I ran DESeq2 analysis( DESeq2_1.8.2 ) with count data for a chip-seq project(comparing ctrl vs trt with no replicates). All padj values are 0.99. The log2FoldChanges seem to be incorrectly calculated and for the same reason I believe some regions don't show up as significantly differentially expressed(p>0.05) although there is a large fold change between the ctrl and trt.
sample counts data,ctrl and trt
group10810 55 1742 group10811 69 2829
normalized counts, ctrl and trt are cols
group10810 77.7817459305202 1231.78001282697 group10811 97.5807358037436 2000.40508397674
group_id baseMean log2FoldChange lfcSE stat pvalue padj group10810 654.7808793787 -0.4367988107 0.4351310663 -1.0038327404 0.3154592404 0.9999999545 group10811 1048.9929098902 -0.4066302582 0.4179425577 -0.9729333629 0.3305864221 0.9999999545
Steps in deseq2 analysis countData<-read.table(counts.table, header = TRUE, row.names = 1) colData<-read.table(metadata.file, header = TRUE, row.names = 1) dds<-DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~condition) dds <- dds[ rowSums(counts(dds)) > 1, ] dds <- DESeq(dds) normalizedCounts<-as.data.frame(counts(dds, normalized = TRUE)) res <- results(dds)
Thanks for the help.