Life Sciences Commons^™

Cautions About The Reliability Of Pairwise Gene Correlations Based On Expression Data, Scott Powers, Matt Dejongh, Aaron A. Best, Nathan L. Tintle

Faculty Publications

Background: Rapid growth in the availability of genome-wide transcript abundance levels through gene expression microarrays and RNAseq promises to provide deep biological insights into the complex, genome-wide transcriptional behavior of single-celled organisms. However, this promise has not yet been fully realized. Results: We find that computation of pairwise gene associations (correlation mutual information) across a set of 2782 total genome-wide expression samples from six diverse bacteria produces unexpectedly large variation in estimates of pairwise gene association regardless of the metric used, the organism under study, or the number and source of the samples. We pinpoint the cause to sampling bias. …