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.[Supplemental material is available online at www.genome.org and http://microarray.cpmc.columbia.edu/tmm.]Gene expression microarray data is a form of high-throughput genomics data providing relative measurements of mRNA levels for thousands of genes in a biological sample. In the last few years, hundreds of laboratories have collected and analyzed microarray data, and the data are beginning to appear in public databases or on researchers' Web sites. These resources serve at least two purposes. One is as an archive of the data, which allows other researchers to confirm the results that have been published by the originator of the data. A second use is to permit novel analyses of the data, that go beyond what was envisioned or possible at the time of the original study. A novel analysis could involve just a single data set, or a meta-analysis of many data sets (where a "data set" is a group of microarrays that were collected together, and typically described as a group in a single publication). The combined analysis of multiple data sets forms the main topic of this paper.Most existing studies that have analyzed multiple independently collected microarray data sets have focused on differential expression, comparing two or more similar data sets to look for genes that distinguish different sets of samples (Breitling et al.