Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.03.28.534514v1?rss=1
Authors: Kerseviciute, I., Gordevicius, J.
Abstract:
Summary: The interpretation of pathway enrichment analysis (PEA) results is frequently complicated by an overwhelming and redundant list of significantly affected pathways. Here, we present an R package aPEAR (Advanced Pathway Enrichment Analysis Representation) which leverages similarities between the pathway gene sets and represents them as a network of interconnected clusters. Each cluster is assigned a meaningful name which highlights the main biological themes in the experiment. Our approach enables automated and objective overview of the data without manual and time-consuming parameter tweaking. Availability and implementation: The package aPEAR is implemented in R, published under the MIT open source license. The source code, documentation, and usage instructions are available at https://github.com/ievaKer/aPEAR. Contact: [email protected] or [email protected]. Supplementary information: The complete analysis used to evaluate the package can be found at https://github.com/ievaKer/aPEAR-publication.
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