Title | Empowering Multi-Cohort Gene Expression Analysis to Increase Reproducibility |
Publication Type | Manuscript |
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 |
Volume/Storage Container | 22 |
Pagination | 144–153 |
Date Published | December, 2016 |
Abstract | 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
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Jul 13 2017 - 2:58pm