Highlight report quality control for genomewide

Authors: Marianna Grinberg

Publish Date: 2015/11/27

Volume: 89, Issue: 12, Pages: 2459-2461

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Abstract

In this issue of the Archives of Toxicology Miriam Lohr and colleagues from TU Dortmund University in Germany contributed a method how to identify sample mixup in gene expression datasets Lohr et al 2015 Today large sets of genomewide expression data obtained either by gene array or by RNAseq are publicly available This offers the possibility to reuse previously analyzed data and to combine several datasets in order to improve statistical power Lohr et al 2015 However a risk is that samples in public databases have been misannotated This may lead to errors which are particularly critical when the same error compromises several studies which all rely on the same publicly available expression data Therefore Lohr et al 2015 established a biostatistical method that allows the retrospective identification of misannotated samples The authors show that two types of error occur surprisingly often in public databases 1 A sample is analyzed twice and the duplicate is erroneously labeled with the identification number of another patient 2 Two samples are mixed up When this mixup occurs between samples from male and female individuals it can clearly be identified by a set of sexspecific genes The authors applied the mixup identification procedures to 45 publicly available datasets including 4913 individuals Erroneous sample annotation was identified in a surprisingly high fraction of 40  of the analyzed datasets Lohr et al 2015 The authors also demonstrate that the removal of the identified erroneous samples may critically influence the results of statistical analysis of individual studiesPublicly available datasets have been particularly often used in clinical cancer research to identify prognostic genes Schmidt et al 2008 2012 Cadenas et al 2010 2014 Stewart et al 2012 Godoy et al 2014 Stock et al 2015 However genomewide expression data are also increasingly used in toxicological research Song et al 2013 Faust et al 2013 Shao et al 2014 Shinde et al 2015 Reif 2014 Stöber 2014 Marchan 2014 Hammad and Ahmed 2014 Hengstler 2011 Stewart 2010 Cuttingedge topics are the establishment of classifiers and signatures in developmental toxicity Krug et al 2013 Rempel et al 2015 Balmer et al 2014 Zimmer et al 2014 Waldmann et al 2014 and hepatotoxicity Campos et al 2014 Yafune et al 2013 Doktorova et al 2012 Zellmer et al 2010 Godoy et al 2015 For these purposes large public databases are available Grinberg et al 2014 Using the novel methods for the identification of sample annotation errors described by Lohr et al 2015 will improve the reliability of genomewide biostatistical analyses in future

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