Understanding the colData in DESeq2
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2.0 years ago
ATCG ▴ 370

Hi, I am using DESEq2 to compute differential gene expression, and if I am interpreting this correctly, changing the names of categories in the colData reverses the test that is performed. The matrix counts is intact with 6 col and (WT)3 (hux)3. Here WT=wild type or control and hux=treatment. I want to perform the test to obtain LFC hux/control(WT). I encountered this issue yesterday, and I tried to articulate the problem the best I could, so I hope my question makes sense.

expdesign = read.delim("expdesign.txt", colClasses='factor')

WT_1    control
WT_2    control
WT_3    control
hux_1       hux
hux_2       hux
hux_3       hux

mcols(res, use.names = T)

log2FoldChange results log2 fold change (MLE): condition hux vs control

L2FC is hux/control

expdesign = read.delim("expdesign.txt", colClasses='factor')

WT_1    WT
WT_2    WT
WT_3    WT
hux_1       hux
hux_2       hux
hux_3       hux

mcols(res, use.names = T)

log2FoldChange results log2 fold change (MLE): condition WT vs hux

L2FC is WT/hux

DEseq2 ColData LFC • 1.4k views
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I suspect this is a consequence of turning a column into a factor. The factor levels are by default set alphabetically. You can check if this is the cause by changing the factor levels and seeing if that changes the constrast.

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Actually this will yield the same results: expdesign = read.delim("expdesign.txt")

Also, DESeqDataSetFromMatrix will convert to factor with a warning which is why I am reading them as factors.

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If DESeqDataSetFromMatrix is coercing it to a factor, the factor levels will also be set alphabetically. After you import the first data.frame in your example, try both expdesign[[2]] <- factor(expdesign[[2]], levels=c("control", "hux")) as well as expdesign[[2]] <- factor(expdesign[[2]], levels=c("hux", "control")). Of course you can just ignore this too and set the contrast manually when returning the results.

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Yes, this works thank you so much for your help!

How would you " ignore this too and set the contrast manually when returning the results?"

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If you give your columns names, such as colnames(expdesign) <- c("sample", "condition"), you can specify what comparison you want to make at the end. For example, you could do results(dds, contrast=c("condition", "control", "hux")) or results(dds, contrast=c("condition", "hux", "control")).

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Thank you, rpolicastro. I also found the answer to this on pg 14 of Beginner's guide to using the DESeq2 package. http://www.bioconductor.org/packages/2.14/bioc/vignettes/DESeq2/inst/doc/beginner.pdf

Do the following "to make sure that Control is the rst level in the treatment factor, so that the default log2 fold changes are calculated as treatment over control and not the other way around".

dds$treatment <- relevel( dds$treatment, "Control" )
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