Question: Is DESeq2 normalisation method appropriate for datasets with multiple cell types?
1
24 days ago by
tkapell10
University of Bonn, Germany
tkapell10 wrote:

Hi all,

 a theoretical question about DESeq2. I have a dataset of sorted blood immune cell types from healthy and diseased humans. I want to calculate DE genes for all the immune cell types so that I can generate a pan-immune disease signature. My worry is that since the blood immune cell types have different amounts of RNA (e.g. neutrophils vs B cells, etc.), the normalisation method that DESeq2 uses internally might give me erroneous results. Do you think I can still use DESeq2 or should I use a different package/normalisation method?


Best, Theo

normalization deseq2 • 64 views
modified 24 days ago by Michael Love26k • written 24 days ago by tkapell10
Answer: Is DESeq2 normalisation method appropriate for datasets with multiple cell types
0
24 days ago by
Michael Love26k
United States
Michael Love26k wrote:

The best way to assess this is by looking at an MA plot. Is it plausible to identify the constant or lowly changing genes in the MA plot?

Thank you very much for the prompt answer. By "identify the constant or lowly changing genes", do you mean to identify these at the MA plot and then test whether these are the same in each of the samples/cell types? Or did I miss your point?

I just mean that you can look at the MA plot to assess if and which genes are being categorized as having LFC near 0. If you have any prior information about genes which you expect should be constitutively expressed you could label those points as well, e.g.

https://bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html#ma-plot