title: 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 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.
authors.forename: Frida
authors.initials: F
authors.lastname: Danielsson
authors.forename: Tojo
authors.initials: T
authors.lastname: James
authors.forename: David
authors.initials: D
authors.lastname: Gomez-Cabrero
authors.forename: Mikael
authors.initials: M
authors.lastname: Huss
mesh_terms: D001921:Brain / Q000378:metabolism; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D006801:Humans; D007668:Kidney / Q000378:metabolism; D009206:Myocardium / Q000378:metabolism; D012313:RNA / Q000235:genetics*; D017423:Sequence Analysis, RNA
chemical_list: D012313:RNA
keywords: RNA-seq; clustering; gene expression; meta-analysis; public data
references.citation: Nat Biotechnol. 2014 Sep;32(9):888-95
references.pmid: 25150837
references.citation: PLoS One. 2014;9(9):e107026
references.pmid: 25268973
references.citation: Nature. 2008 Nov 27;456(7221):470-6
references.pmid: 18978772
references.citation: Nat Methods. 2008 Jul;5(7):621-8
references.pmid: 18516045
references.citation: Bioinformatics. 2009 May 1;25(9):1105-11
references.pmid: 19289445
references.citation: Nat Biotechnol. 2010 May;28(5):511-5
references.pmid: 20436464
references.citation: Nat Rev Genet. 2010 Oct;11(10):733-9
references.pmid: 20838408
references.citation: Nature. 2011 Oct 20;478(7369):343-8
references.pmid: 22012392
references.citation: Bioinformatics. 2012 Mar 15;28(6):882-3
references.pmid: 22257669
references.citation: Bioinformatics. 2012 Apr 15;28(8):1184-5
references.pmid: 22345621
references.citation: Cancer Res. 2012 Apr 15;72(8):1921-8
references.pmid: 22496456
references.citation: Nat Rev Genet. 2013 Jun;14(6):379-89
references.pmid: 23657481
references.citation: Nat Genet. 2013 Jun;45(6):580-5
references.pmid: 23715323
references.citation: Nat Biotechnol. 2013 Nov;31(11):1015-22
references.pmid: 24037425
references.citation: PLoS One. 2014;9(1):e85150
references.pmid: 24416353
references.citation: Mol Cell Proteomics. 2014 Feb;13(2):397-406
references.pmid: 24309898
references.citation: Nat Biotechnol. 2014 Sep;32(9):915-25
references.pmid: 25150835