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. 2017:22:144-153.
doi: 10.1142/9789813207813_0015.

EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY

Winston A Haynes  1 , Francesco Vallania, Charles Liu, Erika Bongen, Aurelie Tomczak, Marta Andres-Terrè, Shane Lofgren, Andrew Tam, Cole A Deisseroth, Matthew D Li, Timothy E Sweeney, Purvesh Khatri
Affiliations

EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY

Winston A Haynes et al. Pac Symp Biocomput. 2017.

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.

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Figures

Fig. 1
Fig. 1
Gene expression meta-analysis workflow with MetaIntegrator utility functions.
Fig. 2
Fig. 2
Diagram of the MetaSignature web application.
See this image and copyright information in PMC

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