Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility

TitleEmpowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility
Publication TypeManuscript
Year of Publication2016
AuthorsHaynes WA, Vallania F, Liu C, Bongen E, Tomczak A, Andres-Terrè M, Lofgren S, Tam A, Deisseroth CA, Li MD, Sweeney TE, Khatri P
Collection TitlePacific Symposium on Biocomputing
Volume/Storage Container22
Date PublishedDecember, 2016

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.

Last Updated: 
Jul 13 2017 - 2:58pm