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Coexpression Analysis of Human Genes Across Many Microarray Data Sets Lee, Homin K; Hsu, Amy K; Sajdak, Jon; Qin, Jie; Pavlidis, Paul

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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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Title	Coexpression Analysis of Human Genes Across Many Microarray Data Sets
Creator	Lee, Homin K; Hsu, Amy K; Sajdak, Jon; Qin, Jie; Pavlidis, Paul
Contributor	Cuthill, Melissa
Date Created	2004; 2019-03-12
Date Issued	2019-03-12
Description	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.
Subject	Medicine, Health and Life Sciences
Type	Dataset
Notes	http://hdl.handle.net/11272/10580
Date Available	2019-03-12
Provider	University of British Columbia Library
License	CC0 1.0
DOI	10.14288/1.0364309
URI	https://doi.org/10.5683/SP2/JOJYOP
Publisher DOI	https://doi.org/10.5683/SP2/JOJYOP
Rights URI	http://creativecommons.org/publicdomain/zero/1.0
Aggregated Source Repository	Dataverse

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CC0 1.0