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

Frida Danielsson, Tojo James, David Gomez-Cabrero, Mikael Huss

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

10 Citations (Scopus)

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 effects, differences in RNA extraction and sequencing library preparation methods 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 expression measurements for ostensibly 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 contributing most to systematic differences. The same comparisons were also performed after preprocessing all data in a consistent way, eliminating potential bias from bioinformatics pipelines. We conclude that published human tissue RNA-seq expression measurements appear relatively consistent in the sense that samples cluster by tissue rather than laboratory of origin given simple preprocessing transformations. The article is supplemented by a detailed walkthrough with embedded R code and figures.
Original languageEnglish
Title of host publicationBriefings in Bioinformatics
PublisherOxford University Press
Pages941-949
Number of pages9
ISBN (Print)1477-4054 (Electronic)\r1467-5463 (Linking)
DOIs
Publication statusPublished - 6 Feb 2015

Publication series

NameBriefings in Bioinformatics
Volume16

Keywords

  • Clustering
  • Gene expression
  • Meta-analysis
  • Public data
  • RNA-seq

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