hello,
I am working on DEseQ2 to find the differentially expressed when i compare the control (3 replicates) Vs. 2 treated samples (3 replicates each, and 6 in total for treated condition) . I am mainly interested in the genes those are differentially expressed when i compare the two treatments to the control.
But im not sure how to set up an experiment in Deseq2
cell dex
control1 s1 control
control2 s2 control
control3 s3 control
Ec32.Eury05.T1 s4 Sus
Ec32.Eury05.T2 s5 Sus
Ec32.Eury05.T3 s6 Sus
Ec32.Eury96.T1 s7 Sus1
Ec32.Eury96.T2 s8 Sus1
Ec32.Eury96.T3 s9 Sus1
So far the commands i used were
coldata<-data.frame(row.names=colnames(Sum_rep.df),cell=paste0("s", 1:9), dex= c(rep('control', 3), rep('Sus',3), rep('Sus1',3)))
coldata$cell<-factor(coldata$cell,coldata$dex)
dds <- DESeqDataSetFromMatrix(countData = round(Sum_rep.df), colData = coldata, design = ~ dex)
Could someone direct me , if its right?
Any help would be appreciated
Looking OK so far, but I always find it best to programmatically derive the labels from the string directly, in case for some reason you sort the Sum_rep.df differently in future, when your hard-wiring of the order of the samples would cause problems. e.g.
dex=gsub(".*(control|Eury05|Eury96).*", "\\1", colnames(Sum_rep.df))and maybe you wantrep=gsub(".*(.)", "\\1", colnames(Sum_rep.df))to capture the batch-number, as those T1's etc look suspiciously like a batch effect, and you'd have a design of~ batch + dex?ok. i will take your comment in to consideration.
I was also wondering with the comparison,i have made the log2Fc for the deferentially expressed genes are calculated by comparing the control and Sus1. But i would like to have a comparison between (control Vs. Sus) and (control Vs. Sus1) in this experiment. Because im interested in the common genes being differential expressed during the susceptible responses.
Could you suggest if its possible to design this and implement in deseq2?
Thnx in advance
Yes, you can do that either via an LRT (test="LRT" when you call the DESeq function), which will give you genes that are different between any pair of conditions, or you can stick with a Wald test and specifically build contrasts to pull out the comparison you want and use those in the 'results' command. If control is currently the baseline condition, then you can get the comparisons against that with a simple 'name=X' where X is the obvious choice out of resultsNames. If you want sus vs sus1, you'll need the slightly more complex
contrast=list(X, Y)format, where X and Y are the two terms in resultsNames that correspond to sus terms (the 'control' will cancel out).