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Genomics. 2014 Jan;103(1):21-30. doi: 10.1016/j.ygeno.2013.10.006. Epub 2013 Oct 30.

Avoiding pitfalls in gene (co)expression meta-analysis.

Author information

  • 1Stockholm Bioinformatics Centre, Science for Life Laboratory, Box 1031, SE-17121 Solna, Sweden; Department of Biochemistry and Biophysics, Stockholm University, Sweden.
  • 2Stockholm Bioinformatics Centre, Science for Life Laboratory, Box 1031, SE-17121 Solna, Sweden; Department of Biochemistry and Biophysics, Stockholm University, Sweden; Swedish eScience Research Center, Sweden. Electronic address: Erik.Sonnhammer@scilifelab.se.

Abstract

Differential gene expression analysis between healthy and diseased groups is a widely used approach to understand the molecular underpinnings of disease. A wide variety of experimental and bioinformatics techniques are available for this type of analysis, yet their impact on the reliability of the results has not been systematically studied. We performed a large scale comparative analysis of clinical expression data, using several background corrections and differential expression metrics. The agreement between studies was analyzed for study pairs of same cancer type, of different cancer types, and between cancer and non-cancer studies. We also replicated the analysis using differential coexpression. We found that agreement of differential expression is primarily dictated by the microarray platform, while differential coexpression requires large sample sizes. Two studies using different differential expression metrics may show no agreement, even if they agree strongly using the same metric. Our analysis provides practical recommendations for gene (co)expression analysis.

KEYWORDS:

Cancer gene expression; Differential coexpression; Differential expression; Microarray data processing

PMID:
24184361
DOI:
10.1016/j.ygeno.2013.10.006
[PubMed - indexed for MEDLINE]
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