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

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

SUBMITTER: Harrington LX 

PROVIDER: S-EPMC5760988 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature