Detect tissue heterogeneity in gene expression data with BioQC

BMC Genomics

Software

Martine Burtin¹  Fabiola Terzi¹  Martin Ebeling²  Gregor Sturm²  Silvia Ines Pomposiello²  Laura Badi²  Jitao David Zhang²  Klas Hatje²  Clemens Broger³ 

[1] Inserm U1151, Université Paris Descartes, Institut Necker Enfants Malades, Hôpital Necker Enfants Malades, 149, Rue de Sèvres, 75015, Paris, France;Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland;Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland;Present address: Peter-Rot-Strasse 84, 4058, Basel, Switzerland;

关键词: Gene expression;    Quality control;    Wilcoxon-Mann-Whitney test;    Gene-set enrichment analysis;   

DOI  :  10.1186/s12864-017-3661-2

 received in 2016-09-27, accepted in 2017-03-25,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundGene expression data can be compromised by cells originating from other tissues than the target tissue of profiling. Failures in detecting such tissue heterogeneity have profound implications on data interpretation and reproducibility. A computational tool explicitly addressing the issue is warranted.ResultsWe introduce BioQC, a R/Bioconductor software package to detect tissue heterogeneity in gene expression data. To this end BioQC implements a computationally efficient Wilcoxon-Mann-Whitney test and provides more than 150 signatures of tissue-enriched genes derived from large-scale transcriptomics studies.Simulation experiments show that BioQC is both fast and sensitive in detecting tissue heterogeneity. In a case study with whole-organ profiling data, BioQC predicted contamination events that are confirmed by quantitative RT-PCR. Applied to transcriptomics data of the Genotype-Tissue Expression (GTEx) project, BioQC reveals clustering of samples and suggests that some samples likely suffer from tissue heterogeneity.ConclusionsOur experience with gene expression data indicates a prevalence of tissue heterogeneity that often goes unnoticed. BioQC addresses the issue by integrating prior knowledge with a scalable algorithm. We propose BioQC as a first-line tool to ensure quality and reproducibility of gene expression data.

【 授权许可】

CC BY   
© The Author(s) 2017

【 图 表 】

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