Question: Comparing gene expression for individual gene between samples in R
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gravatar for mrz132435
5 days ago by
mrz13243520
mrz13243520 wrote:

Hi. I have whole exome raw count data for a bunch of samples, and I'm looking to compare gene expression for a single gene between samples, e.g., between samples that have some type of mutation in said gene and samples that don't. I'd like to do this analysis over a set of genes, but each one individually (that is, I only care about how a mutation in gene X affects expression in gene X, but not how it might affect expression elsewhere). The issue is, I'm not sure how to do this in DESeq2, which seems to be the most popular gene expression package for R. The main DEseq2 function requires a design in which each sample is assigned a condition (e.g., treated vs. untreated) and then applies its negative binomial model to the unnormalized counts, but it would be prohibitively time-consuming to rerun DESeq2 for each gene, using each gene's mutation status as the condition for each run.

So, what would be the best way to do just normalize my count data so that I can compare individual genes (either with DESeq2 or another package)? Also, any comments on what the optimal statistical approach would be for comparing expression between two groups of samples for an individual gene are also appreciated. Thanks.

rna-seq R • 85 views
ADD COMMENTlink modified 5 days ago by swbarnes27.0k • written 5 days ago by mrz13243520
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gravatar for swbarnes2
5 days ago by
swbarnes27.0k
United States
swbarnes27.0k wrote:

The size normalization step of DESeq can be done with a design of ~1. It doesn't take the design into account at all.

Unless you have an enormous number of samples, it doesn't take all that long to run DESeq multiple times. Or run it once, and ask for the result of multiple contrasts.

ADD COMMENTlink written 5 days ago by swbarnes27.0k
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