TY - JOUR T1 - Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility JF - bioRxiv DO - 10.1101/071514 SP - 071514 AU - Winston A. Haynes AU - Francesco Vallania AU - Charles Liu AU - Erika Bongen AU - Aurelie Tomczak AU - Marta Andres-Terrè AU - Shane Lofgren AU - Andrew Tam AU - Cole A. Deisseroth AU - Matthew D. Li AU - Timothy E. Sweeney AU - Purvesh Khatri Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/08/25/071514.abstract N2 - 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. ER -