I use Deseq2 to perform differential gene expression analysis. I have successively changed the LFC, i.e.LFC > 1.5 and LFC > 2 as shown in the following code:
dds_ALL <- DESeq(dds_ALL) GNF_D6_vs_GNF_WT =results(dds_ALL,contrast = c("genotype","TreatedD6","TreatedWT"),lfcThreshold = 1,alpha = 0.05) summary(GNF_D6_vs_GNF_WT,rm.na = TRUE) head(GNF_D6_vs_GNF_WT) treshlod <- GNF_D6_vs_GNF_WT$padj < 0.05 & abs(GNF_D6_vs_GNF_WT$log2FoldChange) >1 GNF_D6_vs_GNF_WT$treshlod<-treshlod sig_D6WT <- data.frame(subset(GNF_D6_vs_GNF_WT,treshlod==TRUE)) write.csv(sig_D6WT , file="TreatedD6_vs_TreatedWT.csv") write.csv(rownames(sig_D6WT) , file="TreatedD6_vs_TreatedWT_ID.csv",row.names = FALSE)
When I keep the LFC > 1.5, all the groups of genes that have for example a LFC = 3, I expect to see the same genes when I put the LFC to 2 and with the same value of LFC, But however I notice a slight variation of the LFC of these genes instead of 3 they come back to 2.8. So I would like to know if this variation is due to the Deseq2 algorithm or due to my code?
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