Too many significant genes when integrating gtex and tcga
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Reza • 0
Last seen 10 days ago
United Kingdom

I have been working on data from the recount3 project to integrate GTEx and TCGA data and perform DEG analysis using DESeq2. However, I am encountering an issue where I am getting too many significant genes while using datasets with large sample size such as TCGA-COAD and colon tissue in GTEX.

This phenomenon is also mentioned here (PMID: 35199033), which reports that 92% of total gene input is accounted for by differentially expressed genes (DEGs) detected across TCGA primary tumor and GTEx normal colon tissue samples.

When using limma for analysis, the treat function can help address this issue by computing empirical Bayes moderated-t p-values relative to a minimum fold-change threshold.

Now I have two questions:

  1. I was wondering if there is a similar solution available for DESeq2.
  2. Is there a better approach to address this problem? because even after using treat there are still many significant genes left.
RNA-seq DESeq2 • 279 views
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These two datasets are from completely different experiments / batches. It is utterly meaningless to compare them. I would suggest comparative analysis within subtypes only using TCGA data.

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Last seen 2 days ago
United States

See ?results, in particular the lfcThreshold argument.

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Yes, this was one of the aspects we highlighted in the 2014 paper, and it's also in the workflow. Check these places first.

Also take a step back and consider: you are asking, with many samples, if the null is true that gene expression is constant regardless of tumor/normal status. Of course it will reject the majority of genes.

Maybe there is a better approach to your biological question that null hypothesis testing.


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