EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY

• DOI: 10.1142/9789813207813_0015
• Publication Date: 2016-11-23T01:53:50Z
• Authors: WINSTON A HAYNES; FRANCESCO VALLANIA; CHARLES LIU; ERIKA BONGEN; AURELIE TOMCZAK; MARTA ANDRES-TERRÈ; SHANE LOFGREN; ANDREW TAM; COLE A DEISSEROTH; MATTHEW D LI; TIMOTHY E SWEENEY; PURVESH KHATRI
• Keywords: Cohort Studies; 0301 basic medicine; Internet; 03 medical and health sciences; Gene Expression Profiling; Computational Biology; Humans; Reproducibility of Results; Disease; Software

A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices. We have compiled and made publicly available the results of our own multi-cohort gene expression analysis of 103 diseases, spanning 615 studies and 36,915 samples, through a novel and interactive web application. As a result, we have made both the process of and the results from multi-cohort gene expression analysis more approachable for non-technical users.