|Title||Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility|
|Year of Publication||2016|
|Authors||Haynes 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 Title||Pacific Symposium on Biocomputing|
|Date Published||December, 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.
Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility
Jul 13 2017 - 2:58pm