Validating RNA-seq results using previously published data

Large-scale omics studies usually require some form of validation following the initial analysis. For RNA-seq or microarray studies, this validation frequently takes the form of qPCR. However, qPCR isn't always possible and, as pointed out here and here, is not without limitations.

Comparing single gene results against a previously published dataset of similar design offers an alternative (or complementary) approach to traditional qPCR validation. The 2010 paper by Suarez-Farinas et al. provides a nice example of how to do this using GSEA. The authors' approach is based on the reasoning that the genes classified as significantly up- and down-regulated in a given study should be enriched at the top and bottom, respectively, of a microarray or RNA-seq dataset testing the same treatment effect. This method is potentially more meaningful than choosing a handful of results for qPCR replication as it allows a large number of differentially-expressed genes to be tested (e.g. 15-1000). Moreover, it can validate genes that respond robustly to the treatment effect of interest under the varied conditions encompassed by the two datasets, whilst ruling out the influence of many small undesirable methodological differences likely to be reproduced by a within-lab qPCR validation.

If you’re interested, check out the paper by Suarez-Farinas et al. (2010) and our recently published RNA-seq study.