首页|simplify Enrichment:A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results
simplify Enrichment:A Bioconductor Package for Clustering and Visualizing Functional Enrichment Results
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Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest.However,it may pro-duce a long list of significant terms with highly redundant information that is difficult to summarize.Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters.We pro-pose a new method named binary cut for clustering similarity matrices of functional terms.Through comprehensive benchmarks on both simulated and real-world datasets,we demonstrated that bi-nary cut could efficiently cluster functional terms into groups where terms showed consistent sim-ilarities within groups and were mutually exclusive between groups.We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with binary cut,while similarity matrices based on gene overlap showed less consistent patterns.We implemented the binary cut algorithm in the R package sim-plifyEnrichment,which additionally provides functionalities for visualizing,summarizing,and com-paring the clustering.The simplify Enrichment package and the documentation are available at https://bioconductor.org/packages/simplifyEnrichment/.
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