require(edgeR) rnaseqMatrix0= read.table("transcript_count_matrix.csv",header=T,sep=",") colnames(rnaseqMatrix0) # 1 2 3 4 5 6 7 # [1] "KO21" "KO22" "KOIFN1" "KOIFN2" "Ko01" "Ko02" "Ko1IL4" # 8 9 10 11 12 13 14 #[8] "Ko2IL4" "Ko61" "Ko62" "wt01" "wt01IL4" "wt02" "wt02IL4" # 15 16 17 18 19 20 #[15] "wt21" "wt22" "wt61" "wt62" "wtIFN1" "wtIFN2" #all wt VS all ko col_ordering = c(11,13,15,16,17,18,19,20,12,14,5,6,1,2,9,10,3,4,7,8) rnaseqMatrix =rnaseqMatrix0[,col_ordering];rnaseqMatrix = rnaseqMatrix[rowSums(rnaseqMatrix)>=2,];conditions = factor(c("WT","WT","WT","WT","WT","WT","WT","WT","WT","WT","KO","KO","KO","KO","KO","KO","KO","KO","KO","KO")); exp_study = DGEList(counts=rnaseqMatrix, group=conditions);exp_study = calcNormFactors(exp_study);exp_study = estimateCommonDisp(exp_study);exp_study = estimateTagwiseDisp(exp_study);et = exactTest(exp_study,pair=c("WT","KO")) tTags = topTags(et,n=NULL,adjust.method = "fdr") #r <- rownames(topTags(et)$table) r <- rownames(tTags) c=cpm(exp_study)[r, order(exp_study$samples$group)] x=cbind(as.data.frame(tTags),c) summary(de <- decideTestsDGE(et, p=0.05)) -1 285 0 73540 1 293 write.table(tTags, file="edgeRtranscripts-wtVSko3.csv") write.table(x, file="edgeRtranscriptsExpr-wtVSko3.csv") # with several points per group, the anticorrelation for all points should not be as strict (cor < -0.1 instead of cor < -0.5) # otherwise we seek transcipts that is alt-spliced in *ALL* points. #awk -f getEdgerDEresultsAndGetAltSplicedGroups2.awk edgeRtranscriptsExpr-wtVSko3.withGID.csv > edgeRtranscriptsExpr-wtVSko3.withGID.csv.alt_spliced.csv #ref transcripts #reczko@max:/data/images/proton/DKlab/orsalia/stringtie$ wc !$ #wc edgeRtranscriptsExpr-wtVSko.withGID.csv.alt_spliced.csv #21 3463 36090 edgeRtranscriptsExpr-wtVSko.withGID.csv.alt_spliced.csv # test anticorr<=-0.5 in each condition: #awk -v verb=0 -f getEdgerDEresultsAndGetAltSplicedGroupsInAtLeastOneCondition1.awk edgeRtranscriptsExpr-wtVSko.withGID.csv > edgeRtranscriptsExpr-wtVSko.withGID.csv.alt_spliced_in_GE_one_condition.csv #wc edgeRtranscriptsExpr-wtVSko.withGID.csv.alt_spliced_in_GE_one_condition.csv # 20 1200 15717 edgeRtranscriptsExpr-wtVSko.withGID.csv.alt_spliced_in_GE_one_condition.csv #transcripts_wt_vs_ko_alt_spliced_in_at_least_one_condition.xlsx col_ordering = c(11,13,5,6)#0h rnaseqMatrix =rnaseqMatrix0[,col_ordering];rnaseqMatrix = rnaseqMatrix[rowSums(rnaseqMatrix)>=2,];conditions = factor(c("WT","WT","KO","KO")); exp_study = DGEList(counts=rnaseqMatrix, group=conditions);exp_study = calcNormFactors(exp_study);exp_study = estimateCommonDisp(exp_study);exp_study = estimateTagwiseDisp(exp_study);et = exactTest(exp_study,pair=c("WT","KO")) tTags = topTags(et,n=NULL,adjust.method = "fdr") #r <- rownames(topTags(et)$table) r <- rownames(tTags) c=cpm(exp_study)[r, order(exp_study$samples$group)] x=cbind(as.data.frame(tTags),c) summary(de <- decideTestsDGE(et, p=0.05)) #detags <- rownames(exp_study)[as.logical(de)] #plotSmear(et, de.tags=detags) #abline(h = c(-1, 1), col = "blue") write.table(tTags, file="edgeRtranscripts-0h.csv") write.table(x, file="edgeRtranscriptsExpr-0h.csv") col_ordering = c(15,16,1,2)#2h rnaseqMatrix =rnaseqMatrix0[,col_ordering];rnaseqMatrix = rnaseqMatrix[rowSums(rnaseqMatrix)>=2,];conditions = factor(c("WT","WT","KO","KO")); exp_study = DGEList(counts=rnaseqMatrix, group=conditions);exp_study = calcNormFactors(exp_study);exp_study = estimateCommonDisp(exp_study);exp_study = estimateTagwiseDisp(exp_study);et = exactTest(exp_study,pair=c("WT","KO")) tTags = topTags(et,n=NULL,adjust.method = "fdr") r <- rownames(tTags) c=cpm(exp_study)[r, order(exp_study$samples$group)] x=cbind(as.data.frame(tTags),c) summary(de <- decideTestsDGE(et, p=0.05)) write.table(tTags, file="edgeRtranscripts-2h.csv") write.table(x, file="edgeRtranscriptsExpr-2h.csv") col_ordering = c(17,18,9,10)#6h rnaseqMatrix =rnaseqMatrix0[,col_ordering];rnaseqMatrix = rnaseqMatrix[rowSums(rnaseqMatrix)>=2,];conditions = factor(c("WT","WT","KO","KO")); exp_study = DGEList(counts=rnaseqMatrix, group=conditions);exp_study = calcNormFactors(exp_study);exp_study = estimateCommonDisp(exp_study);exp_study = estimateTagwiseDisp(exp_study);et = exactTest(exp_study,pair=c("WT","KO")) tTags = topTags(et,n=NULL,adjust.method = "fdr") r <- rownames(tTags) c=cpm(exp_study)[r, order(exp_study$samples$group)] x=cbind(as.data.frame(tTags),c) summary(de <- decideTestsDGE(et, p=0.05)) write.table(tTags, file="edgeRtranscripts-6h.csv") write.table(x, file="edgeRtranscriptsExpr-6h.csv") col_ordering = c(19,20,3,4)#IFN rnaseqMatrix =rnaseqMatrix0[,col_ordering];rnaseqMatrix = rnaseqMatrix[rowSums(rnaseqMatrix)>=2,];conditions = factor(c("WT","WT","KO","KO")); exp_study = DGEList(counts=rnaseqMatrix, group=conditions);exp_study = calcNormFactors(exp_study);exp_study = estimateCommonDisp(exp_study);exp_study = estimateTagwiseDisp(exp_study);et = exactTest(exp_study,pair=c("WT","KO")) tTags = topTags(et,n=NULL,adjust.method = "fdr") r <- rownames(tTags) c=cpm(exp_study)[r, order(exp_study$samples$group)] x=cbind(as.data.frame(tTags),c) summary(de <- decideTestsDGE(et, p=0.05)) write.table(tTags, file="edgeRtranscripts-IFN.csv") write.table(x, file="edgeRtranscriptsExpr-IFN.csv") col_ordering = c(12,14,7,8)#IL4 rnaseqMatrix =rnaseqMatrix0[,col_ordering];rnaseqMatrix = rnaseqMatrix[rowSums(rnaseqMatrix)>=2,];conditions = factor(c("WT","WT","KO","KO")); exp_study = DGEList(counts=rnaseqMatrix, group=conditions);exp_study = calcNormFactors(exp_study);exp_study = estimateCommonDisp(exp_study);exp_study = estimateTagwiseDisp(exp_study);et = exactTest(exp_study,pair=c("WT","KO")) tTags = topTags(et,n=NULL,adjust.method = "fdr") r <- rownames(tTags) c=cpm(exp_study)[r, order(exp_study$samples$group)] x=cbind(as.data.frame(tTags),c) summary(de <- decideTestsDGE(et, p=0.05)) write.table(tTags, file="edgeRtranscripts-IL4.csv") write.table(x, file="edgeRtranscriptsExpr-IL4.csv")