Coexpression Analysis of Human Genes Across Many Microarray Data Sets
Open accessAbstract
We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 “coexpression links” that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function.
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Meta-analysis across many datasets often improves performance dramatically.
“However, meta-analysis across many datasets often improves performance dramatically (Gillis and Pavlidis, 2011a;Lee et al., 2004;Wren, 2009).”
From Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015) · cites this paper as Lee et al., 2004
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- Authors
- Lee, H. K., Hsu, A. K., Sajdak, J., Qin, J., & Pavlidis, P.
- Affiliations
- Columbia University (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2004-06
- Volume
- 14
- Issue
- 6
- Pages
- 1085-1094
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC419787
- OpenAlex
- W2034784092
- Cited by
- 770
- References
- 7
- Citations by year
- 2026: 1 · 2025: 14 · 2024: 14 · 2023: 10 · 2022: 20 · 2021: 23 · 2020: 24 · 2019: 33 · 2018: 40 · 2017: 43 · 2016: 45 · 2015: 52 · 2014: 48 · 2013: 48 · 2012: 44
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- gene co-expression, mRNA expression, co-expression network, microarray data integration, gene function prediction, gene clustering, human genes
- MeSH terms
- Gene Expression Regulation, Genes, Humans, Genetic Linkage, Cluster Analysis, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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