Package clusterprofiler

Transcription

1 Type Package Package clusterprofiler January 22, 2018 Title statistical analysis and visualization of functional profiles for genes and gene clusters Version Maintainer This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters. Depends R (>= 3.3.1), DOSE (>= 3.3.2) Imports AnnotationDbi, ggplot2, GO.db, GOSemSim (>= 2.0.0), magrittr, methods, plyr, qvalue, rvcheck, stats, stats4, tidyr, utils Suggests AnnotationHub, GSEABase, KEGG.db, knitr, org.hs.eg.db, prettydoc, pathview, ReactomePA, testthat, topgo VignetteBuilder knitr ByteCompile true License Artistic-2.0 URL BugReports biocviews Annotation, Clustering, GeneSetEnrichment, GO, KEGG, MultipleComparison, Pathways, Reactome, Visualization RoxygenNote NeedsCompilation no Author [aut, cre, cph] (< Li-Gen Wang [ctb], Giovanni Dall'Olio [ctb] (formula interface of comparecluster) R topics documented: clusterprofiler-package bitr bitr_kegg browsekegg buildgomap comparecluster

2 2 clusterprofiler-package compareclusterresult-class DataSet dotplot,compareclusterresult-method download_kegg dropgo enrichdavid enricher enrichgo enrichkegg enrichmkegg fortify.compareclusterresult getgolevel Gff2GeneTable go2ont go2term gofilter groupgo groupgoresult-class GSEA gsego gsekegg gsemkegg idtype KEGG_path2extid ko2name merge_result plot plotgograph plotting.clusterprofile read.gmt search_kegg_organism simplify viewkegg Index 28 clusterprofiler-package statistical analysis and visualization of functional profiles for genes and gene clusters The package implements methods to analyze and visualize functional profiles of gene and gene clusters. statistical analysis and visualization of functional profiles for genes and gene clusters The package implements methods to analyze and visualize functional profiles of gene and gene clusters.

3 bitr 3 bitr bitr Biological Id TRanslator bitr(geneid, fromtype, totype, OrgDb, drop = TRUE) geneid fromtype totype OrgDb drop input gene id input id type output id type annotation db drop NA or not data.frame bitr_kegg bitr_kegg convert biological ID using KEGG API bitr_kegg(geneid, fromtype, totype, organism, drop = TRUE) geneid fromtype totype organism drop input gene id input id type output id type supported organism, can be search using search_kegg_organism function drop NA or not data.frame

4 4 buildgomap browsekegg browsekegg open KEGG pathway with web browser browsekegg(x, pathid) x pathid an instance of enrichresult or gsearesult pathway ID url buildgomap buildgomap building GO mapping files buildgomap(gomap) gomap data.frame with two columns of GO and gene ID Details provided by a data.frame of GO (column 1) and gene (column 2) direct annotation this function will building gene to GO and GO to gene mapping, with directly and undirectly (ancestor GO term) annotation. data.frame, GO annotation with indirect annotation

5 comparecluster 5 Yu Guangchuang comparecluster Compare gene clusters functional profile Given a list of gene set, this function will compute profiles of each gene cluster. comparecluster(geneclusters, fun = "enrichgo", data = "",...) geneclusters fun data... Other arguments. A clusterprofresult instance. See Also a list of entrez gene id. Alternatively, a formula of type Entrez~group One of "groupgo", "enrichgo", "enrichkegg", "enrichdo" or "enrichpathway". if geneclusters is a formula, the data from which the clusters must be extracted. compareclusterresult-class, groupgo enrichgo Examples ## Not run: data(gcsample) xx <- comparecluster(gcsample, fun="enrichkegg", organism="hsa", pvaluecutoff=0.05) as.data.frame(xx) # plot(xx, type="dot", caption="kegg Enrichment Comparison") ## formula interface mydf <- data.frame(entrez=c('1', '100', '1000', ' ', ' ', ' '), group = c('a', 'A', 'A', 'B', 'B', 'B'), othergroup = c('good', 'good', 'bad', 'bad', 'good', 'bad')) xx.formula <- comparecluster(entrez~group, data=mydf, fun='groupgo', OrgDb='org.Hs.eg.db') as.data.frame(xx.formula)

6 6 DataSet ## formula interface with more than one grouping variable xx.formula.twogroups <- comparecluster(entrez~group+othergroup, data=mydf, fun='groupgo', OrgDb='org.Hs.eg.db') as.data.frame(xx.formula.twogroups) ## End(Not run) compareclusterresult-class Class "compareclusterresult" This class represents the comparison result of gene clusters by GO categories at specific level or GO enrichment analysis. Class "compareclusterresult" This class represents the comparison result of gene clusters by GO categories at specific level or GO enrichment analysis. Slots compareclusterresult cluster comparing result geneclusters a list of genes fun one of groupgo, enrichgo and enrichkegg.call function call See Also groupgoresult enrichresult comparecluster DataSet Datasets gcsample contains a sample of gene clusters. Datasets gcsample contains a sample of gene clusters. Datasets kegg_species contains kegg species information

7 dotplot,compareclusterresult-method 7 dotplot,compareclusterresult-method dotplot dot plot method ## S4 method for signature 'compareclusterresult' dotplot(object, x = ~Cluster, colorby = "p.adjust", showcategory = 5, by = "generatio", split = NULL, includeall = TRUE, font.size = 12, title = "") object x colorby showcategory by split includeall font.size title compareclusterresult object x variable one of pvalue or p.adjust category numbers one of generatio, Percentage or count ONTOLOGY or NULL logical font size figure title download_kegg download_kegg download the latest version of KEGG pathway/module download_kegg(species, keggtype = "KEGG", keytype = "kegg") species keggtype keytype species one of KEGG or MKEGG supported keytype, see bitr_kegg list

8 8 enrichdavid dropgo dropgo drop GO term of specific level or specific terms (mostly too general). dropgo(x, level = NULL, term = NULL) x level term an instance of enrichresult or compareclusterresult GO level GO term modified version of x enrichdavid enrichdavid enrichment analysis by DAVID enrichdavid(gene, idtype = "ENTREZ_GENE_ID", universe, mingssize = 10, maxgssize = 500, annotation = "GOTERM_BP_FAT", pvaluecutoff = 0.05, padjustmethod = "BH", qvaluecutoff = 0.2, species = NA, david.user)

9 enricher 9 gene idtype universe mingssize maxgssize annotation pvaluecutoff padjustmethod qvaluecutoff species david.user input gene id type background genes minimal size of genes annotated for testing maximal size of genes annotated for testing david annotation pvaluecutoff one of "BH" and "bonferroni" qvalutcutoff species david user A enrichresult instance enricher enricher A universal enrichment analyzer enricher(gene, pvaluecutoff = 0.05, padjustmethod = "BH", universe, mingssize = 10, maxgssize = 500, qvaluecutoff = 0.2, TERM2GENE, TERM2NAME = NA) gene pvaluecutoff padjustmethod universe mingssize maxgssize qvaluecutoff TERM2GENE TERM2NAME a vector of gene id pvalue cutoff one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" background genes minimal size of genes annotated for testing maximal size of genes annotated for testing qvalue cutoff user input annotation of TERM TO GENE mapping, a data.frame of 2 column with term and gene user input of TERM TO NAME mapping, a data.frame of 2 column with term and name

10 10 enrichgo A enrichresult instance enrichgo GO Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment GO categories after FDR control. GO Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment GO categories after FDR control. enrichgo(gene, OrgDb, keytype = "ENTREZID", ont = "MF", pvaluecutoff = 0.05, padjustmethod = "BH", universe, qvaluecutoff = 0.2, mingssize = 10, maxgssize = 500, readable = FALSE, pool = FALSE) gene OrgDb keytype ont pvaluecutoff padjustmethod universe qvaluecutoff mingssize maxgssize readable pool a vector of entrez gene id. OrgDb keytype of input gene One of "MF", "BP", and "CC" subontologies. Cutoff value of pvalue. one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" background genes qvalue cutoff minimal size of genes annotated by Ontology term for testing. maximal size of genes annotated for testing whether mapping gene ID to gene Name If ont= ALL, whether pool 3 GO sub-ontologies An enrichresult instance. See Also enrichresult-class, comparecluster

11 enrichkegg 11 Examples ## Not run: data(genelist) de <- names(genelist)[1:100] yy <- enrichgo(de, 'org.hs.eg.db', ont="bp", pvaluecutoff=0.01) head(yy) ## End(Not run) enrichkegg KEGG Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment KEGG categories with FDR control. KEGG Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment KEGG categories with FDR control. enrichkegg(gene, organism = "hsa", keytype = "kegg", pvaluecutoff = 0.05, padjustmethod = "BH", universe, mingssize = 10, maxgssize = 500, qvaluecutoff = 0.2, use_internal_data = FALSE) gene organism keytype pvaluecutoff padjustmethod universe mingssize maxgssize a vector of entrez gene id. supported organism listed in one of "kegg", ncbi-geneid, ncib-proteinid and uniprot Cutoff value of pvalue. one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" background genes minimal size of genes annotated by Ontology term for testing. maximal size of genes annotated for testing qvaluecutoff qvalue cutoff use_internal_data logical, use KEGG.db or latest online KEGG data A enrichresult instance. See Also enrichresult-class, comparecluster

12 12 enrichmkegg Examples data(genelist) de <- names(genelist)[1:100] yy <- enrichkegg(de, pvaluecutoff=0.01) head(yy) #plot(yy) enrichmkegg KEGG Module Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment KEGG Module categories with FDR control. KEGG Module Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment KEGG Module categories with FDR control. enrichmkegg(gene, organism = "hsa", keytype = "kegg", pvaluecutoff = 0.05, padjustmethod = "BH", universe, mingssize = 10, maxgssize = 500, qvaluecutoff = 0.2) gene organism keytype pvaluecutoff padjustmethod universe mingssize maxgssize qvaluecutoff a vector of entrez gene id. supported organism listed in one of "kegg", ncbi-geneid, ncib-proteinid and uniprot Cutoff value of pvalue. one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" background genes minimal size of genes annotated by Ontology term for testing. maximal size of genes annotated for testing qvalue cutoff A enrichresult instance.

13 fortify.compareclusterresult 13 fortify.compareclusterresult fortify convert compareclusterresult to a data.frame that ready for plot ## S3 method for class 'compareclusterresult' fortify(model, data, showcategory = 5, by = "generatio", split = NULL, includeall = TRUE) model data showcategory by split includeall compareclusterresult object not use here category numbers one of generatio, Percentage or count ONTOLOGY or NULL logical data.frame getgolevel get GOIDs at a specific level query GOIDs at a specific level. getgolevel(ont, level) ont level Ontology GO level

14 14 go2ont a vector of GOIDs Gff2GeneTable Gff2GeneTable read GFF file and build gene information table Gff2GeneTable(gffFile, compress = TRUE) gfffile compress GFF file compress file or not Details given a GFF file, this function extracts information from it and save it in working directory file save. Yu Guangchuang go2ont go2ont convert goid to ontology (BP, CC, MF) go2ont(goid) goid a vector of GO IDs

15 go2term 15 data.frame go2term go2term convert goid to descriptive term go2term(goid) goid a vector of GO IDs data.frame gofilter gofilter filter GO enriched result at specific level gofilter(x, level = 4) x level output from enrichgo or comparecluster GO level updated object

16 16 groupgo groupgo Functional Profile of a gene set at specific GO level. Given a vector of genes, this function will return the GO profile at a specific level. Functional Profile of a gene set at specific GO level. Given a vector of genes, this function will return the GO profile at a specific level. groupgo(gene, OrgDb, keytype = "ENTREZID", ont = "CC", level = 2, readable = FALSE) gene OrgDb keytype ont level readable a vector of entrez gene id. OrgDb key type of input gene One of "MF", "BP", and "CC" subontologies. Specific GO Level. if readable is TRUE, the gene IDs will mapping to gene symbols. A groupgoresult instance. See Also groupgoresult-class, comparecluster Examples data(gcsample) yy <- groupgo(gcsample[[1]], 'org.hs.eg.db', ont="bp", level=2) head(summary(yy)) #plot(yy)

17 groupgoresult-class 17 groupgoresult-class Class "groupgoresult" This class represents the result of functional Profiles of a set of gene at specific GO level. Slots Class "groupgoresult" This class represents the result of functional Profiles of a set of gene at specific GO level. result GO classification result ontology Ontology level GO level organism one of "human", "mouse" and "yeast" gene Gene IDs readable logical flag of gene ID in symbol or not. See Also compareclusterresult comparecluster groupgo GSEA GSEA a universal gene set enrichment analysis tools GSEA(geneList, exponent = 1, nperm = 1000, mingssize = 10, maxgssize = 500, pvaluecutoff = 0.05, padjustmethod = "BH", TERM2GENE, TERM2NAME = NA, verbose = TRUE, seed = FALSE, by = "fgsea") genelist exponent nperm mingssize maxgssize pvaluecutoff order ranked genelist weight of each step number of permutations minimal size of each geneset for analyzing maximal size of genes annotated for testing pvalue cutoff

18 18 gsego padjustmethod TERM2GENE TERM2NAME verbose seed by p value adjustment method user input annotation of TERM TO GENE mapping, a data.frame of 2 column with term and gene user input of TERM TO NAME mapping, a data.frame of 2 column with term and name logical logical one of fgsea or DOSE gsearesult object gsego gsego Gene Set Enrichment Analysis of Gene Ontology gsego(genelist, ont = "BP", OrgDb, keytype = "ENTREZID", exponent = 1, nperm = 1000, mingssize = 10, maxgssize = 500, pvaluecutoff = 0.05, padjustmethod = "BH", verbose = TRUE, seed = FALSE, by = "fgsea") genelist ont OrgDb keytype exponent nperm mingssize maxgssize pvaluecutoff padjustmethod verbose seed by order ranked genelist one of "BP", "MF", "CC" or "GO" OrgDb keytype of gene weight of each step permutation numbers minimal size of each geneset for analyzing maximal size of genes annotated for testing pvalue Cutoff pvalue adjustment method print message or not logical one of fgsea or DOSE

19 gsekegg 19 gsearesult object Yu Guangchuang gsekegg gsekegg Gene Set Enrichment Analysis of KEGG gsekegg(genelist, organism = "hsa", keytype = "kegg", exponent = 1, nperm = 1000, mingssize = 10, maxgssize = 500, pvaluecutoff = 0.05, padjustmethod = "BH", verbose = TRUE, use_internal_data = FALSE, seed = FALSE, by = "fgsea") genelist organism keytype exponent nperm mingssize maxgssize pvaluecutoff padjustmethod order ranked genelist supported organism listed in one of "kegg", ncbi-geneid, ncib-proteinid and uniprot weight of each step permutation numbers minimal size of each geneset for analyzing maximal size of genes annotated for testing pvalue Cutoff pvalue adjustment method verbose print message or not use_internal_data logical, use KEGG.db or latest online KEGG data seed by logical one of fgsea or DOSE gsearesult object Yu Guangchuang

20 20 gsemkegg gsemkegg gsemkegg Gene Set Enrichment Analysis of KEGG Module gsemkegg(genelist, organism = "hsa", keytype = "kegg", exponent = 1, nperm = 1000, mingssize = 10, maxgssize = 500, pvaluecutoff = 0.05, padjustmethod = "BH", verbose = TRUE, seed = FALSE, by = "fgsea") genelist organism keytype exponent nperm mingssize maxgssize pvaluecutoff padjustmethod verbose seed by order ranked genelist supported organism listed in one of "kegg", ncbi-geneid, ncib-proteinid and uniprot weight of each step permutation numbers minimal size of each geneset for analyzing maximal size of genes annotated for testing pvalue Cutoff pvalue adjustment method print message or not logical one of fgsea or DOSE gsearesult object Yu Guangchuang

21 idtype 21 idtype idtype list ID types supported by annodb idtype(orgdb = "org.hs.eg.db") OrgDb annotation db character vector KEGG_path2extid KEGG_path2extid query all genes in a KEGG pathway or module KEGG_path2extid(keggID, species = sub("\\d+$", "", keggid), keggtype = "Path", keytype = "kegg") keggid species keggtype keytype KEGG ID, path or module ID species one of Path or Module KEGG gene type, one of "ncbi-proteinid", "ncbi-geneid", "uniprot", or "kegg" extid vector guangchuang yu

22 22 merge_result ko2name ko2name convert ko ID to descriptive name ko2name(ko) ko ko ID data.frame guangchuang yu merge_result merge_result merge a list of enrichresult objects to compareclusterresult merge_result(enrichresultlist) enrichresultlist a list of enrichresult objects a compareclusterresult instance

23 plot 23 plot plot method plot method generics ## S4 method for signature 'compareclusterresult,any' plot(x, type = "dot", colorby = "p.adjust", showcategory = 5, by = "generatio", split = NULL, includeall = TRUE, font.size = 12, title = "") x type colorby showcategory by split includeall font.size title compareclusterresult object one of bar or dot one of pvalue or p.adjust category numbers one of generatio, Percentage or count ONTOLOGY or NULL logical font size figure title... Additional argument list plot plotgograph plotgograph plot GO graph plotgograph(x, firstsignodes = 10, useinfo = "all", sigforall = TRUE, usefullnames = TRUE,...)

24 24 plotting.clusterprofile x firstsignodes useinfo sigforall usefullnames output of enrichgo or gsego number of significant nodes (retangle nodes in the graph) additional info if TRUE the score/p-value of all nodes in the DAG is shown, otherwise only score will be shown logical... additional parameter of showsigofnodes, please refer to topgo GO DAG graph See Also showsigofnodes plotting.clusterprofile plotting-clusterprofile Internal plot function for plotting compareclusterresult plotting.clusterprofile(clprof.reshape.df, x = ~Cluster, type = "dot", colorby = "p.adjust", by = "generatio", title = "", font.size = 12) clprof.reshape.df data frame of comparecluster result x type colorby by title font.size ggplot object x variable one of dot and bar one of pvalue or p.adjust one of percentage and count graph title graph font size

25 read.gmt 25 read.gmt read.gmt parse gmt file to a data.frame read.gmt(gmtfile) gmtfile gmt file data.frame search_kegg_organism search_kegg_organism search kegg organism, listed in search_kegg_organism(str, by = "scientific_name", ignore.case = FALSE) str by ignore.case string one of kegg.code, scientific_name and common_name TRUE or FALSE data.frame

26 26 simplify simplify simplify method simplify output from enrichgo by removing redundancy of enriched GO terms simplify output from comparecluster by removing redundancy of enriched GO terms ## S4 method for signature 'enrichresult' simplify(x, cutoff = 0.7, by = "p.adjust", select_fun = min, measure = "Wang", semdata = NULL) ## S4 method for signature 'compareclusterresult' simplify(x, cutoff = 0.7, by = "p.adjust", select_fun = min, measure = "Wang", semdata = NULL) x cutoff by select_fun measure semdata output of enrichgo similarity cutoff feature to select representative term, selected by select_fun function function to select feature passed by by parameter method to measure similarity GOSemSimDATA object updated enrichresult object updated compareclusterresult object References issue #28

27 viewkegg 27 viewkegg viewkegg function is for visualize KEGG pathways works with enrichresult object to visualize enriched KEGG pathway viewkegg function is for visualize KEGG pathways works with enrichresult object to visualize enriched KEGG pathway viewkegg(obj, pathwayid, foldchange, color.low = "green", color.high = "red", kegg.native = TRUE, out.suffix = "clusterprofiler") obj pathwayid foldchange color.low color.high kegg.native out.suffix enrichresult object pathway ID or index fold change values color of low foldchange genes color of high foldchange genes logical suffix of output file References Luo et al. (2013) Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics (Oxford, England), 29: , ISSN bioinformatics.oxfordjournals.org/content/abstract/29/14/1830.abstract PMID:

28 Index Topic classes compareclusterresult-class, 6 groupgoresult-class, 17 Topic datasets DataSet, 6 Topic manip comparecluster, 5 enrichgo, 10 enrichkegg, 11 groupgo, 16 gofilter, 15 groupgo, 5, 16, 17 groupgoresult, 6 groupgoresult-class, 17 GSEA, 17 gsego, 18 gsekegg, 19 gsemkegg, 20 idtype, 21 bitr, 3 bitr_kegg, 3 browsekegg, 4 buildgomap, 4 clusterprofiler (clusterprofiler-package), 2 clusterprofiler-package, 2 comparecluster, 5, 6, 10, 11, 16, 17 compareclusterresult, 17 compareclusterresult-class, 6 DataSet, 6 dotplot,compareclusterresult,any-method (dotplot,compareclusterresult-method), 7 dotplot,compareclusterresult-method, 7 download_kegg, 7 dropgo, 8 enrichdavid, 8 enricher, 9 enrichgo, 5, 10 enrichkegg, 11 enrichmkegg, 12 enrichresult, 6 fortify.compareclusterresult, 13 gcsample (DataSet), 6 getgolevel, 13 Gff2GeneTable, 14 go2ont, 14 go2term, KEGG_path2extid, 21 kegg_species (DataSet), 6 ko2name, 22 merge_result, 22 plot, 23 plot,compareclusterresult,any-method (plot), 23 plot,compareclusterresult-method (compareclusterresult-class), 6 plotgograph, 23 plotting.clusterprofile, 24 read.gmt, 25 search_kegg_organism, 25 show,compareclusterresult-method (compareclusterresult-class), 6 show,groupgoresult-method (groupgoresult-class), 17 showsigofnodes, 24 simplify, 26 simplify,compareclusterresult-method (simplify), 26 simplify,enrichresult-method (simplify), 26 summary,compareclusterresult-method (compareclusterresult-class), 6 viewkegg, 27