 print('Path selected:')
 print(gs)
 outname = gsub(' |:|/', '_', substr(gs, 0, 100))
 if(hsa==TRUE){results1 <- getBM(attributes = c('entrezgene','hgnc_symbol'), mart=ensembl)}
 if(mmu==TRUE){results1 <- getBM(attributes = c('entrezgene','mgi_symbol') , mart=ensembl)}
 if(dme==TRUE){results1 <- getBM(attributes = c('entrezgene','bdgp_symbol'), mart=ensembl)}
 if(dre==TRUE){results1 <- getBM(attributes = c('entrezgene','zfin_symbol'), mart=ensembl)}
 kegg.gs[[gs]] <- mol.sum(mol.data = kegg.gs[[gs]], id.map = results1, sum.method = 'mean')
 #gage::geneData(genes=rownames(kegg.gs[[gs]]),exprs=log(counts+1),ref=control,samp=treated,
 #outname=outname,txt=T,heatmap=T,limit=3,scatterplot=T)
 }
 ## [1] "Path selected:"
 ## [1] "mmu04621 NOD-like receptor signaling pathway"
 if (geneSet2 == TRUE){ #GO
 data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets
 gs = rownames(res)[num_of_path]
 outname = gsub(' |:|/', '_', substr(gs, 12, 100))
 if(hsa==TRUE){results1 <- getBM(attributes = c('entrezgene','hgnc_symbol'), mart=ensembl)}
 if(mmu==TRUE){results1 <- getBM(attributes = c('entrezgene','mgi_symbol') , mart=ensembl)}
 if(dme==TRUE){results1 <- getBM(attributes = c('entrezgene','bdgp_symbol'), mart=ensembl)}
 if(dre==TRUE){results1 <- getBM(attributes = c('entrezgene','zfin_symbol'), mart=ensembl)}
 go.gs[[gs]] <- mol.sum(mol.data = go.gs[[gs]], id.map = results1, sum.method = 'mean')
 #gage::geneData(genes=rownames(go.gs[[gs]]),exprs=log(counts+1),ref=control,samp=treated,
 #outname=outname,txt=T,heatmap=T,limit=3,scatterplot=T)
 }

Toll-like receptor pathway on DEC results

Here starts the automatically generated code

You chose the following Pathway/GO file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq_Gage_Analysis/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq_Pathway_result.txt ,geneSet1: TRUE ,geneSet2: FALSE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE , num_of_path: 12 ,countFile: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt

This R code has been run:

 geneSet1 <- TRUE
 geneSet2 <-  FALSE
 db <- InitDb(db.name = paste(geneSet1,geneSet2,'kegggoheatmap_db',sep='_'), db.path=file.path('cache'))
 res <- LoadCachedObject(db, 'res_key')
 Project <- LoadCachedObject(db, 'project_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dme <- LoadCachedObject(db, 'dme_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 num_of_path <- LoadCachedObject(db, 'num_of_path_key')
 countFile <- LoadCachedObject(db, 'countFile_key')
 control <- LoadCachedObject(db, 'control_key')
 treated <- LoadCachedObject(db, 'treated_key')
 res=read.table(res, row.names=1, header=TRUE, sep='')
 counts=read.table(countFile, row.names=1, header=TRUE)
 print('Pathway list selected:')
 ## [1] "Pathway list selected:"
 print(head(res,10))
 ##                                                    p.geomean stat.mean
 ## mmu05160 Hepatitis C                            2.981657e-07  4.992401
 ## mmu05164 Influenza A                            4.379829e-07  4.917634
 ## mmu05168 Herpes simplex infection               4.526240e-07  4.911191
 ## mmu05162 Measles                                1.773026e-05  4.135226
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03  3.011642
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03  2.624313
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02  1.891229
 ## mmu05161 Hepatitis B                            3.748099e-02  1.780697
 ## mmu04668 TNF signaling pathway                  4.778172e-02  1.666753
 ## mmu05203 Viral carcinogenesis                   7.479971e-02  1.440948
 ##                                                        p.val        q.val
 ## mmu05160 Hepatitis C                            2.981657e-07 1.025948e-05
 ## mmu05164 Influenza A                            4.379829e-07 1.025948e-05
 ## mmu05168 Herpes simplex infection               4.526240e-07 1.025948e-05
 ## mmu05162 Measles                                1.773026e-05 3.014144e-04
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03 1.766902e-02
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03 4.920026e-02
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02 2.845983e-01
 ## mmu05161 Hepatitis B                            3.748099e-02 3.185884e-01
 ## mmu04668 TNF signaling pathway                  4.778172e-02 3.610174e-01
 ## mmu05203 Viral carcinogenesis                   7.479971e-02 5.086380e-01
 ##                                                 set.size         exp1
 ## mmu05160 Hepatitis C                                  23 2.981657e-07
 ## mmu05164 Influenza A                                  37 4.379829e-07
 ## mmu05168 Herpes simplex infection                     40 4.526240e-07
 ## mmu05162 Measles                                      29 1.773026e-05
 ## mmu04622 RIG-I-like receptor signaling pathway        13 1.299193e-03
 ## mmu04623 Cytosolic DNA-sensing pathway                13 4.341199e-03
 ## mmu04060 Cytokine-cytokine receptor interaction       23 2.929689e-02
 ## mmu05161 Hepatitis B                                  22 3.748099e-02
 ## mmu04668 TNF signaling pathway                        19 4.778172e-02
 ## mmu05203 Viral carcinogenesis                         21 7.479971e-02
 print('Count file selected:')
 ## [1] "Count file selected:"
 print(head(counts))
 ##                        DEC_1     DEC_2      E2_1      E2_2     UNTR_1
 ## ENSMUSG00000063889 1386.2100 1296.7846 1840.5502 1797.3179 1927.78005
 ## ENSMUSG00000024231 1618.9080 1662.3452 1738.6008 1696.5656 1716.66789
 ## ENSMUSG00000024232  144.2284  159.8542  177.8831  178.9201  214.62411
 ## ENSMUSG00000073647  139.4464  131.0292  126.9828  108.9937   78.76874
 ## ENSMUSG00000024235 1926.7159 1819.7991 1337.5621 1318.2114 1214.82938
 ## ENSMUSG00000024234  913.4006  860.6585  927.9543  923.3442  924.83273
 ##                        UNTR_2
 ## ENSMUSG00000063889 1927.03014
 ## ENSMUSG00000024231 1643.01010
 ## ENSMUSG00000024232  230.77168
 ## ENSMUSG00000073647   67.22854
 ## ENSMUSG00000024235 1255.74910
 ## ENSMUSG00000024234  960.87190
 counts <- LoadCachedObject(db, 'counts_key')
 if (geneSet1 == TRUE){ #kegg
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 if(dre==TRUE){ks=kegg.gsets(species = 'dre', id.type = 'entrez')}
 kegg.gs=ks$kg.sets
 gs = rownames(res)[num_of_path]
 print('Path selected:')
 print(gs)
 outname = gsub(' |:|/', '_', substr(gs, 0, 100))
 if(hsa==TRUE){results1 <- getBM(attributes = c('entrezgene','hgnc_symbol'), mart=ensembl)}
 if(mmu==TRUE){results1 <- getBM(attributes = c('entrezgene','mgi_symbol') , mart=ensembl)}
 if(dme==TRUE){results1 <- getBM(attributes = c('entrezgene','bdgp_symbol'), mart=ensembl)}
 if(dre==TRUE){results1 <- getBM(attributes = c('entrezgene','zfin_symbol'), mart=ensembl)}
 kegg.gs[[gs]] <- mol.sum(mol.data = kegg.gs[[gs]], id.map = results1, sum.method = 'mean')
 #gage::geneData(genes=rownames(kegg.gs[[gs]]),exprs=log(counts+1),ref=control,samp=treated,
 #outname=outname,txt=T,heatmap=T,limit=3,scatterplot=T)
 }
 ## [1] "Path selected:"
 ## [1] "mmu04621 NOD-like receptor signaling pathway"
 if (geneSet2 == TRUE){ #GO
 data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets
 gs = rownames(res)[num_of_path]
 outname = gsub(' |:|/', '_', substr(gs, 12, 100))
 if(hsa==TRUE){results1 <- getBM(attributes = c('entrezgene','hgnc_symbol'), mart=ensembl)}
 if(mmu==TRUE){results1 <- getBM(attributes = c('entrezgene','mgi_symbol') , mart=ensembl)}
 if(dme==TRUE){results1 <- getBM(attributes = c('entrezgene','bdgp_symbol'), mart=ensembl)}
 if(dre==TRUE){results1 <- getBM(attributes = c('entrezgene','zfin_symbol'), mart=ensembl)}
 go.gs[[gs]] <- mol.sum(mol.data = go.gs[[gs]], id.map = results1, sum.method = 'mean')
 #gage::geneData(genes=rownames(go.gs[[gs]]),exprs=log(counts+1),ref=control,samp=treated,
 #outname=outname,txt=T,heatmap=T,limit=3,scatterplot=T)
 }

NOD pathway on DEC results

Here starts the automatically generated code

You chose the following Pathway/GO file: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq_Gage_Analysis/Proportion_counts_FeatureCounts.txt_UQUA.txt_DEC_vs_UNTR.txt_prob=0.95_DE_UP_genes_NOISeq_Pathway_result.txt ,geneSet1: TRUE ,geneSet2: FALSE ,Project: BMDC_analysis ,hsa: FALSE ,mmu: TRUE ,dme: FALSE ,dre: FALSE ,conversion: TRUE , num_of_path: 22 ,countFile: /RNASeqGUI_Projects/BMDC_analysis/Results/Proportion_counts_FeatureCounts.txt_UQUA.txt

This R code has been run:

 geneSet1 <- TRUE
 geneSet2 <-  FALSE
 db <- InitDb(db.name = paste(geneSet1,geneSet2,'kegggoheatmap_db',sep='_'), db.path=file.path('cache'))
 res <- LoadCachedObject(db, 'res_key')
 Project <- LoadCachedObject(db, 'project_key')
 hsa <- LoadCachedObject(db, 'hsa_key')
 mmu <- LoadCachedObject(db, 'mmu_key')
 dme <- LoadCachedObject(db, 'dme_key')
 dre <- LoadCachedObject(db, 'dre_key')
 conversion <- LoadCachedObject(db, 'conversion_key')
 num_of_path <- LoadCachedObject(db, 'num_of_path_key')
 countFile <- LoadCachedObject(db, 'countFile_key')
 control <- LoadCachedObject(db, 'control_key')
 treated <- LoadCachedObject(db, 'treated_key')
 res=read.table(res, row.names=1, header=TRUE, sep='')
 counts=read.table(countFile, row.names=1, header=TRUE)
 print('Pathway list selected:')
 ## [1] "Pathway list selected:"
 print(head(res,10))
 ##                                                    p.geomean stat.mean
 ## mmu05160 Hepatitis C                            2.981657e-07  4.992401
 ## mmu05164 Influenza A                            4.379829e-07  4.917634
 ## mmu05168 Herpes simplex infection               4.526240e-07  4.911191
 ## mmu05162 Measles                                1.773026e-05  4.135226
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03  3.011642
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03  2.624313
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02  1.891229
 ## mmu05161 Hepatitis B                            3.748099e-02  1.780697
 ## mmu04668 TNF signaling pathway                  4.778172e-02  1.666753
 ## mmu05203 Viral carcinogenesis                   7.479971e-02  1.440948
 ##                                                        p.val        q.val
 ## mmu05160 Hepatitis C                            2.981657e-07 1.025948e-05
 ## mmu05164 Influenza A                            4.379829e-07 1.025948e-05
 ## mmu05168 Herpes simplex infection               4.526240e-07 1.025948e-05
 ## mmu05162 Measles                                1.773026e-05 3.014144e-04
 ## mmu04622 RIG-I-like receptor signaling pathway  1.299193e-03 1.766902e-02
 ## mmu04623 Cytosolic DNA-sensing pathway          4.341199e-03 4.920026e-02
 ## mmu04060 Cytokine-cytokine receptor interaction 2.929689e-02 2.845983e-01
 ## mmu05161 Hepatitis B                            3.748099e-02 3.185884e-01
 ## mmu04668 TNF signaling pathway                  4.778172e-02 3.610174e-01
 ## mmu05203 Viral carcinogenesis                   7.479971e-02 5.086380e-01
 ##                                                 set.size         exp1
 ## mmu05160 Hepatitis C                                  23 2.981657e-07
 ## mmu05164 Influenza A                                  37 4.379829e-07
 ## mmu05168 Herpes simplex infection                     40 4.526240e-07
 ## mmu05162 Measles                                      29 1.773026e-05
 ## mmu04622 RIG-I-like receptor signaling pathway        13 1.299193e-03
 ## mmu04623 Cytosolic DNA-sensing pathway                13 4.341199e-03
 ## mmu04060 Cytokine-cytokine receptor interaction       23 2.929689e-02
 ## mmu05161 Hepatitis B                                  22 3.748099e-02
 ## mmu04668 TNF signaling pathway                        19 4.778172e-02
 ## mmu05203 Viral carcinogenesis                         21 7.479971e-02
 print('Count file selected:')
 ## [1] "Count file selected:"
 print(head(counts))
 ##                        DEC_1     DEC_2      E2_1      E2_2     UNTR_1
 ## ENSMUSG00000063889 1386.2100 1296.7846 1840.5502 1797.3179 1927.78005
 ## ENSMUSG00000024231 1618.9080 1662.3452 1738.6008 1696.5656 1716.66789
 ## ENSMUSG00000024232  144.2284  159.8542  177.8831  178.9201  214.62411
 ## ENSMUSG00000073647  139.4464  131.0292  126.9828  108.9937   78.76874
 ## ENSMUSG00000024235 1926.7159 1819.7991 1337.5621 1318.2114 1214.82938
 ## ENSMUSG00000024234  913.4006  860.6585  927.9543  923.3442  924.83273
 ##                        UNTR_2
 ## ENSMUSG00000063889 1927.03014
 ## ENSMUSG00000024231 1643.01010
 ## ENSMUSG00000024232  230.77168
 ## ENSMUSG00000073647   67.22854
 ## ENSMUSG00000024235 1255.74910
 ## ENSMUSG00000024234  960.87190
 counts <- LoadCachedObject(db, 'counts_key')
 if (geneSet1 == TRUE){ #kegg
 if(hsa==TRUE){ks=kegg.gsets(species = 'hsa', id.type = 'entrez')}
 if(mmu==TRUE){ks=kegg.gsets(species = 'mmu', id.type = 'entrez')}
 if(dme==TRUE){ks=kegg.gsets(species = 'dme', id.type = 'entrez')}
 if(dre==TRUE){ks=kegg.gsets(species = 'dre', id.type = 'entrez')}
 kegg.gs=ks$kg.sets
 gs = rownames(res)[num_of_path]
 print('Path selected:')
 print(gs)
 outname = gsub(' |:|/', '_', substr(gs, 0, 100))
 if(hsa==TRUE){results1 <- getBM(attributes = c('entrezgene','hgnc_symbol'), mart=ensembl)}
 if(mmu==TRUE){results1 <- getBM(attributes = c('entrezgene','mgi_symbol') , mart=ensembl)}
 if(dme==TRUE){results1 <- getBM(attributes = c('entrezgene','bdgp_symbol'), mart=ensembl)}
 if(dre==TRUE){results1 <- getBM(attributes = c('entrezgene','zfin_symbol'), mart=ensembl)}
 kegg.gs[[gs]] <- mol.sum(mol.data = kegg.gs[[gs]], id.map = results1, sum.method = 'mean')
 gage::geneData(genes=rownames(kegg.gs[[gs]]),exprs=log(counts+1),ref=control,samp=treated, outname=outname,txt=T,heatmap=T,limit=3,scatterplot=T)
 }
 ## [1] "Path selected:"
 ## [1] "mmu04621 NOD-like receptor signaling pathway"
 if (geneSet2 == TRUE){ #GO
 data(bods)
 if(hsa==TRUE){row=subset(bods, bods[,3] == 'hsa')}
 if(mmu==TRUE){row=subset(bods, bods[,3] == 'mmu')}
 if(dme==TRUE){row=subset(bods, bods[,3] == 'dme')}
 if(dre==TRUE){row=subset(bods, bods[,3] == 'dre')}
 go=go.gsets(species = row[2], id.type = 'entrez')
 go.gs=go$go.sets
 gs = rownames(res)[num_of_path]
 outname = gsub(' |:|/', '_', substr(gs, 12, 100))
 if(hsa==TRUE){results1 <- getBM(attributes = c('entrezgene','hgnc_symbol'), mart=ensembl)}
 if(mmu==TRUE){results1 <- getBM(attributes = c('entrezgene','mgi_symbol') , mart=ensembl)}
 if(dme==TRUE){results1 <- getBM(attributes = c('entrezgene','bdgp_symbol'), mart=ensembl)}
 if(dre==TRUE){results1 <- getBM(attributes = c('entrezgene','zfin_symbol'), mart=ensembl)}
 #go.gs[[gs]] <- mol.sum(mol.data = go.gs[[gs]], id.map = results1, sum.method = 'mean')
 #gage::geneData(genes=rownames(go.gs[[gs]]),exprs=log(counts+1),ref=control,samp=treated, outname=outname,txt=T,heatmap=T,limit=3,scatterplot=T)
 }