Help with Designing an Approach for Biomarker Signature Generation
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2.1 years ago
JJ ▴ 560

Hi all,

I am trying to design an experiment and could use some input. My goal is to generate a predictive gene signature, which can separate two groups BUT I have a number of confounding variables. My starting point are RNA-seq experiments.

  • compute gene expression from RNA-seq experiments (lower number of samples)
  • build model based on RNA-seq training data (Elastic Net regression)
  • test the model with the test data

=> pre-select a gene panel

  • use a targeted method e.g. qPCR or NanoString and get gene expression for the gene panel (high number of samples)
  • build model based on training data for the targeted method (Elastic Net regression)
  • test the model with the test data

=> final panel

Does this make sense? Or should I better use just RNA-seq experiments but with a higher number of samples. Or would it make sense to compute the DEG with RNA-seq, select the top genes (or pre-select somehow) and then do the model prediction just with the targeted method?

Can anyone also in general advise on sample size requirements for marker predictions?

Thank you so much!

RNA-Seq gene • 433 views

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