Statistically controlled identification of differentially expressed genes in one-to-one cell line comparisons of the CMAP database for drug repositioning.

ABSTRACT:

Background

The Connectivity Map (CMAP) database, an important public data source for drug repositioning, archives gene expression profiles from cancer cell lines treated with and without bioactive small molecules. However, there are only one or two technical replicates for each cell line under one treatment condition. For such small-scale data, current fold-changes-based methods lack statistical control in identifying differentially expressed genes (DEGs) in treated cells. Especially, one-to-one comparison may result in too many drug-irrelevant DEGs due to random experimental factors. To tackle this problem, CMAP adopts a pattern-matching strategy to build "connection" between disease signatures and gene expression changes associated with drug treatments. However, many drug-irrelev

SUBMITTER: He J 

PROVIDER: S-EPMC5622488 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature