EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY.

Pac Symp Biocomput

Stanford Institute for Immunity, Transplantation, and Infection, Stanford University, USA2Biomedical Informatics Training Program, Stanford University, USA3Stanford Center for Biomedical Informatics Research, Stanford University, USA.

Published: July 2017

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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5167529PMC
http://dx.doi.org/10.1142/9789813207813_0015DOI Listing

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