Concise functional enrichment of ranked gene lists
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.06.30.547164v1?rss=1

Authors: Jia, X., Phan, A., Kadelka, C.

Abstract: Genome-wide expression data has become ubiquitous within the last two decades. Given such data, functional enrichment methods identify functional categories (e.g., biological processes) that preferentially annotate differentially expressed genes. However, many existing methods operate in a binary manner, disregarding valuable information contained in the gene ranking. The few methods that consider the ranking often return redundant or non-specific functional categories. To address these limitations, we developed a novel method called Concise Ranked Functional Enrichment (CRFE), which effectively leverages the ranking information in gene expression data to compute a non-redundant set of specific functional categories that are notably enriched for highly ranked genes. A particularly useful feature of CRFE is a tunable parameter that defines how much focus should be given to the most highly ranked genes. Using four treatment-control RNA-seq datasets, we compared the performance of CRFE with the two most widely used types of functional enrichment methods, Gene Set Enrichment Analysis and over-representation analysis. We evaluated the methods based on their ability to utilize ranking information, generate non-redundant results, and return functional categories with high information content. CRFE excelled in all evaluated criteria, outperforming the existing methods, each of which exhibits deficiencies in at least one aspect. Using lung adenocarcinoma data, we further showed that the functional categories identified by CRFE are biologically meaningful. In conclusion, CRFE computes an informative set of functional categories that summarizes genome-wide expression data. With its superior performance over existing methods, CRFE harbors great promise to become a widely used functional enrichment method.

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