What to the log 2 fold changes mean using LRT on time course in DESeq2
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@0b9197a6
Last seen 28 days ago
United States

Hi as I understand it, in DESeq2 one of the ways to run a DEG analysis on time course data is using a likelihood ration test on two different models. For example, I can test for genes that show significant differences in time trajectories for two different treatments can be done as follows

ddsMat<-DESeqDataSetFromMatrix(countData=Xdata, coldata=DMAT, design= treatment+time+treatment:time)
dds<-DESeq(ddsMat,test="LRT", reduced =~ treatment+time)

From my limited understanding of GLM, this basically looks at two different models one with an extra-term for the interaction between time and treatment and the other without and performs a statistical test (LRT) to determine which one better models the mean expression values better. You can then look at the results matrix and determine which genes meet your significance criteria. However, the results table also gives you log2FoldChange but it is not clear what this log2Foldchange is with respect to. In a standard DEG analysis the log2Foldchange is the log2foldchange between one group and a reference group, but in this case we have a reference model. I'm trying to figure out how to plot these results, and a volcano plot doesn't make sense unless there is an interpretable meaning to the log2foldchange value. Thanks

DESeq2 timecourse DifferentialExpression • 190 views
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@james-w-macdonald-5106
Last seen 5 hours ago
United States

It's still just the same logFC you would get with the Wald test.

> library(DESeq2)
> z <- makeExampleDESeqDataSet()
> z$condition <- factor(rep(c("A","B","C"), each = 4))
> dds <- DESeq(z)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
> dds.lrt <- DESeq(z, "LRT", reduced = ~1)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
> all.equal(results(dds)$log2FoldChange, results(dds.lrt)$log2FoldChange)
[1] TRUE
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And

> resultsNames(dds.lrt)
[1] "Intercept"       
[2] "condition_B_vs_A"
[3] "condition_C_vs_A"

> resultsNames(dds)
[1] "Intercept"       
[2] "condition_B_vs_A"
[3] "condition_C_vs_A"
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