Question: Clarification on contrast levels in DESeq2
gravatar for Menachem Sklarz
2.7 years ago by
European Union
Menachem Sklarz10 wrote:


I was running DESeq2 with the simplest possible design: one factor (called group) with 5 levels: A-E, where A is the reference level.

I run the model thus:

dds <- DESeqDataSetFromMatrix(countData = counts_data,
                              colData = col_data,
                              rowData = row_data,
                              design = ~ group)
dds <- DESeq(dds)

To test pairwise contrasts, I do the following:

b<-results(dds,contrast = c("group","A","B"))


However, I noticed several strange things:

  1. For the A-B comparison, I get significant genes when all samples in groups A and B have zero counts! It looks as if the comparison is against the Intercept, rather than level A, even though the results object says the data is for A vs. B comparison.
  2. When I change the reference level to E, I do not get these significant genes.
  3. When testing C vs. D, I get different results depending on whether A or E are the refenece level.
  4. I get a significant enrichment (adjusted p-value of 1.06E-04) when the reads in group A are 0 (for two samples) and the reads in group B are c(1,2,2). Does this make sense? Aren’t extremely low-count genes supposed to be filtered out in the “independent filtering” stage?

Note: I solved issues 1-3 by setting betaPrior to TRUE and modelMatrixType to "expanded".

Thanks a lot! Menachem

rna-seq deseq2 • 1.3k views
ADD COMMENTlink modified 2.5 years ago by Biostar ♦♦ 20 • written 2.7 years ago by Menachem Sklarz10

An obvious comment: why are you even using data for samples that are all zero?

ADD REPLYlink modified 2.5 years ago • written 2.5 years ago by Kevin Blighe69k
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