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Question: DESeq2: Different Dispersions for different sample groups
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gravatar for hhoeflin
3.5 years ago by
hhoeflin0
Switzerland
hhoeflin0 wrote:

Hi,

I am using DESeq2 for analyzing an RNASeq Experiment. The details of the setup are as follows:

I have two treatments for which I want to test for differential expression. However, when inspecting the data, the variability of the counts in one treatment group is considerably higher than the variability in the other group (for same mean).

If I understand the DESeq2 documentation correctly, the DESeq function fits one dispersion parameter per gene, not allowing for sample groups with different dispersion. 

a) Is this understanding correct?

b) Is there a way to allow for sample-group specific dispersions? In a linear model setting, this would usually be achieved by setting different weights for the samples in different groups.

Thanks!

Holger

ADD COMMENTlink modified 3.5 years ago by Steve Lianoglou12k • written 3.5 years ago by hhoeflin0
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gravatar for Steve Lianoglou
3.5 years ago by
Denali
Steve Lianoglou12k wrote:

Not sure if it has hit your radar, but the limma/voom folks have recently published a paper that describes the voomWithQualityWeights function, which allows you to achieve this behavior to analyze rna-seq data with sample level weights:

 

ADD COMMENTlink written 3.5 years ago by Steve Lianoglou12k

Great thanks - that sounds very close to what I need. No, it hadn't hit my radar yet. Guess I will switch the workflow over to limma.

~Holger

 

ADD REPLYlink written 3.5 years ago by hhoeflin0
0
gravatar for Michael Love
3.5 years ago by
Michael Love20k
United States
Michael Love20k wrote:

hi Holger,

a) yes, you're right. we fit one dispersion estimate per gene, so locking the relationship Var = mean + dispersion * mean^2.

b) There is not a way to have group-specific dispersion values with DESeq2. Off the top of my head, I think baySeq offers different dispersion estimates per group: getPriors.NB(..., equalDispersions=FALSE)

ADD COMMENTlink written 3.5 years ago by Michael Love20k
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