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RSEM Downstream Analysis

This question is similar to this. I have RSEM output files namely "quatify.genes.result" and "quatify.isoform.result". My question is reagarding the downstream analysis, I want to get differential expressed genes. the quatify.genes.result file has two columns one expected counts and FPKM value. How to convert these expected counts to raw counts, so that I can use DESEQ or DEGseq. Could anyone suggest me other other softwares for the downstream analysis on RSEM output/ or what could be a better approach to this.

Thanks

rsem count rna-seq
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updated by Ram Ram • written by Anushka • Anushka

It was FPKM and not RPKM...I am going to edit this in my question

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written by Anushka • Anushka

1 answer

You can't convert expected counts to raw counts, that's impossible. You should not use DESeq(2) or DEGseq to analyze this dataset. Limma (via voom()) would work and edgeR is apparently also OK with expected counts.

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written by Devon Ryan Devon Ryan

my two cents: DESeq2 on rounded estimated counts will behave similarly to edgeR on estimated counts. If the ambiguity of reads across genes is minimal, then estimated counts will behave similar to raw counts. However, where this can potentially break down (the Var(count) >= E(count) assumption) is with many ambiguous reads across genes.

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written by Michael Love Michael Love

I've been wondering what your opinion was on this. Have you thought about relaxing/removing the check for integers when a DESeqDataSet is constructed? I suppose the biggest concern is people shooting themselves in the foot by then inputting FPKM/RPKM...

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written by Devon Ryan Devon Ryan

No plans to relax the check. It helps us a lot.

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written by Michael Love Michael Love

I have an RNA Seq data set that is log2(x+1) transformed RSEM normalized count.

Description of the data set is as follows :- The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. Level 3 data was downloaded from TCGA data coordination center. This dataset shows the gene-level transcription estimates, as in log2(x+1) transformed RSEM normalized count. Genes are mapped onto the human genome coordinates using UCSC Xena HUGO probeMap.

You reckon i can round off this normalized count and use DeSeq on it? Please help!

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written by bioinfo456 bioinfo456

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