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Avoiding pitfalls in gene (co)expression meta-analysis
Open accessAbstract
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.
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For identifying regulatory relationships, perturbation data are preferable because canonical data cannot distinguish between true and false positives in regulatory relationships.
“To identify regulatory relationships, perturbation data are preferable, as canonical data cannot distinguish between true and false positives in regulatory relationships [129, 168].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [168]
The Introduction discusses differential expression and coexpression, and the Discussion addresses their reliability across studies. Neither passage compares perturbation with canonical data or says canonical data cannot distinguish true from false regulatory relationships. Searches for perturbation data and regulatory-relationship inference found no passage addressing that claim. (LLM, conf=0.72)
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References 31 · Cited by 11
Metadata
- Authors
- Östlund, G., & Sonnhammer, E. L. L.
- Affiliations
- Stockholm University (SE)
- Corresponding author
- Sonnhammer, Erik L.L.
- Venue
- Genomics
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 103
- Issue
- 1
- Pages
- 21-30
- ISSN
- 0888-7543 (ISSN-L)
- OpenAlex
- W2086673782
- Cited by
- 11
- References
- 31
- Citations by year
- 2026: 1 · 2022: 2 · 2020: 1 · 2019: 1 · 2018: 1 · 2017: 1 · 2016: 2 · 2014: 2
- FWCI
- 0.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Genomics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- differential gene expression, meta-analysis, background correction, microarray platforms, cancer gene expression, sample size, reproducibility
- MeSH terms
- Humans, Models, Molecular, Neoplasms, Reproducibility of Results, Gene Expression Regulation, Neoplastic, Computational Biology, Databases, Genetic, Microarray Analysis
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