clusterProfiler: A universal enrichment tool for ...

1 Package clusterProfiler December 16, 2022 TypePackageTitleA universal enrichment tool for interpreting omics package supports functional characteristics of both coding and non-coding ge-nomics data for thousands of species with up-to-date gene annotation. It provides a univeral in-terface for gene functional annotation from a variety of sources and thus can be applied in di-verse scenarios. It provides a tidy interface to access, manipulate, and visualize enrichment re-sults to help users achieve efficient data interpretation. Datasets obtained from multiple treat-ments and time points can be analyzed and compared in a single run, easily revealing func-tional consensus and differences among distinct (>= )ImportsAnnotationDbi, downloader, DOSE (>= ), dplyr, enrichplot(>= ), , GOSemSim, gson (>= ), magrittr,methods, plyr, qvalue, rlang, stats, tidyr, utils, , knitr, rmarkdown, , prettydoc,ReactomePA, (docs), (paper) , Clustering, GeneSetEnrichment, GO, KEGG,MultipleComparison, Pathways, Reactome, documented:git_last_commit_date2022-11-0 1 Date/Publication2022-12-16 AuthorGuangchuang Yu [aut, cre, cph](< >),Li-Gen Wang [ctb],Erqiang Hu [ctb],Xiao Luo [ctb],Meijun Chen [ctb],Giovanni Dall'Olio [ctb],Wanqian Wei [ctb],Chun-Hui Gao [ctb] (< >)Rtopics documented:clusterProfiler-package.

2 3bitr ..3bitr_kegg ..4browseKEGG ..4buildGOmap ..5compareCluster ..6 DataSet ..7download_KEGG ..7dropGO ..8enrichDAVID ..8enricher .. 10enrichGO .. 11enrichKEGG .. 12enrichMKEGG .. 14enrichWP .. 15get_wp_organisms .. 15 Gff2 GeneTable .. 16go2ont .. 17go2term .. 17gofilter .. 18groupGO .. 18groupGOResult-class .. 19 GSEA .. 20gseGO .. 21gseKEGG .. 22gseMKEGG .. 23gseWP .. 25gson_GO .. 25gson_KEGG .. 26gson_KEGG_mapper .. 27gson_WP .. 28idType .. 28ko2name .. 29clusterProfiler-package3merge_result .. 29parse_gff .. 30plotGOgraph .. 30search_kegg_organism .. 31simplify .. 32uniprot_get .. 33 Index34clusterProfiler-packagestatistica l analysis and visualization of functional profiles for genesand gene clustersDescriptionThe package implements methods to analyze and visualize functional profiles of gene and Id TRanslatorUsagebitr(geneID, fromType, toType, OrgDb, drop = TRUE)ArgumentsgeneIDinput gene idfromTypeinput id typetoTypeoutput id typeOrgDbannotation dbdropdrop NA or (s)Guangchuang Yu4browseKEGG bitr_keggbitr_keggDescriptionconvert biological ID using KEGG APIU sagebitr_kegg(geneID, fromType, toType, organism, drop = TRUE)ArgumentsgeneIDinput gene idfromTypeinput id typetoTypeoutput id typeorganismsupported organism, can be search using search_kegg_organism functiondropdrop NA or (s)Guangchuang YubrowseKEGG browseKEGGD escriptionopen KEGG pathway with web browserUsagebrowseKEGG(x, pathID)Argumentsxan instance of enrichResult or gseaResultpathIDpathway IDbuildGOmap5 ValueurlAuthor(s)

3 Guangchuang YubuildGOmapbuildGOmapDescriptionbuildin g GO mapping filesUsagebuildGOmap(gomap) with two columns of GO and gene IDDetailsprovided by a of GO (column 1) and gene (column 2) direct annotation this functionwill building gene to GO and GO to gene mapping, with directly and undirectly (ancestor GO term) , GO annotation with indirect annotationAuthor(s)Yu Guangchuang6compareClustercompareCluster Compare gene clusters functional profileDescriptionGiven a list of gene set, this function will compute profiles of each gene (geneClusters,fun = "enrichGO",data = "",source_from = NULL,..)ArgumentsgeneClustersa list of entrez gene id. Alternatively, a formula of typeEntrez~groupor a for-mula of typeEntrez | logFC ~ groupfor "gseGO", "gseKEGG" and "GSEA".funOne of "groupGO", "enrichGO", "enrichKEGG", "enrichDO" or "enrichPath-way" . Users can also supply their own geneClusters is a formula, the data from which the clusters must be using a custom function in "fun", provide the source package as a string , the function will be obtained from the global (s)Guangchuang AlsocompareClusterResult-class,groupGO enrichGOExamples## Not run.

4 Data(gcSample)xx <- compareCluster(gcSample, fun="enrichKEGG",organism="hsa", pvalueCutoff= ) (xx)DataSet7# plot(xx, type="dot", caption="KEGG enrichment Comparison")dotplot(xx)## formula interfacemydf <- (Entrez=c('1','100','1000','100101467',' 100127206','100128071'),logFC = c( , , 5, , -3, 3),group = c('A','A','A','B','B','B'),othergroup = c('good','good','bad','bad','good','bad' )) <- compareCluster(Entrez~group, data=mydf,fun='groupGO', OrgDb=' ') ( )## formula interface with more than one grouping <- compareCluster(Entrez~group+othergroup, data=mydf,fun='groupGO', OrgDb=' ') ( )## End(Not run)DataSetDatasets gcSample contains a sample of gene gcSample contains a sample of gene kegg_species contains kegg species informationDatasets DE_GSE8057 contains differential epxressed genes obtained from GSE8057 datasetdownload_KEGG download_KEGGD escriptiondownload the latest version of KEGG pathway/moduleUsagedownload_KEGG(species , keggType = "KEGG", keyType = "kegg")ArgumentsspeciesspecieskeggTypeon e of KEGG or MKEGG keyTypesupported keyType, see bitr_kegg8enrichDAVIDV aluelistAuthor(s)Guangchuang YudropGOdropGODescriptiondrop GO term of specific level or specific terms (mostly too general).

5 UsagedropGO(x, level = NULL, term = NULL)Argumentsxan instance of enrichResult or compareClusterResult levelGO leveltermGO termValuemodified version of xAuthor(s)Guangchuang YuenrichDAVID enrichDAVIDD escriptionenrichment analysis by DAVID enrichDAVID9 UsageenrichDAVID(gene,idType = "ENTREZ_GENE_ID",universe,minGSSize = 10,maxGSSize = 500,annotation = "GOTERM_BP_FAT",pvalueCutoff = ,pAdjustMethod = "BH",qvalueCutoff = ,species = NA, )Argumentsgeneinput geneidTypeid typeuniversebackground genes. If missing, the all genes listed in the database (eg TERM2 GENE table) will be used as size of genes annotated for testingmaxGSSizemaximal size of genes annotated for testingannotationdavid annotationpvalueCutoffadjusted pvalue cutoff on enrichment tests to reportpAdjustMethodone of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"qvalueCutoffqvalue cutoff on enrichment tests to report as significant. Tests must pass i)pvalueCutoffon unadjusted pvalues, ii)pvalueCutoffon adjusted pvaluesand iii)qvalueCutoffon qvalues to be userValueAenrichResultinstanceAuthor(s)G uangchuang Yu10enricherenricherenricherDescriptionA universal enrichment analyzerUsageenricher(gene,pvalueCutoff = ,pAdjustMethod = "BH",universe = NULL,minGSSize = 10,maxGSSize = 500,qvalueCutoff = ,gson = NULL,TERM2 GENE,TERM2 NAME = NA)Argumentsgenea vector of gene idpvalueCutoffadjusted pvalue cutoff on enrichment tests to reportpAdjustMethodone of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"universebackground genes.

6 If missing, the all genes listed in the database (eg TERM2 GENE table) will be used as size of genes annotated for testingmaxGSSizemaximal size of genes annotated for testingqvalueCutoffqvalue cutoff on enrichment tests to report as significant. Tests must pass i)pvalueCutoffon unadjusted pvalues, ii)pvalueCutoffon adjusted pvaluesand iii)qvalueCutoffon qvalues to be GSON object, if not NULL, use it as annotation input annotation of TERM TO GENE mapping, a of 2 columnwith term and gene. Only used when gson is input of TERM TO NAME mapping, a of 2 column with termand name. Only used when gson is (s)Guangchuang enrichment Analysis of a gene set. Given a vector of genes, thisfunction will return the enrichment GO categories after FDR enrichment Analysis of a gene set. Given a vector of genes, this function will return theenrichment GO categories after FDR (gene,OrgDb,keyType = "ENTREZID",ont = "MF",pvalueCutoff = ,pAdjustMethod = "BH",universe,qvalueCutoff = ,minGSSize = 10,maxGSSize = 500,readable = FALSE,pool = FALSE)Argumentsgenea vector of entrez gene of input geneontOne of "BP", "MF", and "CC" subontologies, or "ALL" for all pvalue cutoff on enrichment tests to reportpAdjustMethodone of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"universebackground genes.

7 If missing, the all genes listed in the database (eg TERM2 GENE table) will be used as cutoff on enrichment tests to report as significant. Tests must pass i)pvalueCutoffon unadjusted pvalues, ii)pvalueCutoffon adjusted pvaluesand iii)qvalueCutoffon qvalues to be size of genes annotated by Ontology term for size of genes annotated for testingreadablewhether mapping gene ID to gene NamepoolIf ont= ALL , whether pool 3 GO (s)Guangchuang AlsoenrichResult-class,compareClusterExa mples## Not run:data(geneList, package = "DOSE")de <- names(geneList)[1:100]yy <- enrichGO(de,' ', ont="BP", pvalueCutoff= )head(yy)## End(Not run)enrichKEGGKEGG enrichment Analysis of a gene set. Given a vector of genes,this function will return the enrichment KEGG categories with enrichment Analysis of a gene set. Given a vector of genes, this function will return theenrichment KEGG categories with FDR (gene,organism = "hsa",keyType = "kegg",pvalueCutoff = ,pAdjustMethod = "BH",universe,minGSSize = 10,maxGSSize = 500,qvalueCutoff = ,use_internal_data = FALSE)enrichKEGG13 Argumentsgenea vector of entrez gene organism listed in keyTypeone of "kegg", ncbi-geneid , ncib-proteinid and uniprot pvalueCutoffadjusted pvalue cutoff on enrichment tests to reportpAdjustMethodone of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"universebackground genes.

8 If missing, the all genes listed in the database (eg TERM2 GENE table) will be used as size of genes annotated by Ontology term for size of genes annotated for testingqvalueCutoffqvalue cutoff on enrichment tests to report as significant. Tests must pass i)pvalueCutoffon unadjusted pvalues, ii)pvalueCutoffon adjusted pvaluesand iii)qvalueCutoffon qvalues to be , use or latest online KEGG (s)Guangchuang AlsoenrichResult-class,compareClusterExa mples## Not run:data(geneList, package='DOSE')de <- names(geneList)[1:100]yy <- enrichKEGG(de, pvalueCutoff= )head(yy)## End(Not run)14enrichMKEGG enrichMKEGGKEGG Module enrichment Analysis of a gene set. Given a vector ofgenes, this function will return the enrichment KEGG Module cate-gories with FDR Module enrichment Analysis of a gene set. Given a vector of genes, this function will returnthe enrichment KEGG Module categories with FDR (gene,organism = "hsa",keyType = "kegg",pvalueCutoff = ,pAdjustMethod = "BH",universe,minGSSize = 10,maxGSSize = 500,qvalueCutoff = )Argumentsgenea vector of entrez gene organism listed in keyTypeone of "kegg", ncbi-geneid , ncib-proteinid and uniprot pvalueCutoffadjusted pvalue cutoff on enrichment tests to reportpAdjustMethodone of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"universebackground genes.

9 If missing, the all genes listed in the database (eg TERM2 GENE table) will be used as size of genes annotated by Ontology term for size of genes annotated for testingqvalueCutoffqvalue cutoff on enrichment tests to report as significant. Tests must pass i)pvalueCutoffon unadjusted pvalues, ii)pvalueCutoffon adjusted pvaluesand iii)qvalueCutoffon qvalues to be analysis for WikiPathwaysUsageenrichWP(gene, organism, ..)Argumentsgenea vector of entrez gene idorganismsupported organisms, which can be accessed via the get_wp_organisms() parameters, see also the parameters supported by the enricher() func-tionDetailsThis function performs over-representation analysis using WikiPathwaysValueAenrichResultinstanceAu thor(s)Guangchuang Yuget_wp_organismsget_wp_organismDescrip tionlist supported organism of WikiPathwaysUsageget_wp_organisms()Detai lsThis function extracts information from and lists allsupported organisms16 Gff2 GeneTableValuesupported organism listAuthor(s)Guangchuang YuGff2 GeneTableGff2 GeneTableDescriptionread GFF file and build gene information tableUsageGff2 GeneTable(gffFile, compress = TRUE)ArgumentsgffFileGFF filecompresscompress file or notDetailsgiven a GFF file, this function extracts information from it and save it in working directoryValuefile (s)Yu Guangchuanggo2ont17go2ontgo2ontDescripti onconvert goid to ontology (BP, CC, MF)

10 Usagego2ont(goid)Argumentsgoida vector of GO (s)Guangchuang Yugo2termgo2termDescriptionconvert goid to descriptive termUsagego2term(goid)Argumentsgoida vector of GO (s)Guangchuang Yu18groupGOgofiltergofilterDescriptionfi lter GO enriched result at specific levelUsagegofilter(x, level = 4)Argumentsxoutput from enrichGO or compareClusterlevelGO levelValueupdated objectAuthor(s)Guangchuang YugroupGOFunctional Profile of a gene set at specific GO level. Given a vector ofgenes, this function will return the GO profile at a specific Profile of a gene set at specific GO level. Given a vector of genes, this function willreturn the GO profile at a specific (gene,OrgDb,keyType = "ENTREZID",ont = "CC",level = 2,readable = FALSE)groupGOResult-class19 Argumentsgenea vector of entrez gene type of input geneontOne of "MF", "BP", and "CC" GO readable is TRUE, the gene IDs will mapping to gene (s)Guangchuang AlsogroupGOResult-class,compareClusterEx amplesdata(gcSample)yy <- groupGO(gcSample[[1]],' ', ont="BP", level=2)head(summary(yy))#plot(yy)groupG OResult-classClass "groupGOResult" This class represents the result of functionalProfiles of a set of gene at specific GO "groupGOResult" This class represents the result of functional Profiles of a set of gene atspecific GO classification resultontologyOntologylevelGO levelorganismone of "human", "mouse" and "yeast"geneGene IDsreadablelogical flag of gene ID in symbol or (s)