An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments.

ABSTRACT: A common goal of microarray and related high-throughput genomic experiments is to identify genes that vary across biological condition. Most often this is accomplished by identifying genes with changes in mean expression level, so called differentially expressed (DE) genes, and a number of effective methods for identifying DE genes have been developed. Although useful, these approaches do not accommodate other types of differential regulation. An important example concerns differential coexpression (DC). Investigations of this class of genes are hampered by the large cardinality of the space to be interrogated as well as by influential outliers. As a result, existing DC approaches are often underpowered, exceedingly prone to false discoveries, and/or computationally intractable for even a

SUBMITTER: Dawson JA 

PROVIDER: S-EPMC3482945 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature