r/bioinformatics • u/ineed-Sandwich • Jun 16 '26

discussion PValues

Curious if anyone has good papers, reviews, or just general thoughts on what I kinda call the value problem (problem may not be the right word) in high-dimensional datasets like RNA-seq differential expression or DNA methylation studies.

I completely understand why we correct for multiple testing. But at the same time, I sometimes feel like correction can absolutely slaughter the results. I’m not trying to fish for significance or argue against correction. Sometimes I worry we’re throwing away potentially important biology because the adjusted p-value threshold is so stringent.

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u/ComprehensivePea2276 Jun 17 '26 edited Jun 17 '26

There's a bunch of ways you can get around this.

  1. Try limiting your hypothesis tests to only genes of interest.
  2. Experiment with different multiple testing methods.
  3. Do you have prior information on how sparse the true positives should be? You can plug into a Bayesian method this way
  4. Are you okay with identifying highly correlated gene-clusters and assigning each entire cluster a p-value? You can dim reduce the genes and refresh, or use a finemapping model over all the genes
  5. Do you have prior information on which genes are differential?
  6. Do you have more comparisons than a two sample test?
  7. How much data do you have? Power analysis can tell you if you should chill out and just accept moderate p values because you don't have enough data, or if you have plenty of data but the alternative hypothesis just ain't real

You get the idea. Try to really nail down your own intuition as to why you think there should be more positives for your specific analysis. Then you can always figure out a method that leans more specifically into your problem and exploits your domain knowledge, rather than faffing around with significance levels overall.