Question: Deconvolution Methods on RNA-Seq Data (Mixed cell types)
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Pauly Lin ▴ 120
@pauly-lin-7537
Last seen 6.3 years ago
University of New South Wales, Australia

Dear all, 

I want to use deconvolution methods to estimate the proportions of different cell types in my RNA-Seq samples. In this post ( https://www.biostars.org/p/121286/ ), it's mentioned that "signals from different cell-types/tissues will sum more linearly in microarrays than RNAseq, where the sum is highly non-linear" and  "Any paper talking about signal separation will likely mention that the signals need to be independent for optimal performance, which they self-evidently aren't in RNAseq." Could someone please explain to me why in RNA-Seq samples the signals from different cell-types/tissues are not independent, or why the signals don't sum linearly?

Also, if I do decide to go ahead with using deconvolution methods, should I apply the deconvolution methods to raw RNA-Seq counts, log(CPM) transformed data, or voom transformed data?

Thanks. 

Paul

deconvolution rna-seq • 1.4k views
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@steve-lianoglou-2771
Last seen 19 hours ago
United States

Don't have thorough answers for you yet, but I did poke Devon over at Biostars to clarify the "self-evident" nature of that statement.

A few other notes:

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