A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification.

ABSTRACT:

Background

Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.

Results

This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united ge

SUBMITTER: Xie XP 

PROVIDER: S-EPMC5568075 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature