![]() ![]() The clusterProfiler library was first published in 2012 7 and designed to perform over-representation analysis (ORA) 8 using GO and KEGG for several model organisms and to compare functional profiles of various conditions on one level (e.g., different treatment groups). ![]() Reanalyzing the GTEx dataset 6 published by the ENCODE consortium using clusterProfiler uncovered a large number of new pathways, which were missed in the analysis using out-of-date annotation ( ), and new hypotheses were generated based on these new pathways. Such negative impacts of outdated annotation can be propagated for years and can hinder follow-up studies. A previous study 5 reported that about 42% of the tools were outdated by more than 5 years and that functional significance was severely underestimated, with only 26% of biological processes or pathways captured in comparison with those employing up-to-date annotation. Because annotation databases have diverse or irregular update periods, many tools may fail to update the corresponding information in time. Moreover, the increasing concerns for the quality of gene annotation have raised an alarm in biomedical research. 4 Non-model organisms and functional annotations other than GO and KEGG are poorly supported. Although many tools have been developed for gene-centric or epigenomic enrichment analysis, most are designed for model organisms or specific domains (e.g., fungi, 2 plants 3) embedded with particular annotations such as Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.įunctional enrichment analysis is one of the most widely used techniques for interpreting gene lists or genome-wide regions of interest (ROIs) 1 derived from various high-throughput studies. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. This package has been enhanced considerably compared with its original version published 9 years ago. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. Package in your R session.Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. Statistical analysis and visualization of functional profiles for genes and gene clustersĪnnotation, Clustering, GO, GeneSetEnrichment, KEGG, MultipleComparison, Pathways, Reactome, Software, VisualizationĪnnotationDbi, downloader, DOSE(>= 3.23.2), dplyr, enrichplot(>= 1.9.3), GO.db, GOSemSim, gson (>= 0.0.7), magrittr, methods, plyr, qvalue, rlang, stats, tidyr, utils, yulab.utilsĪnnotationHub, knitr, rmarkdown, org.Hs.eg.db, prettydoc, ReactomePA, testthatīioCancer, CEMiTool, CeTF, conclus, debrowser, Eas圜ellType, eegc, enrichTF, esATAC, ExpHunterSuite, famat, fcoex, GDCRNATools, goSorensen, IRISFGM, MAGeCKFlute, MetaPhOR, methylGSA, MicrobiomeProfiler, miRspongeR, MoonlightR, multiSight, netboxr, PanomiR, PFP, Pigengene, recountWorkflow, RNASeqR, signatureSearch, TCGAbiolinksGUI, TCGAWorkflow, TimiRGeNĬhIPseeker, cola, DAPAR, DOSE, enrichplot, EpiCompare, epihet, EpiMix, GeneTonic, GenomicSuperSignature, GOSemSim, GRaNIE, GSEAmining, MesKit,, paxtoolsr, ReactomePA, rrvgo, scGPS, simplifyEnrichment, TCGAbiolinks, tidybulk, vsclust To view documentation for the version of this package installed If (!require("BiocManager", quietly = TRUE))įor older versions of R, please refer to the appropriate To install this package, start R (version
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