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Isoform Differential Expression Analysis

So basically I want to investigate whether there is a switch in isoform preference between condition 1 and condition 2. My idea was to conduct differential expression analysis on the data. If isoformA have decreased differential expression and isoformB have increased differential expression from condition 1 to condition 2, there is a shift in isoform preference.

Fortunately, I have rsem data so I just need a tool that does what I want which is also my issue. From doing my own investigation rsem tends to be paired with EBseq for analysis, so I looked up the manual from EBseq. Based on the manual, the output of EBseq only informs whether an isoform is differentially expressed or equivalently expressed based on your FDR. I need a tool that gives some fold change.

Tools like DESeq2 will give me the results in the form I want but according to various posts, using isoform counts will violate the underlying assumptions of these tool.

Is there any tool that is made for isoform data and produces output that i want or is it acceptable to adapt DESeq2 to my purposes?

rna-seq rsem deseq2 ebseq
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updated by bendakiiracha17 • bendakiiracha17 • written by john • john

4 answers

Take a look at DEXseq. SInce you have RSEM data it is worth to take a look at the enter link description here

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written by EVR EVR

I would recommend IsoformSwitchAnalyzeR ( http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ ) which directly parses RSEM (count) output (also support cufflinks/cuffdiff, Kallisto, Salmon). IsoformSwitchAnalyzeR enables statistical identification of isoform switches with predicted functional consequences where the consequences can be chosen from a long list but includes gain/loss of protein domains, signal peptides changes in NMD sensitivity etc.

For other inspiration of other post-analysis of isoform switches take a look at http://mcr.aacrjournals.org/content/early/2017/06/02/1541-7786.MCR-16-0459

Hope this helps

Kristoffer

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written by Kristoffer Vitting-Seerup Kristoffer Vitting-Seerup

I would recommend IsoformSwitchAnalyzeR ( http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ ) which directly parses RSEM (count) output (also support cufflinks/cuffdiff, Kallisto, Salmon). IsoformSwitchAnalyzeR enables statistical identification of isoform switches with predicted functional consequences where the consequences can be chosen from a long list but includes gain/loss of protein domains, signal peptides changes in NMD sensitivity etc.

For other inspiration of other post-analysis of isoform switches take a look at http://mcr.aacrjournals.org/content/early/2017/06/02/1541-7786.MCR-16-0459

Hope this helps

Kristoffer

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written by bendakiiracha17 • bendakiiracha17

Hi everyone,

I’m currently using IsoformSwitchAnalyzeR to analyze PacBio Long Reads (quantified with IsoQuant).

I am encountering a surprising result: from an initial set of ~118,000 isoforms, the tool only identifies about 20 switching isoforms. This seems underestimated, especially since previous studies using short-read data on similar models usually identify hundreds of switches.

I have already tried to relax the filters (dIF > 0.1 and even 0.05) and adjusted the preFilter() parameters, but the number of significant results remains very low. I suspect the issue might be related to the p-value adjustment (FDR) being too stringent for this specific dataset, or perhaps a lack of power in the isoformSwitchTestDEXSeq().

Has anyone experienced this "underestimation" with Long Reads? Should I look into the dispersion estimation or is there a specific way to handle FDR for long-read counts in the package?

Thanks for your help

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written by bendakiiracha17 • bendakiiracha17

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