Package 'clusterProfiler'

Title: A Universal Enrichment Tool for Interpreting Omics Data
Description: A universal tool for interpreting functional characteristics of omics data. It supports Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) for both coding and non-coding genomics data of thousands of species. It provides a unified and tidy interface to access, manipulate, and visualize enrichment results. A key capability is the simultaneous analysis and comparison of datasets from multiple treatments or time points. It also provides high-level access to topology-aware enrichment workflows powered by 'enrichit', including single-network and multi-layer network enrichment for Gene Ontology and KEGG analyses. Furthermore, it integrates Large Language Model (LLM) capabilities to provide automated and insightful interpretation of enrichment results.
Authors: Guangchuang Yu [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-6485-8781>), Li-Gen Wang [ctb], Giovanni Dall'Olio [ctb], Qianwen Wang [ctb], Wanqian Wei [ctb], Chun-Hui Gao [ctb] (ORCID: <https://orcid.org/0000-0002-1445-7939>)
Maintainer: Guangchuang Yu <[email protected]>
License: Artistic-2.0
Version: 4.21.4
Built: 2026-11-01 12:52:55 UTC
Source: https://github.com/bioc/clusterProfiler

Help Index

Add the GO level of each enriched term

Description

'enrichGO()'/'gseGO()' results carry the term, its ontology and its statistics, but not how deep the term sits in the GO hierarchy. This appends that as a 'level' column (1 = ontology root), which makes it easy to keep a band of levels by filtering, e.g. 'subset(res, level >= 3 & level <= 6)'.

Usage

add_go_level(x)

Arguments

x

an 'enrichResult', 'gseaResult' or 'compareClusterResult' from a GO enrichment analysis

Details

The level is taken from the ontology the term itself belongs to, so an 'ont = "ALL"' (or 'compareCluster()') result is labelled correctly term by term. Terms that GO.db does not recognise get 'NA'.

Value

'x' with a 'level' column appended to its result table

Author(s)

Guangchuang Yu https://yulab-smu.top

Examples

## Not run: 
x <- enrichGO(gene, OrgDb = "org.Hs.eg.db", ont = "BP")
x <- add_go_level(x)
subset(x, level >= 3 & level <= 6)

## End(Not run)

append_kegg_category

Description

add KEGG pathway category information

Usage

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

bitr

Description

Biological Id TRanslator

Usage

bitr(geneID, fromType, toType, OrgDb, drop = TRUE)

Arguments

geneID

input gene id
fromType

input id type
toType

output id type
OrgDb

annotation db
drop

drop NA or not

Value

data.frame

Author(s)

Guangchuang Yu

bitr_kegg

Description

convert biological ID using KEGG API

Usage

bitr_kegg(geneID, fromType, toType, organism, drop = TRUE)

Arguments

geneID

input gene id
fromType

input id type, one of "Path", "Module", "ncbi-proteinid", "ncbi-geneid", "uniprot", "kegg", and "ko"
toType

output id type, one of "Path", "Module", "ncbi-proteinid", "ncbi-geneid", "uniprot", "kegg", and "ko"
organism

supported organism, can be search using search_kegg_organism function
drop

drop NA or not

Value

data.frame

Author(s)

Guangchuang Yu

Examples

## Not run: 
  bitr_kegg(c("P31946", "P01308"), "uniprot", "ko", "hsa")
  bitr_kegg(c("K04456", "K04526"), "ko", "uniprot", "hsa")

## End(Not run)

browseKEGG

Description

open KEGG pathway with web browser

Usage

browseKEGG(x, pathID)

Arguments

x

an instance of enrichResult or gseaResult
pathID

pathway ID

Value

url

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,
  ...
)

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

See Also

[compareClusterResult-class], [groupGO], [enrichGO], [enrichKEGG], [enrichDO][DOSE::enrichDO], [enrichPathway][ReactomePA::enrichPathway]

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)

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

download_KEGG

Description

download the latest version of KEGG pathway/module

Usage

download_KEGG(species, keggType = "KEGG", keyType = "kegg")

Arguments

species

species
keggType

one of 'KEGG' or 'MKEGG'
keyType

supported keyType, see bitr_kegg

Value

list

Author(s)

Guangchuang Yu

dropGO

Description

drop GO term of specific level or specific terms (mostly too general).

Usage

dropGO(x, level = NULL, term = NULL)

Arguments

x

an instance of 'enrichResult' or 'compareClusterResult'
level

GO level
term

GO term

Value

modified version of x

Author(s)

Guangchuang Yu

enrichDAVID

Description

enrichment analysis by DAVID

Usage

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
)

Arguments

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

Value

A enrichResult instance

Author(s)

Guangchuang Yu

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
)

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,
  evidence = NULL
)

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
evidence

optional character vector of GO evidence codes to keep (for example 'c("IDA", "IPI", "IMP")', or '"IEA"' for electronic annotations only). When supplied, annotations carrying any other evidence code are dropped before the analysis — useful to avoid circular reasoning (dropping 'IEP' when the gene sets come from co-expression clustering) or to keep only manually reviewed annotations (dropping 'IEA'). Because the annotation changes, so does the background, so the resulting terms are not simply a subset of the unfiltered run. Filtered runs are not cached, so they rebuild the annotation each time. 'NULL' (default) uses every annotation.

Value

An enrichResult instance.

Author(s)

Guangchuang Yu https://yulab-smu.top

See Also

[enrichResult-class], [compareCluster]

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)

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
)

Arguments

gene

a vector of entrez gene id.
organism

supported organism listed in 'https://www.genome.jp/kegg/catalog/org_list.html'; alternatively a 'GSON' object (e.g. from 'gson_KEGG()') holding the annotation, which allows the analysis to run without contacting KEGG
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

See Also

[enrichResult-class], [compareCluster]

Examples

## 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

As with enrichKEGG(), organism may be a species code or a GSON object, so module enrichment can be run against a locally built annotation (see gson_KEGG()) when KEGG is unreachable.

Usage

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'; alternatively a 'GSON' object (e.g. from 'gson_KEGG()') holding the annotation, which allows the analysis to run without contacting KEGG
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, ...)

Arguments

gene

a vector of genes (either hgnc symbols or uniprot IDs)
...

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, ...)

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

Evidence status of every gene named in an interpretation

Description

Deterministically classifies each gene symbol that an interpretation names as observed (a member gene of the enrichment result it was given) or out_of_evidence (a valid symbol that is not). No model is called. Use it to label hypotheses from interpret_agent() as inferred rather than observed: unlike a second model, this check is exact and cannot also mark grounded genes as uncertain.

Usage

evidence_status(
  x,
  enrichment,
  OrgDb = NULL,
  symbols = NULL,
  fields = c("overview", "key_mechanisms", "regulatory_drivers", "crosstalk",
    "hypothesis", "narrative", "reasoning", "network_evidence")
)

Arguments

x

An interpretation or interpretation_list returned by interpret(), interpret_agent() or interpret_hierarchical().
enrichment

The enrichResult, compareClusterResult, gseaResult or data.frame the interpretation was produced from (readable, so that member genes are symbols).
OrgDb

Optional OrgDb used to recognise out-of-evidence symbols. Without it, only tokens that look like gene symbols and appear in symbols are considered.
symbols

Optional character vector of valid gene symbols (alternative to OrgDb).
fields

Record fields to scan.

Value

A data.frame with columns cluster, field, gene and status.

get_ppi_network

Description

get_ppi_network

Usage

get_ppi_network(
  taxID = 9606,
  version = "12.0",
  score_threshold = 400,
  output = "data.frame"
)

Arguments

taxID

NCBI taxon identifier (e.g., 9606 for Human).
version

STRING database version (default: "12.0").
score_threshold

threshold of significance to include a interaction (default: 400).
output

one of 'data.frame' or 'igraph'

Details

Download and cache the full STRING PPI background network for a specific species.

Value

a 'data.frame' or an 'igraph' object containing the background PPI network.

get_wp_organism

Description

list supported organism of WikiPathways

Usage

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"
)

get_ppi(
  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)

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")

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)

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)

Arguments

goid

a vector of GO IDs

Value

data.frame

Author(s)

Guangchuang Yu

go2term

Description

convert goid to descriptive term

Usage

go2term(goid)

Arguments

goid

a vector of GO IDs

Value

data.frame

Author(s)

Guangchuang Yu

gofilter

Description

filter GO enriched result at specific level

Usage

gofilter(x, level = 4)

Arguments

x

output from enrichGO or compareCluster
level

GO level

Value

updated object

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
)

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

See Also

[groupGOResult-class], [compareCluster]

Examples

## Not run: 
	data(gcSample)
	yy <- groupGO(gcSample[[1]], 'org.Hs.eg.db', ont="BP", level=2)
	head(summary(yy))
	#plot(yy)

## End(Not run)

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

See Also

[compareClusterResult], [compareCluster], [groupGO]

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,
  nPerm = 1000,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 101,
  maxPerm = 1e+05,
  pvalThreshold = 0.1,
  seed = FALSE,
  ...
)

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

boundary for calculating the p value in multilevel mode
pvalueCutoff

p-value cutoff applied to both the raw p-value and the adjusted p-value (p.adjust), consistent with the historical clusterProfiler/DOSE behavior
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
nPerm

The number of permutations.
method

method of calculating the pvalue, one of "multilevel", "monte carlo" and "fgsea"
adaptive

logical, whether to use adaptive method for calculating pvalue
minPerm

minimal number of permutations for adaptive method
maxPerm

maximal number of permutations for adaptive method
pvalThreshold

pvalue threshold for adaptive method
seed

random seed for reproducibility, set to a number (or TRUE to use a fixed default seed) to make the result reproducible, or FALSE (default) to draw a random seed on each run, so results may vary between runs. The underlying permutation engine uses its own RNG seeded with this value; see enrichit::gsea() for details.
...

other parameters passed to enrichit::gsea_gson()

Value

gseaResult object

Author(s)

Guangchuang Yu https://yulab-smu.top

gseGO

Description

Gene Set Enrichment Analysis of Gene Ontology

Usage

gseGO(
  geneList,
  ont = "BP",
  OrgDb,
  keyType = "ENTREZID",
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  eps = 1e-10,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  nPerm = 1000,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 101,
  maxPerm = 1e+05,
  pvalThreshold = 0.1,
  seed = FALSE,
  ...
)

Arguments

geneList

order ranked geneList
ont

one of "BP", "MF", and "CC" subontologies, or "ALL" for all three.
OrgDb

OrgDb
keyType

keytype of gene
exponent

weight of each step
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
eps

boundary for calculating the p value in multilevel mode
pvalueCutoff

pvalue Cutoff
pAdjustMethod

pvalue adjustment method
verbose

print message or not
nPerm

The number of permutations.
method

method of calculating the pvalue, one of "multilevel", "permute" and "sample"
adaptive

logical, whether to use adaptive method for calculating pvalue
minPerm

minimal number of permutations for adaptive method
maxPerm

maximal number of permutations for adaptive method
pvalThreshold

pvalue threshold for adaptive method
seed

random seed for reproducibility, set to a number (or TRUE to use a fixed default seed) to make the result reproducible, or FALSE (default) to draw a random seed on each run, so results may vary between runs. The underlying permutation engine uses its own RNG seeded with this value; see enrichit::gsea() for details.
...

other parameters passed to enrichit::gsea_gson()

Value

gseaResult object

Author(s)

Yu Guangchuang

gseKEGG

Description

Gene Set Enrichment Analysis of KEGG

Usage

gseKEGG(
  geneList,
  organism = "hsa",
  keyType = "kegg",
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  eps = 1e-10,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  use_internal_data = FALSE,
  nPerm = 1000,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 101,
  maxPerm = 1e+05,
  pvalThreshold = 0.1,
  seed = FALSE,
  ...
)

Arguments

geneList

order ranked geneList
organism

supported organism listed in 'https://www.genome.jp/kegg/catalog/org_list.html'; alternatively a 'GSON' object (e.g. from 'gson_KEGG()') holding the annotation, which allows the analysis to run without contacting KEGG
keyType

one of "kegg", 'ncbi-geneid', 'ncib-proteinid' and 'uniprot'
exponent

weight of each step
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
eps

boundary for calculating the p value in multilevel mode
pvalueCutoff

pvalue Cutoff
pAdjustMethod

pvalue adjustment method
verbose

print message or not
use_internal_data

logical, use KEGG.db or latest online KEGG data
nPerm

The number of permutations.
method

method of calculating the pvalue, one of "multilevel", "permute" and "sample"
adaptive

logical, whether to use adaptive method for calculating pvalue
minPerm

minimal number of permutations for adaptive method
maxPerm

maximal number of permutations for adaptive method
pvalThreshold

pvalue threshold for adaptive method
seed

random seed for reproducibility, set to a number (or TRUE to use a fixed default seed) to make the result reproducible, or FALSE (default) to draw a random seed on each run, so results may vary between runs. The underlying permutation engine uses its own RNG seeded with this value; see enrichit::gsea() for details.
...

other parameters passed to enrichit::gsea_gson()

Value

gseaResult object

Author(s)

Yu Guangchuang

gseMKEGG

Description

Gene Set Enrichment Analysis of KEGG Module

Usage

gseMKEGG(
  geneList,
  organism = "hsa",
  keyType = "kegg",
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  eps = 1e-10,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  nPerm = 1000,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 101,
  maxPerm = 1e+05,
  pvalThreshold = 0.1,
  seed = FALSE,
  ...
)

Arguments

geneList

order ranked geneList
organism

supported organism listed in 'https://www.genome.jp/kegg/catalog/org_list.html'; alternatively a 'GSON' object (e.g. from 'gson_KEGG()') holding the annotation, which allows the analysis to run without contacting KEGG
keyType

one of "kegg", 'ncbi-geneid', 'ncib-proteinid' and 'uniprot'
exponent

weight of each step
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
eps

boundary for calculating the p value in multilevel mode
pvalueCutoff

pvalue Cutoff
pAdjustMethod

pvalue adjustment method
verbose

print message or not
nPerm

The number of permutations.
method

method of calculating the pvalue, one of "multilevel", "permute" and "sample"
adaptive

logical, whether to use adaptive method for calculating pvalue
minPerm

minimal number of permutations for adaptive method
maxPerm

maximal number of permutations for adaptive method
pvalThreshold

pvalue threshold for adaptive method
seed

random seed for reproducibility, set to a number (or TRUE to use a fixed default seed) to make the result reproducible, or FALSE (default) to draw a random seed on each run, so results may vary between runs. The underlying permutation engine uses its own RNG seeded with this value; see enrichit::gsea() for details.
...

other parameters passed to enrichit::gsea_gson()

Value

gseaResult object

Author(s)

Yu Guangchuang

gsePC

Description

GSEA analysis for Pathway Commons

Usage

gsePC(geneList, eps = 1e-10, ...)

Arguments

geneList

a ranked gene list
eps

boundary for calculating the p value in multilevel mode
...

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, eps = 1e-10, ...)

Arguments

geneList

ranked gene list
organism

supported organisms, which can be accessed via the get_wp_organisms() function
eps

boundary for calculating the p value in multilevel mode
...

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

gson_KEGG

Description

download the latest version of KEGG pathway and stored in a 'GSON' object

Usage

gson_GO(OrgDb, keytype = "ENTREZID", ont = "BP")

Arguments

OrgDb

OrgDb
keytype

keytype of genes.
ont

one of "BP", "MF", "CC", and "ALL"

Value

a 'GSON' object

Build a gson object that annotate Gene Ontology

Description

Build a gson object that annotate Gene Ontology

Usage

gson_GO_local(data, ont = c("ALL", "BP", "CC", "MF"), species = NULL, ...)

Arguments

data

a two-column data.frame of original GO annotation. The columns are "gene_id" and "go_id".
ont

type of GO annotation, which is "ALL", "BP", "MF", or "CC". default: "ALL".
species

name of species. Default: NULL.
...

pass to 'gson::gson()' constructor.

Value

a 'gson' instance

Examples

data = data.frame(gene_id = "gene1",
                   go_id = c("GO:0035492", "GO:0009764", "GO:0031040", "GO:0033714", "GO:0036349"))
 gson_GO_local(data, species = "E. coli")

gson_KEGG

Description

download the latest version of KEGG pathway and stored in a 'GSON' object

Usage

gson_KEGG(species, KEGG_Type = "KEGG", keyType = "kegg")

Arguments

species

species
KEGG_Type

one of "KEGG" and "MKEGG"
keyType

one of "kegg", 'ncbi-geneid', 'ncib-proteinid', 'uniprot', and 'ko'.

Value

a 'GSON' object

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,
  ...
)

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)

gson_KO

Description

Download KEGG Orthology annotation as a GSON object

Usage

gson_KO()

Value

a 'GSON' object

gson_WP

Description

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

Usage

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")

Arguments

OrgDb

annotation db

Value

character vector

Author(s)

Guangchuang Yu

Interpret Enrichment Results Using LLMs

Description

Functions for interpreting functional enrichment analysis results using Large Language Models. Supports single-call interpretation, multi-agent deep analysis, and hierarchical cluster strategies.

Built on top of aisdk's 'generate_object()' for reliable structured output, and the Agent/Session system for multi-agent pipelines.

Sends enrichment results along with optional experimental context to an LLM to generate a structured biological interpretation, hypothesis, and narrative suitable for a publication.

Usage

interpret(
  x,
  context = NULL,
  n_pathways = NULL,
  model = NULL,
  add_ppi = FALSE,
  gene_fold_change = NULL,
  gene_stats = NULL,
  max_tokens = 8192,
  temperature = 0.3,
  reduce = TRUE,
  reduce_threshold = NULL,
  reasoning = NULL,
  verbose = FALSE,
  reduce_go = NULL,
  go_reduction_threshold = NULL
)

Arguments

x

An enrichment result object (enrichResult, gseaResult, compareClusterResult, or a data.frame with pathway columns).
context

A string describing the experimental background (e.g., "scRNA-seq of mouse myocardial infarction at day 3").
n_pathways

Maximum number of terms (per cluster) sent to the model, taken in order of adjusted p-value after reduction. NULL (default) sends every term that passes the object's cutoffs; with reduce = TRUE that is one representative per theme. On five public lists a cap below the number of representatives named fewer themes of the full result; for a model with a short context, lower reduce_threshold (for example 0.3) before setting a cap.
model

Optional LLM model. When NULL (default), uses the aisdk package-wide default model configured via aisdk::set_model(). You can also supply a model ID in provider:model format (e.g., "deepseek:deepseek-chat", "gemini:gemini-2.5-flash") or a LanguageModelV1 object. Bare model names are supported with a warning (e.g., "deepseek-chat").
add_ppi

Logical, whether to query STRING PPI network data. Default FALSE.
gene_fold_change

Named numeric vector of log fold changes for expression context. For a gseaResult left NULL, the ranked list the GSEA was run on (x@geneList) is used, so the leading-edge genes reach the model with their values.
gene_stats

Optional data frame of per-gene statistics from the differential analysis behind x (a limma topTable(), DESeq2 results(), edgeR topTags() or Seurat FindMarkers() table). Recognised columns: the gene (gene, SYMBOL, symbol, gene_symbol, ENTREZID, ID, or the row names), the log fold change (log2FC, logFC, log2FoldChange, avg_log2FC) and the adjusted p-value (padj, adj.P.Val, FDR, p_val_adj, p.adjust). When supplied, the genes of the enriched terms are listed with log fold change and FDR, and the most significant up- and down-regulated genes are added, so that the genes the model names are read from the data rather than from the term names. It also supplies gene_fold_change when that is NULL.
max_tokens

Maximum tokens for the LLM response. Default 8192. Some models (especially reasoning models) may need much higher values (e.g., 16384 or more) to produce complete structured output.
temperature

Sampling temperature. Default 0.3.
reduce

Logical. Run reduce_redundancy() before building the prompt, so every significant theme reaches the model once: Wang similarity for GO results, Jaccard overlap of member genes for KEGG and other gene-set collections. Default TRUE; a no-op for data frames and lists.
reduce_threshold

Similarity threshold passed to reduce_redundancy(). NULL (default) uses its default of 0.5.
reasoning

Optional reasoning/thinking configuration passed through to the provider, e.g. list(type = "enabled", budget_tokens = 4096). Left NULL the request is byte-identical to one made before this argument existed. Whatever is passed is recorded in the execution stamp as reasoning_requested, alongside reasoning_evidence – whether the response actually carried a reasoning block.
verbose

Logical, whether to print debug messages showing raw API responses, token usage, and JSON parsing details. Default FALSE. Equivalent to setting options(aisdk.debug = TRUE) for the call.
reduce_go, go_reduction_threshold

Earlier names of reduce and reduce_threshold, still accepted; a call that passes reduce_go keeps its earlier default threshold of 0.7.

Details

Uses generate_object() internally for reliable structured output with automatic JSON repair, eliminating manual parsing failures.

Value

An interpretation object (list): overview, regulatory_drivers, key_mechanisms, crosstalk, hypothesis, narrative, refined_network and network_evidence. interpret() interprets the enrichment result and nothing else; to combine it with the results of other tools (a SingleR label, a TF-activity ranking, a complex enrichment) and reach a conclusion, use interpret_agent() with prior.

Examples

## Not run: 
# Basic usage with a data frame
df <- data.frame(
  ID = c("GO:0006915", "GO:0008284"),
  Description = c("apoptotic process", "positive regulation of proliferation"),
  GeneRatio = c("10/100", "20/100"),
  p.adjust = c(0.01, 0.02),
  geneID = c("TP53/BAX", "MYC/CCND1/CDK4")
)
res <- interpret(df,
  model = "deepseek:deepseek-chat",
  context = "Cancer proliferation study"
)
# Every significant term, one representative per theme (the default)
ego <- enrichGO(gene = genes, OrgDb = org.Mm.eg.db, ont = "BP")
res <- interpret(ego)
# A shorter prompt for a model with a short context: merge more terms per theme
res <- interpret(ego, reduce_threshold = 0.3)
# Reuse aisdk's global default model
# aisdk::set_model("openai:gpt-4o-mini")
# res <- interpret(df, context = "Cancer proliferation study")
print(res)

## End(Not run)

Interpret enrichment results using a multi-agent pipeline (Deep Mode)

Description

Employs specialized AI agents in sequence for rigorous interpretation, and is the entry point that combines the enrichment evidence with the results of other tools (prior) into a conclusion:

  1. Agent Cleaner: Filters noise and selects relevant pathways.

  2. Agent Detective: Identifies key regulators and functional modules.

  3. Agent Synthesizer: Produces a coherent biological narrative.

  4. Agent Reviewer (optional, review = TRUE): Audits the draft for grounding, overclaiming, and experimental-label fidelity; flags issues and returns a corrected overview.

Usage

interpret_agent(
  x,
  context = NULL,
  prior = NULL,
  n_pathways = NULL,
  model = NULL,
  add_ppi = FALSE,
  gene_fold_change = NULL,
  max_tokens = 8192,
  temperature = 0.3,
  review = FALSE,
  reduce = TRUE,
  reduce_threshold = NULL,
  reasoning = NULL,
  verbose = FALSE,
  reduce_go = NULL,
  go_reduction_threshold = NULL
)

Arguments

x

An enrichment result object.
context

A string describing the experimental background. State the question the analysis should answer here (for example "which cell type is this cluster?"); the answer is returned as conclusion.
prior

Optional results of other tools to be checked against the enrichment evidence, as text: a SingleR or scGPT label, a ranked list of transcription factors from a regulon analysis, the protein complexes enriched in the gene list, and so on. A single string applies to every cluster; a named character vector or list supplies one entry per cluster. The Detective and the Synthesizer see it as a claim to check, not as evidence, and the result gains prior_verdict (Confirmed, Refined, Corrected or Not assessable) and prior_rationale.
n_pathways

Maximum number of terms (per cluster) the Cleaner starts from, taken in order of adjusted p-value after reduction. NULL (default) uses every term that passes the object's cutoffs; with reduce = TRUE that is one representative per theme.
model

Optional LLM model. When NULL (default), uses the aisdk package-wide default model configured via aisdk::set_model(). You can also supply a model ID in provider:model format or a LanguageModelV1 object. Bare model names are supported with a warning.
add_ppi

Logical, whether to query PPI data. Default FALSE.
gene_fold_change

Named numeric vector of log fold changes. When supplied, every input gene is also listed to the Detective and the Synthesizer, most up-regulated first.
max_tokens

Maximum tokens per agent call. Default 8192.
temperature

Sampling temperature. Default 0.3.
review

Logical, whether to run a final Reviewer agent (Agent 4) that audits the synthesized draft against the input evidence for grounding, overclaiming, experimental-label fidelity, and evidence sufficiency (whether the supplied evidence classes can support a claim as specific as the draft makes). When TRUE, the result gains a review field (verdict, grounding_score, evidence_sufficiency, supported_resolution, abstain_recommended, issues, adjusted_confidence, warnings) and overview_original; if the verdict is "revise", overview is replaced by the corrected version, in which claims exceeding supported_resolution are downgraded to that resolution. Default FALSE, which reproduces the released three-agent behavior exactly: the audit is an addition a caller opts into, and leaving it off keeps existing scripts measuring what they measured before. Note that the audit roughly doubles wall-clock time per unit and adds one model call, and that it is not infallible – on objects where the Synthesizer returned nothing it has been observed to return pass with a High grounding score.
reduce

Logical. Run reduce_redundancy() before building any prompt, as interpret() does: Wang similarity for GO results, Jaccard overlap of member genes for other gene-set collections. Deterministic and makes no model call. Default TRUE.
reduce_threshold

Similarity threshold passed to reduce_redundancy(). NULL (default) uses its default of 0.5.
reasoning

Optional reasoning/thinking configuration passed through to the provider, e.g. list(type = "enabled", budget_tokens = 4096). Left NULL the request is byte-identical to one made before this argument existed. Whatever is passed is recorded in the execution stamp as reasoning_requested, alongside reasoning_evidence – whether the response actually carried a reasoning block. The two are separate because a gateway can accept the parameter and drop it, and the caller can only observe the second.
verbose

Logical, whether to print debug messages. Default FALSE.
reduce_go, go_reduction_threshold

Earlier names of reduce and reduce_threshold, still accepted; a call that passes reduce_go keeps its earlier default threshold of 0.7.

Details

Uses aisdk's Agent and Session system for shared context across agents.

Value

An interpretation object with deep analysis fields, conclusion (the answer to the question the context poses), plus regulatory_drivers, refined_network, and network_evidence from the detective agent. When prior is supplied, also prior_verdict and prior_rationale. When review = TRUE, also includes review and overview_original. The review field reports grounding separately from resolution: abstain_recommended = "yes" flags a draft whose cited evidence is real but which resolves to a more specific entity than that evidence can single out, with supported_resolution giving the claim the evidence does support.

Examples

## Not run: 
res <- interpret_agent(df,
  model = "openai:gpt-4o",
  context = "scRNA-seq of mouse MI day 3"
)
print(res)
# Check another tool's answer against the enrichment evidence
res <- interpret_agent(ego_cluster3,
  context = "PBMC scRNA-seq; which cell type is cluster 3?",
  gene_fold_change = fc3, prior = "SingleR (HPCA): T_cell:CD4+_Naive"
)
res$conclusion; res$prior_verdict

## End(Not run)

Interpret enrichment results using a hierarchical strategy

Description

First interprets major clusters to establish lineage context, then interprets sub-clusters with hierarchical constraints from the major cluster annotations.

Usage

interpret_hierarchical(
  x_minor,
  x_major,
  mapping,
  model = NULL,
  max_tokens = 8192,
  temperature = 0.3,
  n_pathways = NULL,
  reduce = TRUE,
  reduce_threshold = NULL
)

Arguments

x_minor

Enrichment result for sub-clusters.
x_major

Enrichment result for major clusters.
mapping

A named vector mapping sub-cluster IDs to major cluster IDs.
model

Optional LLM model. When 'NULL' (default), uses the aisdk package-wide default model configured via 'aisdk::set_model()'. You can also supply a model ID in 'provider:model' format or a 'LanguageModelV1' object. Bare model names are supported with a warning.
max_tokens

Maximum tokens. Default 8192.
temperature

Sampling temperature. Default 0.3.
n_pathways, reduce, reduce_threshold

Passed to both levels, as in interpret(): by default every significant term, one representative per theme.

Value

An 'interpretation_list' object with a cell-type label ('cell_type', 'confidence', 'reasoning', 'markers') per sub-cluster.

ko2name

Description

convert ko ID to descriptive name

Usage

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)

Arguments

enrichResultList

a list of enrichResult objects

Value

a compareClusterResult instance

Author(s)

Guangchuang Yu

mnseGO

Description

Multi-layer Network-based Gene Set Enrichment Analysis of Gene Ontology

Usage

mnseGO(
  seed_list,
  networks,
  couplings,
  ont = "BP",
  OrgDb,
  keyType = "ENTREZID",
  mode = c("evidence", "signed"),
  layer_weights = NULL,
  collapse = c("weighted_mean", "sum", "mean", "max_abs"),
  target_layer = NULL,
  output_space = c("union", "gene"),
  p = 0.5,
  interlayer_strength = 1,
  specific_weight = FALSE,
  minGSSize = 10,
  maxGSSize = 500,
  threshold = 1e-09,
  maxIter = 100,
  verbose = TRUE,
  ...
)

Arguments

seed_list

named list of named numeric vectors, one per layer
networks

named list of layer-specific networks
couplings

data.frame of inter-layer edges
ont

one of "BP", "MF", and "CC" subontologies, or "ALL" for all three.
OrgDb

OrgDb
keyType

keytype of gene
mode

propagation mode, one of "evidence" and "signed"
layer_weights

optional named numeric vector of layer weights
collapse

one of "weighted_mean", "sum", "mean", or "max_abs"
target_layer

optional layer name to export scores from
output_space

one of "union" and "gene"
p

restart probability for RWR
interlayer_strength

global scaling factor for coupling edges
specific_weight

logical, whether to apply gene specificity weighting
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
threshold

convergence threshold for RWR
maxIter

maximal number of RWR iterations
verbose

print message or not
...

other parameters passed to enrichit::mnsea_gson()

Value

mnseaResult object

mnseKEGG

Description

Multi-layer Network-based Gene Set Enrichment Analysis of KEGG

Usage

mnseKEGG(
  seed_list,
  networks,
  couplings,
  organism = "hsa",
  keyType = "kegg",
  mode = c("evidence", "signed"),
  layer_weights = NULL,
  collapse = c("weighted_mean", "sum", "mean", "max_abs"),
  target_layer = NULL,
  output_space = c("union", "gene"),
  p = 0.5,
  interlayer_strength = 1,
  specific_weight = FALSE,
  minGSSize = 10,
  maxGSSize = 500,
  threshold = 1e-09,
  maxIter = 100,
  verbose = TRUE,
  use_internal_data = FALSE,
  ...
)

Arguments

seed_list

named list of named numeric vectors, one per layer
networks

named list of layer-specific networks
couplings

data.frame of inter-layer edges
organism

supported organism listed in 'https://www.genome.jp/kegg/catalog/org_list.html', or a GSON object
keyType

one of "kegg", 'ncbi-geneid', 'ncbi-proteinid' and 'uniprot'
mode

propagation mode, one of "evidence" and "signed"
layer_weights

optional named numeric vector of layer weights
collapse

one of "weighted_mean", "sum", "mean", or "max_abs"
target_layer

optional layer name to export scores from
output_space

one of "union" and "gene"
p

restart probability for RWR
interlayer_strength

global scaling factor for coupling edges
specific_weight

logical, whether to apply gene specificity weighting
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
threshold

convergence threshold for RWR
maxIter

maximal number of RWR iterations
verbose

print message or not
use_internal_data

logical, use KEGG.db or latest online KEGG data
...

other parameters passed to enrichit::mnsea_gson()

Value

mnseaResult object

mnseMKEGG

Description

Multi-layer Network-based Gene Set Enrichment Analysis of KEGG Module

Usage

mnseMKEGG(
  seed_list,
  networks,
  couplings,
  organism = "hsa",
  keyType = "kegg",
  mode = c("evidence", "signed"),
  layer_weights = NULL,
  collapse = c("weighted_mean", "sum", "mean", "max_abs"),
  target_layer = NULL,
  output_space = c("union", "gene"),
  p = 0.5,
  interlayer_strength = 1,
  specific_weight = FALSE,
  minGSSize = 10,
  maxGSSize = 500,
  threshold = 1e-09,
  maxIter = 100,
  verbose = TRUE,
  ...
)

Arguments

seed_list

named list of named numeric vectors, one per layer
networks

named list of layer-specific networks
couplings

data.frame of inter-layer edges
organism

supported organism listed in 'https://www.genome.jp/kegg/catalog/org_list.html', or a GSON object
keyType

one of "kegg", 'ncbi-geneid', 'ncbi-proteinid' and 'uniprot'
mode

propagation mode, one of "evidence" and "signed"
layer_weights

optional named numeric vector of layer weights
collapse

one of "weighted_mean", "sum", "mean", or "max_abs"
target_layer

optional layer name to export scores from
output_space

one of "union" and "gene"
p

restart probability for RWR
interlayer_strength

global scaling factor for coupling edges
specific_weight

logical, whether to apply gene specificity weighting
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
threshold

convergence threshold for RWR
maxIter

maximal number of RWR iterations
verbose

print message or not
...

other parameters passed to enrichit::mnsea_gson()

Value

mnseaResult object

mnseWP

Description

Multi-layer Network-based Gene Set Enrichment Analysis of WikiPathways

Usage

mnseWP(
  seed_list,
  networks,
  couplings,
  organism,
  mode = c("evidence", "signed"),
  layer_weights = NULL,
  collapse = c("weighted_mean", "sum", "mean", "max_abs"),
  target_layer = NULL,
  output_space = c("union", "gene"),
  p = 0.5,
  interlayer_strength = 1,
  specific_weight = FALSE,
  minGSSize = 10,
  maxGSSize = 500,
  threshold = 1e-09,
  maxIter = 100,
  verbose = TRUE,
  ...
)

Arguments

seed_list

named list of named numeric vectors, one per layer
networks

named list of layer-specific networks
couplings

data.frame of inter-layer edges
organism

supported organisms listed by get_wp_organisms(), or a GSON object
mode

propagation mode, one of "evidence" and "signed"
layer_weights

optional named numeric vector of layer weights
collapse

one of "weighted_mean", "sum", "mean", or "max_abs"
target_layer

optional layer name to export scores from
output_space

one of "union" and "gene"
p

restart probability for RWR
interlayer_strength

global scaling factor for coupling edges
specific_weight

logical, whether to apply gene specificity weighting
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
threshold

convergence threshold for RWR
maxIter

maximal number of RWR iterations
verbose

print message or not
...

other parameters passed to enrichit::mnsea_gson()

Value

mnseaResult object

nseGO

Description

Network-based Gene Set Enrichment Analysis of Gene Ontology

Usage

nseGO(
  geneList,
  network,
  ont = "BP",
  OrgDb,
  keyType = "ENTREZID",
  mode = c("evidence", "signed"),
  p = 0.5,
  specific_weight = FALSE,
  minGSSize = 10,
  maxGSSize = 500,
  threshold = 1e-09,
  maxIter = 100,
  verbose = TRUE,
  ...
)

Arguments

geneList

order ranked geneList
network

edge list (data.frame with 2 or 3 columns) or sparse matrix
ont

one of "BP", "MF", and "CC" subontologies, or "ALL" for all three.
OrgDb

OrgDb
keyType

keytype of gene
mode

propagation mode, one of "evidence" and "signed"
p

restart probability for RWR
specific_weight

logical, whether to apply gene specificity weighting
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
threshold

convergence threshold for RWR
maxIter

maximal number of RWR iterations
verbose

print message or not
...

other parameters passed to enrichit::nsea_gson()

Value

nseaResult object

nseKEGG

Description

Network-based Gene Set Enrichment Analysis of KEGG

Usage

nseKEGG(
  geneList,
  network,
  organism = "hsa",
  keyType = "kegg",
  mode = c("evidence", "signed"),
  p = 0.5,
  specific_weight = FALSE,
  minGSSize = 10,
  maxGSSize = 500,
  threshold = 1e-09,
  maxIter = 100,
  verbose = TRUE,
  use_internal_data = FALSE,
  ...
)

Arguments

geneList

order ranked geneList
network

edge list (data.frame with 2 or 3 columns) or sparse matrix
organism

supported organism listed in 'https://www.genome.jp/kegg/catalog/org_list.html', or a GSON object
keyType

one of "kegg", 'ncbi-geneid', 'ncbi-proteinid' and 'uniprot'
mode

propagation mode, one of "evidence" and "signed"
p

restart probability for RWR
specific_weight

logical, whether to apply gene specificity weighting
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
threshold

convergence threshold for RWR
maxIter

maximal number of RWR iterations
verbose

print message or not
use_internal_data

logical, use KEGG.db or latest online KEGG data
...

other parameters passed to enrichit::nsea_gson()

Value

nseaResult object

nseMKEGG

Description

Network-based Gene Set Enrichment Analysis of KEGG Module

Usage

nseMKEGG(
  geneList,
  network,
  organism = "hsa",
  keyType = "kegg",
  mode = c("evidence", "signed"),
  p = 0.5,
  specific_weight = FALSE,
  minGSSize = 10,
  maxGSSize = 500,
  threshold = 1e-09,
  maxIter = 100,
  verbose = TRUE,
  ...
)

Arguments

geneList

order ranked geneList
network

edge list (data.frame with 2 or 3 columns) or sparse matrix
organism

supported organism listed in 'https://www.genome.jp/kegg/catalog/org_list.html', or a GSON object
keyType

one of "kegg", 'ncbi-geneid', 'ncbi-proteinid' and 'uniprot'
mode

propagation mode, one of "evidence" and "signed"
p

restart probability for RWR
specific_weight

logical, whether to apply gene specificity weighting
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
threshold

convergence threshold for RWR
maxIter

maximal number of RWR iterations
verbose

print message or not
...

other parameters passed to enrichit::nsea_gson()

Value

nseaResult object

nseWP

Description

Network-based Gene Set Enrichment Analysis of WikiPathways

Usage

nseWP(
  geneList,
  network,
  organism,
  mode = c("evidence", "signed"),
  p = 0.5,
  specific_weight = FALSE,
  minGSSize = 10,
  maxGSSize = 500,
  threshold = 1e-09,
  maxIter = 100,
  verbose = TRUE,
  ...
)

Arguments

geneList

order ranked geneList
network

edge list (data.frame with 2 or 3 columns) or sparse matrix
organism

supported organisms listed by get_wp_organisms(), or a GSON object
mode

propagation mode, one of "evidence" and "signed"
p

restart probability for RWR
specific_weight

logical, whether to apply gene specificity weighting
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of genes annotated for testing
threshold

convergence threshold for RWR
maxIter

maximal number of RWR iterations
verbose

print message or not
...

other parameters passed to enrichit::nsea_gson()

Value

nseaResult object

plot

Description

plot

Usage

## S3 method for class 'interpretation'
plot(x, layout = "nicely", ...)

Arguments

x

An 'interpretation' object.
layout

Graph layout, default is "nicely".
...

Additional arguments passed to 'ggplot2::ggplot'.

plotGOgraph

Description

plot GO graph

Usage

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

Execution provenance of an interpretation

Description

Returns the execution record attached to a result of interpret(), interpret_agent() or interpret_hierarchical(): the model actually addressed, the decoding parameters requested (and whether the response confirmed them), SHA-256 digests of the system prompt, user prompt, raw response and input evidence, token usage, finish_reason, per-call status and package versions.

Usage

provenance(x)

Arguments

x

An interpretation, interpretation_list or agent result.

Details

A remote model is not a versionable dependency. Without this record an interpretation cannot be reproduced, and a call that failed at the transport layer is indistinguishable from a model that had nothing to say – both arrive as an ordinary result object. Check provenance(x)$ok before treating any interpretation as data.

Value

An interpret_provenance list, or NULL if the object carries no stamp.

read.gmt.pc

Description

Parse gmt file from Pathway Common

Usage

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'

Reduce GO term redundancy by semantic similarity

Description

Applies semantic similarity-based clustering to remove redundant GO terms before LLM interpretation. This algorithmic preprocessing complements the multi-agent interpretation layer by reducing input noise and token count.

Usage

reduce_go_redundancy(
  enrich_result,
  threshold = 0.7,
  orgdb = NULL,
  method = "Wang"
)

Arguments

enrich_result

An enrichResult object from enrichGO()
threshold

Semantic similarity threshold (0-1). Higher values are more aggressive. Default 0.7 means terms with >70% similarity are clustered.
orgdb

OrgDb package name (e.g., "org.Hs.eg.db"). If NULL, auto-detected from the enrichResult organism field.
method

Similarity measure passed to GOSemSim::goSim(). Default "Wang", which reads the GO DAG directly and needs no corpus statistics. The information-content measures "Resnik", "Lin", "Rel" and "Jiang" are accepted but require godata(computeIC = TRUE) and are substantially slower.

Details

Implemented directly on GOSemSim, which is already an Imports dependency. The ⁠rrvgo_*⁠ attribute names are kept for backwards compatibility with existing ablation outputs and predate the switch away from rrvgo.

Value

A reduced enrichResult object with redundant terms removed. The object carries attributes recording the reduction: rrvgo_reduced, rrvgo_original_n, rrvgo_reduced_n, rrvgo_threshold, rrvgo_method.

Examples

## Not run: 
ego <- enrichGO(gene = genes, OrgDb = org.Mm.eg.db, ont = "BP")
ego_reduced <- reduce_go_redundancy(ego, threshold = 0.7)
message(sprintf("Reduced from %d to %d terms",
  attr(ego_reduced, "rrvgo_original_n"),
  attr(ego_reduced, "rrvgo_reduced_n")))

## End(Not run)

Reduce redundant terms in an enrichment result before interpretation

Description

Keeps one representative for each group of near-duplicate significant terms, so that every significant theme of a long result can be shown to the model without repeating its near-synonyms. GO results are grouped by Wang semantic similarity over the GO DAG (GOSemSim); every other gene-set collection (KEGG, Reactome, WikiPathways, MSigDB, custom TERM2GENE) by the Jaccard overlap of the member genes the result reports (geneID, or core_enrichment for GSEA). Groups are formed greedily in order of adjusted p-value: the most significant remaining term is kept and every remaining term more similar to it than threshold is dropped. Only terms that pass the object's cutoffs are considered; a compareClusterResult is reduced within each cluster, and a GO result spanning several ontologies within each ontology. Deterministic, and makes no model call. interpret() and interpret_agent() call it by default (reduce = TRUE).

Usage

reduce_redundancy(
  x,
  threshold = NULL,
  method = c("auto", "Wang", "jaccard"),
  semData = NULL
)

Arguments

x

An enrichResult, gseaResult or compareClusterResult.
threshold

Similarity above which a term joins the group of a more significant term. NULL (default) uses 0.5 for both measures.
method

"auto" (default) uses "Wang" when every term is a GO term and "jaccard" otherwise; either can be forced. Wang on non-GO terms falls back to Jaccard.
semData

Optional GOSemSimDATA for the Wang measure; built with GOSemSim::godata(ont = ) when NULL. Wang reads only the GO DAG, so no OrgDb is needed.

Value

x with the redundant significant terms removed. Attributes record the step: reduced, n_original, n_reduced, reduction_method and reduction_threshold, and the legacy ⁠rrvgo_*⁠ names read by earlier analysis scripts.

Examples

## Not run: 
ego <- enrichGO(gene = genes, OrgDb = org.Hs.eg.db, ont = "BP")
reduce_redundancy(ego)                 # Wang, 0.5
kk <- enrichKEGG(gene = genes, organism = "hsa")
reduce_redundancy(kk)                  # Jaccard on member genes, 0.5

## End(Not run)

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
)

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
)

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)

Arguments

taxID

taxonomy ID

Value

gene table data frame

Author(s)

guangchuang yu