Assessing the consistency of public human tissue RNA-seq data sets

Abstract

Sequencing-based gene expression methods like RNA-sequencing (RNA-seq) have become increasingly common, but it is often claimed that results obtained in different studies are not comparable owing to the influence of laboratory batch ef- fects, differences in RNA extraction and sequencing library preparationmethods and bioinformatics processing pipelines. It would be unfortunate if different experiments were in fact incomparable, as there is great promise in data fusion and meta-analysis applied to sequencing data sets. We therefore compared reported gene expressionmeasurements for osten- sibly similar samples (specifically, human brain, heart and kidney samples) in several different RNA-seq studies to assess their overall consistency and to examine the factors contributingmost to systematic differences. The same comparisons were also performed after preprocessing all data in a consistent way, eliminating potential bias frombioinformatics pipe- lines. We conclude that published human tissue RNA-seq expressionmeasurements appear relatively consistent in the sense that samples cluster by tissue rather than laboratory of origin given simple preprocessing transformations. The art- icle is supplemented by a detailed walkthrough with embedded R code and figures.