Functional network community detection can disaggregate and filter multiple underlying pathways in enrichment analyses

Lia X. Harrington1, Gregory P. Way2, Jennifer A. Doherty3, Casey S. Greene2


1Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College
2Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania
3Huntsman Cancer Institute, Population Health Sciences, University of Utah
Email: lia.harrington.gr@dartmouth.edu, gregway@mail.med.upenn.edu, jen.doherty@hci.utah.edu, csgreene@mail.med.upenn.edu

Pacific Symposium on Biocomputing 23:157-167(2018)

© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.


Abstract

Differential expression experiments or other analyses often end in a list of genes. Pathway enrichment analysis is one method to discern important biological signals and patterns from noisy expression data. However, pathway enrichment analysis may perform suboptimally in situations where there are multiple implicated pathways — such as in the case of genes that define subtypes of complex diseases. Our simulation study shows that in this setting, standard overrepresentation analysis identifies many false positive pathways along with the true positives. These false positives hamper investigators' attempts to glean biological insights from enrichment analysis. We develop and evaluate an approach that combines community detection over functional networks with pathway enrichment to reduce false positives. Our simulation study demonstrates that a large reduction in false positives can be obtained with a small decrease in power. Though we hypothesized that multiple communities might underlie previously described subtypes of high-grade serous ovarian cancer and applied this approach, our results do not support this hypothesis. In summary, applying community detection before enrichment analysis may ease interpretation for complex gene sets that represent multiple distinct pathways.


[Full-Text PDF] [PSB Home Page]