PT  - JOURNAL ARTICLE
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
TI  - Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility
AID  - 10.1101/071514
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 071514
4099  - http://biorxiv.org/content/early/2016/08/25/071514.short
4100  - http://biorxiv.org/content/early/2016/08/25/071514.full
AB  - 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.