A novel joint analysis framework improves identification of differentially expressed genes in cross disease transcriptomic analysis.

ABSTRACT:

Motivation

Detecting differentially expressed (DE) genes between disease and normal control group is one of the most common analyses in genome-wide transcriptomic data. Since most studies don't have a lot of samples, researchers have used meta-analysis to group different datasets for the same disease. Even then, in many cases the statistical power is still not enough. Taking into account the fact that many diseases share the same disease genes, it is desirable to design a statistical framework that can identify diseases' common and specific DE genes simultaneously to improve the identification power.

Results

We developed a novel empirical Bayes based mixture model to identify DE genes in specific study by leveraging the shared information across multiple different disease ex

SUBMITTER: Qin W 

PROVIDER: S-EPMC5819186 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature