Package 'clusterProfiler' reference manual

Title:	A universal enrichment tool for interpreting omics data
Description:	This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios. It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation. Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct conditions.
Authors:	Guangchuang 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]
Maintainer:	Guangchuang Yu <[email protected]>
License:	Artistic-2.0
Version:	4.13.0
Built:	2024-07-24 05:16:59 UTC
Source:	https://github.com/bioc/clusterProfiler

append_kegg_category

Description

add KEGG pathway category information

Usage

append_kegg_category(x)
append_kegg_category(x)

Arguments

`x`	KEGG enrichment result

Details

This function appends the KEGG pathway category information to KEGG enrichment result (either output of 'enrichKEGG' or 'gseKEGG'

Value

update KEGG enrichment result with category information

Author(s)

Guangchuang Yu

Compare gene clusters functional profile

Description

Given a list of gene set, this function will compute profiles of each gene cluster.

Usage

compareCluster(
  geneClusters,
  fun = "enrichGO",
  data = "",
  source_from = NULL,
  ...
)
compareCluster(
  geneClusters,
  fun = "enrichGO",
  data = "",
  source_from = NULL,
  ...
)

Arguments

`geneClusters`	a list of entrez gene id. Alternatively, a formula of type `Entrez~group` or a formula of type `Entrez \| logFC ~ group` for "gseGO", "gseKEGG" and "GSEA".
`fun`	One of "groupGO", "enrichGO", "enrichKEGG", "enrichDO" or "enrichPathway" . Users can also supply their own function.
`data`	if geneClusters is a formula, the data from which the clusters must be extracted.
`source_from`	If using a custom function in "fun", provide the source package as a string here. Otherwise, the function will be obtained from the global environment.
`...`	Other arguments.

Value

A clusterProfResult instance.

Author(s)

Guangchuang Yu https://yulab-smu.top

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")
dotplot(xx)

## formula interface
mydf <- data.frame(Entrez=c('1', '100', '1000', '100101467',
                            '100127206', '100128071'),
                   logFC = c(1.1, -0.5, 5, 2.5, -3, 3),
                   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)

## 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)
## 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")
dotplot(xx)

## formula interface
mydf <- data.frame(Entrez=c('1', '100', '1000', '100101467',
                            '100127206', '100128071'),
                   logFC = c(1.1, -0.5, 5, 2.5, -3, 3),
                   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)

## 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)

Datasets gcSample contains a sample of gene clusters.

Description

Datasets gcSample contains a sample of gene clusters.

Datasets kegg_species contains kegg species information

Datasets kegg_category contains kegg pathway category information

Datasets DE_GSE8057 contains differential epxressed genes obtained from GSE8057 dataset

enricher

Description

A universal enrichment analyzer

Usage

enricher(
  gene,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe = NULL,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  gson = NULL,
  TERM2GENE,
  TERM2NAME = NA
)
enricher(
  gene,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe = NULL,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  gson = NULL,
  TERM2GENE,
  TERM2NAME = NA
)

Arguments

`gene`	a vector of gene id
`pvalueCutoff`	adjusted pvalue cutoff on enrichment tests to report
`pAdjustMethod`	one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
`universe`	background genes. If missing, the all genes listed in the database (eg TERM2GENE table) will be used as background.
`minGSSize`	minimal size of genes annotated for testing
`maxGSSize`	maximal size of genes annotated for testing
`qvalueCutoff`	qvalue cutoff on enrichment tests to report as significant. Tests must pass i) `pvalueCutoff` on unadjusted pvalues, ii) `pvalueCutoff` on adjusted pvalues and iii) `qvalueCutoff` on qvalues to be reported.
`gson`	a GSON object, if not NULL, use it as annotation data.
`TERM2GENE`	user input annotation of TERM TO GENE mapping, a data.frame of 2 column with term and gene. Only used when gson is NULL.
`TERM2NAME`	user input of TERM TO NAME mapping, a data.frame of 2 column with term and name. Only used when gson is NULL.

Value

A enrichResult instance

Author(s)

Guangchuang Yu https://yulab-smu.top

GO Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment GO categories after FDR control.

Description

GO Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment GO categories after FDR control.

Usage

enrichGO(
  gene,
  OrgDb,
  keyType = "ENTREZID",
  ont = "MF",
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  qvalueCutoff = 0.2,
  minGSSize = 10,
  maxGSSize = 500,
  readable = FALSE,
  pool = FALSE
)
enrichGO(
  gene,
  OrgDb,
  keyType = "ENTREZID",
  ont = "MF",
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  qvalueCutoff = 0.2,
  minGSSize = 10,
  maxGSSize = 500,
  readable = FALSE,
  pool = FALSE
)

Arguments

`gene`	a vector of entrez gene id.
`OrgDb`	OrgDb
`keyType`	keytype of input gene
`ont`	One of "BP", "MF", and "CC" subontologies, or "ALL" for all three.
`pvalueCutoff`	adjusted pvalue cutoff on enrichment tests to report
`pAdjustMethod`	one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
`universe`	background genes. If missing, the all genes listed in the database (eg TERM2GENE table) will be used as background.
`qvalueCutoff`	qvalue cutoff on enrichment tests to report as significant. Tests must pass i) `pvalueCutoff` on unadjusted pvalues, ii) `pvalueCutoff` on adjusted pvalues and iii) `qvalueCutoff` on qvalues to be reported.
`minGSSize`	minimal size of genes annotated by Ontology term for testing.
`maxGSSize`	maximal size of genes annotated for testing
`readable`	whether mapping gene ID to gene Name
`pool`	If ont='ALL', whether pool 3 GO sub-ontologies

Value

An enrichResult instance.

Author(s)

Guangchuang Yu https://yulab-smu.top

Examples

## Not run: 
  data(geneList, package = "DOSE")
	de <- names(geneList)[1:100]
	yy <- enrichGO(de, 'org.Hs.eg.db', ont="BP", pvalueCutoff=0.01)
	head(yy)

## End(Not run)
## Not run: 
  data(geneList, package = "DOSE")
	de <- names(geneList)[1:100]
	yy <- enrichGO(de, 'org.Hs.eg.db', ont="BP", pvalueCutoff=0.01)
	head(yy)

## End(Not run)

KEGG Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment KEGG categories with FDR control.

Description

KEGG Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment KEGG categories with FDR control.

Usage

enrichKEGG(
  gene,
  organism = "hsa",
  keyType = "kegg",
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  use_internal_data = FALSE
)
enrichKEGG(
  gene,
  organism = "hsa",
  keyType = "kegg",
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  use_internal_data = FALSE
)

Arguments

`gene`	a vector of entrez gene id.
`organism`	supported organism listed in 'https://www.genome.jp/kegg/catalog/org_list.html'
`keyType`	one of "kegg", 'ncbi-geneid', 'ncbi-proteinid' and 'uniprot'
`pvalueCutoff`	adjusted pvalue cutoff on enrichment tests to report
`pAdjustMethod`	one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
`universe`	background genes. If missing, the all genes listed in the database (eg TERM2GENE table) will be used as background.
`minGSSize`	minimal size of genes annotated by Ontology term for testing.
`maxGSSize`	maximal size of genes annotated for testing
`qvalueCutoff`	qvalue cutoff on enrichment tests to report as significant. Tests must pass i) `pvalueCutoff` on unadjusted pvalues, ii) `pvalueCutoff` on adjusted pvalues and iii) `qvalueCutoff` on qvalues to be reported.
`use_internal_data`	logical, use KEGG.db or latest online KEGG data

Value

A enrichResult instance.

Author(s)

Guangchuang Yu https://yulab-smu.top

Examples

## Not run: 
  data(geneList, package='DOSE')
  de <- names(geneList)[1:100]
  yy <- enrichKEGG(de, pvalueCutoff=0.01)
  head(yy)

## End(Not run)
## Not run: 
  data(geneList, package='DOSE')
  de <- names(geneList)[1:100]
  yy <- enrichKEGG(de, pvalueCutoff=0.01)
  head(yy)

## End(Not run)

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.

Description

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.

Usage

enrichMKEGG(
  gene,
  organism = "hsa",
  keyType = "kegg",
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2
)
enrichMKEGG(
  gene,
  organism = "hsa",
  keyType = "kegg",
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2
)

Arguments

`gene`	a vector of entrez gene id.
`organism`	supported organism listed in 'https://www.genome.jp/kegg/catalog/org_list.html'
`keyType`	one of "kegg", 'ncbi-geneid', 'ncbi-proteinid' and 'uniprot'
`pvalueCutoff`	adjusted pvalue cutoff on enrichment tests to report
`pAdjustMethod`	one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
`universe`	background genes. If missing, the all genes listed in the database (eg TERM2GENE table) will be used as background.
`minGSSize`	minimal size of genes annotated by Ontology term for testing.
`maxGSSize`	maximal size of genes annotated for testing
`qvalueCutoff`	qvalue cutoff on enrichment tests to report as significant. Tests must pass i) `pvalueCutoff` on unadjusted pvalues, ii) `pvalueCutoff` on adjusted pvalues and iii) `qvalueCutoff` on qvalues to be reported.

Value

A enrichResult instance.

enrichPC

Description

ORA analysis for Pathway Commons

Usage

enrichPC(gene, source, keyType = "hgnc", ...)
enrichPC(gene, source, keyType = "hgnc", ...)

Arguments

`gene`	a vector of genes (either hgnc symbols or uniprot IDs)
`source`	Data source of Pathway Commons, e.g., 'reactome', 'kegg', 'pathbank', 'netpath', 'panther', etc.
`keyType`	specify the type of input 'gene' (one of 'hgnc' or 'uniprot')
`...`	additional parameters, see also the parameters supported by the enricher() function

Details

This function performs over-representation analysis using Pathway Commons

Value

A enrichResult instance

enrichWP

Description

ORA analysis for WikiPathways

Usage

enrichWP(gene, organism, ...)
enrichWP(gene, organism, ...)

Arguments

`gene`	a vector of entrez gene id
`organism`	supported organisms, which can be accessed via the get_wp_organisms() function
`...`	additional parameters, see also the parameters supported by the enricher() function

Details

This function performs over-representation analysis using WikiPathways

Value

A enrichResult instance

Author(s)

Guangchuang Yu

get_wp_organism

Description

list supported organism of WikiPathways

Usage

get_wp_organisms()
get_wp_organisms()

Details

This function extracts information from 'https://data.wikipathways.org/current/gmt/' and lists all supported organisms

Value

supported organism list

Author(s)

Guangchuang Yu

getPPI

Description

getPPI

Usage

getPPI(
  x,
  ID = 1,
  taxID = "auto",
  required_score = NULL,
  network_type = "functional",
  add_nodes = 0,
  show_query_node_labels = 0,
  output = "igraph"
)
getPPI(
  x,
  ID = 1,
  taxID = "auto",
  required_score = NULL,
  network_type = "functional",
  add_nodes = 0,
  show_query_node_labels = 0,
  output = "igraph"
)

Arguments

`x`	an 'enrichResult“ object or a vector of proteins, e.g. 'c("PTCH1", "TP53", "BRCA1", "BRCA2")'
`ID`	ID or index to extract genes in the enriched term(s) if 'x' is an 'enrichResult' object
`taxID`	NCBI taxon identifiers (e.g. Human is 9606, see: [STRING organisms](https://string-db.org/cgi/input.pl?input_page_active_form=organisms).
`required_score`	threshold of significance to include a interaction, a number between 0 and 1000 (default depends on the network)
`network_type`	network type: functional (default), physical
`add_nodes`	adds a number of proteins with to the network based on their confidence score (default:1)
`show_query_node_labels`	when available use submitted names in the preferredName column when (0 or 1) (default:0)
`output`	one of 'data.frame' or 'igraph'

Details

[Getting the STRING network interactions](https://string-db.org/cgi/help.pl?sessionId=btsvnCeNrBk7).

Value

a 'data.frame' or an 'igraph' object

Author(s)

Yonghe Xia and modified by Guangchuang Yu

getTaxID

Description

Convert species scientific name to taxonomic ID

Usage

getTaxID(species)
getTaxID(species)

Arguments

species

scientific name of a species

Value

taxonomic ID

Author(s)

Guangchuang Yu

getTaxInfo

Description

Query taxonomy information from 'stringdb' or 'ensembl' web services

Usage

getTaxInfo(species, source = "stringdb")
getTaxInfo(species, source = "stringdb")

Arguments

`species`	scientific name of a species
`source`	one of 'stringdb' or 'ensembl'

Value

a 'data.frame' of query information

Author(s)

Guangchuang Yu

Gff2GeneTable

Description

read GFF file and build gene information table

Usage

Gff2GeneTable(gffFile, compress = TRUE)
Gff2GeneTable(gffFile, compress = TRUE)

Arguments

`gffFile`	GFF file
`compress`	compress file or not

Details

given a GFF file, this function extracts information from it and save it in working directory

Value

file save.

Author(s)

Yu Guangchuang

go2ont

Description

convert goid to ontology (BP, CC, MF)

Usage

go2ont(goid)
go2ont(goid)

Arguments

goid

a vector of GO IDs

Value

data.frame

Author(s)

Guangchuang Yu

go2term

Description

convert goid to descriptive term

Usage

go2term(goid)
go2term(goid)

Arguments

goid

a vector of GO IDs

Value

data.frame

Author(s)

Guangchuang Yu

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.

Description

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.

Usage

groupGO(
  gene,
  OrgDb,
  keyType = "ENTREZID",
  ont = "CC",
  level = 2,
  readable = FALSE
)
groupGO(
  gene,
  OrgDb,
  keyType = "ENTREZID",
  ont = "CC",
  level = 2,
  readable = FALSE
)

Arguments

`gene`	a vector of entrez gene id.
`OrgDb`	OrgDb
`keyType`	key type of input gene
`ont`	One of "MF", "BP", and "CC" subontologies.
`level`	Specific GO Level.
`readable`	if readable is TRUE, the gene IDs will mapping to gene symbols.

Value

A groupGOResult instance.

Author(s)

Guangchuang Yu https://yulab-smu.top

Examples


	data(gcSample)
	yy <- groupGO(gcSample[[1]], 'org.Hs.eg.db', ont="BP", level=2)
	head(summary(yy))
	#plot(yy)

data(gcSample)
	yy <- groupGO(gcSample[[1]], 'org.Hs.eg.db', ont="BP", level=2)
	head(summary(yy))
	#plot(yy)

Class "groupGOResult" This class represents the result of functional Profiles of a set of gene at specific GO level.

Description

Class "groupGOResult" This class represents the result of functional Profiles of a set of gene at specific GO level.

Slots

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.

Author(s)

Guangchuang Yu https://yulab-smu.top

GSEA

Description

a universal gene set enrichment analysis tools

Usage

GSEA(
  geneList,
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  eps = 1e-10,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  gson = NULL,
  TERM2GENE,
  TERM2NAME = NA,
  verbose = TRUE,
  seed = FALSE,
  by = "fgsea",
  ...
)
GSEA(
  geneList,
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  eps = 1e-10,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  gson = NULL,
  TERM2GENE,
  TERM2NAME = NA,
  verbose = TRUE,
  seed = FALSE,
  by = "fgsea",
  ...
)

Arguments

`geneList`	order ranked geneList
`exponent`	weight of each step
`minGSSize`	minimal size of each geneSet for analyzing
`maxGSSize`	maximal size of genes annotated for testing
`eps`	This parameter sets the boundary for calculating the p value.
`pvalueCutoff`	adjusted pvalue cutoff
`pAdjustMethod`	one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
`gson`	a GSON object, if not NULL, use it as annotation data.
`TERM2GENE`	user input annotation of TERM TO GENE mapping, a data.frame of 2 column with term and gene. Only used when gson is NULL.
`TERM2NAME`	user input of TERM TO NAME mapping, a data.frame of 2 column with term and name. Only used when gson is NULL.
`verbose`	logical
`seed`	logical
`by`	one of 'fgsea' or 'DOSE'
`...`	other parameter

Value

gseaResult object

Author(s)

Guangchuang Yu https://yulab-smu.top

gsePC

Description

GSEA analysis for Pathway Commons

Usage

gsePC(geneList, source, keyType, ...)
gsePC(geneList, source, keyType, ...)

Arguments

`geneList`	a ranked gene list
`source`	Data source of Pathway Commons, e.g., 'reactome', 'kegg', 'pathbank', 'netpath', 'panther', etc.
`keyType`	specify the type of input 'gene' (one of 'hgnc' or 'uniprot')
`...`	additional parameters, see also the parameters supported by the GSEA() function

Details

This function performs GSEA using Pathway Commons

Value

A gseaResult instance

gseWP

Description

GSEA analysis for WikiPathways

Usage

gseWP(geneList, organism, ...)
gseWP(geneList, organism, ...)

Arguments

`geneList`	ranked gene list
`organism`	supported organisms, which can be accessed via the get_wp_organisms() function
`...`	additional parameters, see also the parameters supported by the GSEA() function

Details

This function performs GSEA using WikiPathways

Value

A gseaResult instance

Author(s)

Guangchuang Yu

Build KEGG annotation for novel species using KEGG Mapper

Description

KEGG Mapper service can annotate protein sequences for novel species with KO database, and KO annotation need to be converted into Pathway or Module annotation, which can then be used in 'clusterProfiler'

Usage

gson_KEGG_mapper(
  file,
  format = c("BLAST", "Ghost", "Kofam"),
  type = c("pathway", "module"),
  species = NULL,
  ...
)
gson_KEGG_mapper(
  file,
  format = c("BLAST", "Ghost", "Kofam"),
  type = c("pathway", "module"),
  species = NULL,
  ...
)

Arguments

`file`	the name of the file which comes from the KEGG Mapper service, see Details for file format
`format`	string indicate format of KEGG Mapper result
`type`	string indicate annotation database
`species`	your species, NULL if ignored
`...`	pass to gson::gson()

Details

File is a two-column dataset with K numbers in the second column, optionally preceded by the user's identifiers in the first column. This is consistent with the output files of automatic annotation servers, BlastKOALA, GhostKOALA, and KofamKOALA. KOALA (KEGG Orthology And Links Annotation) is KEGG's internal annotation tool for K number assignment of KEGG GENES using SSEARCH computation. BlastKOALA and GhostKOALA assign K numbers to the user's sequence data by BLAST and GHOSTX searches, respectively, against a nonredundant set of KEGG GENES. KofamKOALA is a new member of the KOALA family available at GenomeNet using the HMM profile search, rather than the sequence similarity search, for K number assignment. see https://www.kegg.jp/blastkoala/, https://www.kegg.jp/ghostkoala/ and https://www.genome.jp/tools/kofamkoala/ for more information.

Value

a gson instance

Examples

## Not run: 
 file = system.file('extdata', "kegg_mapper_blast.txt", package='clusterProfiler')
 gson_KEGG_mapper(file, format = "BLAST", type = "pathway")

## End(Not run)
## Not run: 
 file = system.file('extdata', "kegg_mapper_blast.txt", package='clusterProfiler')
 gson_KEGG_mapper(file, format = "BLAST", type = "pathway")

## End(Not run)

gson_WP

Description

Download the latest version of WikiPathways data and stored in a 'GSON' object

Usage

gson_WP(organism)
gson_WP(organism)

Arguments

organism

supported organism, which can be accessed via the get_wp_organisms() function.

idType

Description

list ID types supported by annoDb

Usage

idType(OrgDb = "org.Hs.eg.db")
idType(OrgDb = "org.Hs.eg.db")

Arguments

OrgDb

annotation db

Value

character vector

Author(s)

Guangchuang Yu

ko2name

Description

convert ko ID to descriptive name

Usage

ko2name(ko)
ko2name(ko)

Arguments

ko

ko ID

Value

data.frame

Author(s)

guangchuang yu

merge_result

Description

merge a list of enrichResult objects to compareClusterResult

Usage

merge_result(enrichResultList)
merge_result(enrichResultList)

Arguments

enrichResultList

a list of enrichResult objects

Value

a compareClusterResult instance

Author(s)

Guangchuang Yu

plotGOgraph

Description

plot GO graph

Usage

plotGOgraph(
  x,
  firstSigNodes = 10,
  useInfo = "all",
  sigForAll = TRUE,
  useFullNames = TRUE,
  ...
)
plotGOgraph(
  x,
  firstSigNodes = 10,
  useInfo = "all",
  sigForAll = TRUE,
  useFullNames = TRUE,
  ...
)

Arguments

`x`	output of enrichGO or gseGO
`firstSigNodes`	number of significant nodes (retangle nodes in the graph)
`useInfo`	additional info
`sigForAll`	if TRUE the score/p-value of all nodes in the DAG is shown, otherwise only score will be shown
`useFullNames`	logical
`...`	additional parameter of showSigOfNodes, please refer to topGO

Value

GO DAG graph

Author(s)

Guangchuang Yu

read.gmt.pc

Description

Parse gmt file from Pathway Common

Usage

read.gmt.pc(gmtfile, output = "data.frame")
read.gmt.pc(gmtfile, output = "data.frame")

Arguments

`gmtfile`	A gmt file
`output`	one of 'data.frame' or 'GSON'

Details

This function parse gmt file downloaded from Pathway common

Value

A data.frame or A GSON object depends on the value of 'output'

search_kegg_organism

Description

search kegg organism, listed in https://www.genome.jp/kegg/catalog/org_list.html

Usage

search_kegg_organism(
  str,
  by = "scientific_name",
  ignore.case = FALSE,
  use_internal_data = TRUE
)
search_kegg_organism(
  str,
  by = "scientific_name",
  ignore.case = FALSE,
  use_internal_data = TRUE
)

Arguments

`str`	string
`by`	one of 'kegg.code', 'scientific_name' and 'common_name'
`ignore.case`	TRUE or FALSE
`use_internal_data`	logical, use kegg_species.rda or latest online KEGG data

Value

data.frame

Author(s)

Guangchuang Yu

simplify method

Description

simplify output from enrichGO and gseGO by removing redundancy of enriched GO terms

simplify output from compareCluster by removing redundancy of enriched GO terms

Usage

## S4 method for signature 'enrichResult'
simplify(
  x,
  cutoff = 0.7,
  by = "p.adjust",
  select_fun = min,
  measure = "Wang",
  semData = NULL
)

## S4 method for signature 'gseaResult'
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
)
## S4 method for signature 'enrichResult'
simplify(
  x,
  cutoff = 0.7,
  by = "p.adjust",
  select_fun = min,
  measure = "Wang",
  semData = NULL
)

## S4 method for signature 'gseaResult'
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
)

Arguments

`x`	output of enrichGO
`cutoff`	similarity cutoff
`by`	feature to select representative term, selected by 'select_fun' function
`select_fun`	function to select feature passed by 'by' parameter
`measure`	method to measure similarity
`semData`	GOSemSimDATA object

Value

updated enrichResult object

updated compareClusterResult object

Author(s)

Guangchuang Yu

Gwang-Jin Kim and Guangchuang Yu

References

issue #28 https://github.com/GuangchuangYu/clusterProfiler/issues/28

issue #162 https://github.com/GuangchuangYu/clusterProfiler/issues/162

uniprot_get

Description

retreve annotation data from uniprot

Usage

uniprot_get(taxID)
uniprot_get(taxID)

Arguments

taxID

taxonomy ID

Value

gene table data frame

Author(s)

guangchuang yu

`geneID`	input gene id
`fromType`	input id type
`toType`	output id type
`OrgDb`	annotation db
`drop`	drop NA or not

`geneID`	input gene id
`fromType`	input id type
`toType`	output id type
`organism`	supported organism, can be search using search_kegg_organism function
`drop`	drop NA or not

`x`	an instance of enrichResult or gseaResult
`pathID`	pathway ID

`species`	species
`keggType`	one of 'KEGG' or 'MKEGG'
`keyType`	supported keyType, see bitr_kegg

`x`	an instance of 'enrichResult' or 'compareClusterResult'
`level`	GO level
`term`	GO term

`gene`	input gene
`idType`	id type
`universe`	background genes. If missing, the all genes listed in the database (eg TERM2GENE table) will be used as background.
`minGSSize`	minimal size of genes annotated for testing
`maxGSSize`	maximal size of genes annotated for testing
`annotation`	david annotation
`pvalueCutoff`	adjusted pvalue cutoff on enrichment tests to report
`pAdjustMethod`	one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
`qvalueCutoff`	qvalue cutoff on enrichment tests to report as significant. Tests must pass i) `pvalueCutoff` on unadjusted pvalues, ii) `pvalueCutoff` on adjusted pvalues and iii) `qvalueCutoff` on qvalues to be reported.
`species`	species
`david.user`	david user

`species`	species
`KEGG_Type`	one of "KEGG" and "MKEGG"
`keyType`	one of "kegg", 'ncbi-geneid', 'ncib-proteinid' and 'uniprot'.

Package 'clusterProfiler'

Help Index

append_kegg_category

Description

Usage

Arguments

Details

Value

Author(s)

bitr

Description

Usage

Arguments

Value

Author(s)

bitr_kegg

Description

Usage

Arguments

Value

Author(s)

browseKEGG

Description

Usage

Arguments

Value

Author(s)

Compare gene clusters functional profile

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Datasets gcSample contains a sample of gene clusters.

Description

download_KEGG

Description

Usage

Arguments

Value

Author(s)

dropGO

Description

Usage

Arguments

Value

Author(s)

enrichDAVID

Description

Usage

Arguments

Value

Author(s)

enricher

Description

Usage

Arguments

Value

Author(s)

GO Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment GO categories after FDR control.

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

KEGG Enrichment Analysis of a gene set. Given a vector of genes, this function will return the enrichment KEGG categories with FDR control.

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

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.

Description

Usage