Question

deseq2 pseudo counts

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Prasad Siddavatam ▴ 150

@prasad-siddavatam-4508

Last seen 9.4 years ago

United States

For each sample (and replicate), do I have to convert zero read counts to ones (pseudo counts) while running deseq2?

I assume I don’t have to, but a quick clarification.

deseq2 • 3.0k views

ADD COMMENT • link updated 2.6 years ago by Michael Love 41k • written 9.4 years ago by Prasad Siddavatam ▴ 150

Michael Love · Answer 1 · 2014-12-09

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Michael Love 41k

@mikelove

Last seen 16 hours ago

United States

Hi Prasad, No you don't have to convert. The zeros (including when all samples have a zero), are automatically handled by the model (and by the software).

ADD COMMENT • link 9.4 years ago Michael Love 41k

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Hello, Michael!

How exactly does DESeq2 handles with the zero counts? I tried to understand and look for it but is still unclear for me. Thanks

ADD REPLY • link 6.2 years ago mgdias.jose • 0

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Here is the DESeq2 paper with details on the methods:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4302049/

Counts of zero are not treated differently by the statistical model than a count of 1, 2, ...

ADD REPLY • link 6.2 years ago Michael Love 41k

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Hi Michael! But when I try to use only the normalized data from DESeq2, I can see that the data [gene] with at least one value as "zero" is eliminated. This will be affecting the result - that is in this case, when such genes are eliminated it's not possible for us to know the gene's expression value in other tissues.

Please advise on how to consider the data with zero read counts as well..

Thanks in advance.

ADD REPLY • link 2.6 years ago Sandhiya • 0

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If you mean it is filtered, that is controlled by independentFiltering in results(), just set it to FALSE.

In this case, I would recommend a minimal filter at the top of your script, before DESeq():

keep <- rowSums(counts(dds) >= 10) >= X
dds <- dds[keep,]

Some recommendation for X is the sample size of the smallest condition group (this from limma/edgeR software guides).

ADD REPLY • link 2.6 years ago Michael Love 41k

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Hi Micheal!

Thank you for the quick response.. Firstly, I congratulate and Thank you and your team for your efforts on DESeq2.

Secondly, I fixed the problem by adding your suggestion. I just tried to change it to zero.

keep <- rowSums(counts(dds)) >=0
#keep only these data
dds <- dds[keep,]

Now, I have the genes which had one or two zero values with other integer values with its normalized counts- ie., zero counts are not discarded.

ADD REPLY • link updated 2.6 years ago by Michael Love 41k • written 2.6 years ago by Sandhiya • 0

score 0 · Answer 2 · 2014-12-09

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Prasad Siddavatam ▴ 150

@prasad-siddavatam-4508

Last seen 9.4 years ago

United States

Thank you very much.

ADD COMMENT • link 9.4 years ago Prasad Siddavatam ▴ 150