Estimating DNA methylation levels by joint modeling of multiple methylation profiles from microarray data

A Novel Joint Gene Set Analysis Framework Improves Identification of Enriched Pathways in Cross Disease Transcriptomic Analysis

Motivation: Gene set enrichment analysis is a widely accepted expression analysis tool which aims at detecting coordinated expression change within a pre-defined gene sets rather than individual genes. The benefit of gene set analysis over individual differentially expressed (DE) gene analysis includes more reproducible and interpretable results and detecting small but consistent change among gene set which could not be detected by DE gene analysis. There have been many successful gene set analysis applications in human diseases. However, when the sample size of a disease study is small and no other public data sets of the same disease are available, it will lead to lack of power to detect pathways of importance to the disease. Results: We have developed a novel joint gene set analysis statistical framework which aims at improving the power of identifying enriched gene sets through integrating multiple similar disease data sets. Through comprehensive simulation studies, we demonstrated that our proposed frameworks obtained much better AUC scores than single data set analysis and another meta-analysis method in identification of enriched pathways. When applied to two real data sets, the proposed framework could retain the enriched gene sets identified by single data set analysis and exclusively obtained up to 200% more disease-related gene sets demonstrating the improved identification power through information shared between similar diseases. We expect that the proposed framework would enable researchers to better explore public data sets when the sample size of their study is limited.

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

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 expression data sets. The effectiveness of joint analysis was demonstrated through comprehensive simulation studies and two real data applications. The simulation results showed that our method consistently outperformed single data set analysis and two other meta-analysis methods in identification power. In real data analysis, overall our method demonstrated better identification power in detecting DE genes and prioritized more disease related genes and disease related pathways than single data set analysis. Over 150% more disease related genes are identified by our method in application to Huntington’s disease. We expect that our method would provide researchers a new way of utilizing available data sets from different diseases when sample size of the focused disease is limited.

Electronic supplementary material

The online version of this article (10.1186/s13040-018-0163-y) contains supplementary material, which is available to authorized users.