Differential Expression Analysis on RNAseq - RSEM expected_count from UCSC RNA-Seq toil recompute
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Niveditha ▴ 20
@fab15350
Last seen 21 months ago
India

Hi,

I'm using RNAseq - RSEM expected_count from UCSC RNA-Seq toil recompute data (link to data) for performing differential expression analysis using DESeq2. Details of dataset:

data

Prior, to performing the de-analysis, the actual counts are computed using 2^(count_from_toil) - 1, and the counts are rounded using the round() function in R.

Please advise if this is the correct approach to be followed.

DESeq2 Toil RSEM • 1.7k views
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Michael Love Request your suggestions on this

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ATpoint ★ 4.5k
@atpoint-13662
Last seen 10 minutes ago
Germany

Yes, makes sense and was asked/answered before: Expected counts from RSEM in DESeq2

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Thanks for the reply, @atpoint.

I've gone through the shared link before. It talks about using RSEM data as input to DESEq2. In my case also, the expected counts are from RSEM but some preprocessing is already done by UCSC Toil Recompute DB. As suggested in the post, to use the tximport() pipeline, we need the rsem.genes.results.gz file which contains the expected_length output by RSEM. This is not available from "Toil" but just the expected counts are provided. A screenshot of the sample data and format available is shared in the post.

Hence, wanted to check if the step which is being followed - log2 conversion and rounding values is correct.

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