Package 'enrichit'

Title: 'C++' Implementations of Functional Enrichment Analysis
Description: Fast implementations of functional enrichment analysis methods using 'C++' via 'Rcpp'. Currently provides Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). The multilevel GSEA algorithm is derived from the 'fgsea' package. Methods are described in Subramanian et al. (2005) <doi:10.1073/pnas.0506580102> and Korotkevich et al. (2021) <doi:10.1101/060012>.
Authors: Guangchuang Yu [aut, cre]
Maintainer: Guangchuang Yu <[email protected]>
License: Artistic-2.0
Version: 0.1.4
Built: 2026-05-08 09:04:43 UTC
Source: https://github.com/cran/enrichit

Help Index

Class "compareClusterResult" This class represents the comparison result of gene clusters by GO categories at specific level or GO enrichment analysis.

Description

Class "compareClusterResult" This class represents the comparison result of gene clusters by GO categories at specific level or GO enrichment analysis.

Slots

compareClusterResult

cluster comparing result

geneClusters

a list of genes

fun

one of groupGO, enrichGO and enrichKEGG

gene2Symbol

gene ID to Symbol

keytype

Gene ID type

readable

logical flag of gene ID in symbol or not.

.call

function call

termsim

Similarity between term

method

method of calculating the similarity between nodes

dr

dimension reduction result

organism

organism

Author(s)

Guangchuang Yu https://yulab-smu.top

See Also

enrichResult

Common parameters for enrichit functions

Description

Common parameters for enrichit functions

Arguments

geneList

A named numeric vector of gene statistics (e.g., log fold change), ranked in descending order.
gene_sets

A named list of gene sets. Each element is a character vector of genes.
nPerm

Number of permutations for p-value calculation (default: 1000).
exponent

Weighting exponent for enrichment score (default: 1.0).
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of each geneSet for analyzing
pvalueCutoff

P-value cutoff.
pAdjustMethod

P-value adjustment method (e.g., "BH").
verbose

Logical. Print progress messages.
gson

A GSON object containing gene set information.
method

Permutation method.
adaptive

Logical. Use adaptive permutation.
minPerm

Minimum permutations for adaptive mode.
maxPerm

Maximum permutations for adaptive mode.
pvalThreshold

P-value threshold for early stopping.

Class "enrichResult" This class represents the result of enrichment analysis.

Description

Class "enrichResult" This class represents the result of enrichment analysis.

Slots

result

enrichment analysis

pvalueCutoff

pvalueCutoff

pAdjustMethod

pvalue adjust method

qvalueCutoff

qvalueCutoff

organism

only "human" supported

ontology

biological ontology

gene

Gene IDs

keytype

Gene ID type

universe

background gene

gene2Symbol

mapping gene to Symbol

geneSets

gene sets

readable

logical flag of gene ID in symbol or not.

termsim

Similarity between term

method

method of calculating the similarity between nodes

dr

dimension reduction result

Author(s)

Guangchuang Yu https://yulab-smu.top

EXTID2NAME

Description

mapping gene ID to gene Symbol

Usage

EXTID2NAME(OrgDb, geneID, keytype, toType = "SYMBOL")

Arguments

OrgDb

OrgDb
geneID

entrez gene ID
keytype

keytype
toType

ID type of the output

Value

gene symbol

Author(s)

Guangchuang Yu https://yulab-smu.top

geneID generic

Description

geneID generic

Usage

geneID(x)

Arguments

x

enrichResult object

Value

'geneID' return the 'geneID' column of the enriched result which can be converted to data.frame via 'as.data.frame'

Examples

data(geneList, package="DOSE")
de <- names(geneList)[1:100]
x <- DOSE::enrichDO(de)
geneID(x)

geneInCategory generic

Description

geneInCategory generic

Usage

geneInCategory(x)

Arguments

x

enrichResult

Value

'geneInCategory' return a list of genes, by spliting the input gene vector to enriched functional categories

Examples

data(geneList, package="DOSE")
de <- names(geneList)[1:100]
x <- DOSE::enrichDO(de)
geneInCategory(x)

Gene Set Enrichment Analysis (GSEA)

Description

Perform Gene Set Enrichment Analysis (GSEA) using a ranked gene list.

Usage

gsea(
  geneList,
  gene_sets,
  minGSSize = 10,
  maxGSSize = 500,
  nPerm = 1000,
  exponent = 1,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 101,
  maxPerm = 1e+05,
  pvalThreshold = 0.1,
  eps = 1e-10,
  sampleSize = 101,
  seed = FALSE,
  nPermSimple = 1000,
  scoreType = "std",
  verbose = TRUE
)

Arguments

geneList

A named numeric vector of gene statistics (e.g., log fold change), ranked in descending order.
gene_sets

A named list of gene sets. Each element is a character vector of genes.
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of each geneSet for analyzing
nPerm

Number of permutations for p-value calculation (default: 1000).
exponent

Weighting exponent for enrichment score (default: 1.0).
method

Permutation method.
adaptive

Logical. Use adaptive permutation.
minPerm

Minimum permutations for adaptive mode.
maxPerm

Maximum permutations for adaptive mode.
pvalThreshold

P-value threshold for early stopping.
eps

Epsilon for multilevel methods (default: 1e-10). Sets the smallest p-value that can be estimated.
sampleSize

Sample size for multilevel methods (default: 101).
seed

Random seed for reproducibility (default: FALSE). If FALSE, a random seed is generated.
nPermSimple

Number of permutations for the simple method (default: 1000).
scoreType

Type of enrichment score calculation: "std", "pos", "neg" (default: "std").
verbose

Logical. Print progress messages.

Value

A data.frame with columns:

  • ID: Gene set name

  • enrichmentScore: Enrichment Score

  • NES: Normalized Enrichment Score

  • pvalue: Empirical p-value from permutation test

  • setSize: Size of the gene set (number of genes found in geneList)

  • nPerm: (adaptive mode only) Actual number of permutations used

  • rank: Rank at which the maximum enrichment score is attained

  • leading_edge: Leading edge statistics (tags, list, signal)

  • core_enrichment: Genes in the leading edge, separated by '/'

Examples

# Example data
stats <- rnorm(1000)
names(stats) <- paste0("Gene", 1:1000)
stats <- sort(stats, decreasing = TRUE)

gs1 <- paste0("Gene", 1:50)
gs2 <- paste0("Gene", 500:550)
gene_sets <- list(Pathway1 = gs1, Pathway2 = gs2)

# Use default fixed permutation method
result <- gsea(geneList=stats, gene_sets=gene_sets, nPerm=100)

# Use adaptive permutation for more accurate p-values

result_adaptive <- gsea(geneList=stats, gene_sets=gene_sets, adaptive=TRUE)

gsea_gson

Description

generic function for gene set enrichment analysis

Usage

gsea_gson(
  geneList,
  gson,
  nPerm = 1000,
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 101,
  maxPerm = 1e+05,
  pvalThreshold = 0.1,
  verbose = TRUE,
  ...
)

Arguments

geneList

A named numeric vector of gene statistics (e.g., log fold change), ranked in descending order.
gson

A GSON object containing gene set information.
nPerm

Number of permutations for p-value calculation (default: 1000).
exponent

Weighting exponent for enrichment score (default: 1.0).
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of each geneSet for analyzing
pvalueCutoff

P-value cutoff.
pAdjustMethod

P-value adjustment method (e.g., "BH").
method

Permutation method.
adaptive

Logical. Use adaptive permutation.
minPerm

Minimum permutations for adaptive mode.
maxPerm

Maximum permutations for adaptive mode.
pvalThreshold

P-value threshold for early stopping.
verbose

Logical. Print progress messages.
...

Additional parameters passed to gsea()

Value

gseaResult object

Author(s)

Guangchuang Yu

Class "gseaResult" This class represents the result of GSEA analysis

Description

Class "gseaResult" This class represents the result of GSEA analysis

Slots

result

GSEA anaysis

organism

organism

setType

setType

geneSets

geneSets

geneList

order rank geneList

keytype

ID type of gene

permScores

permutation scores

params

parameters

gene2Symbol

gene ID to Symbol

readable

whether convert gene ID to symbol

dr

dimension reduction result

Author(s)

Guangchuang Yu https://yulab-smu.top

Calculate GSEA Running Enrichment Scores

Description

Calculate GSEA Running Enrichment Scores

Usage

gseaScores(geneList, geneSet, exponent = 1, fortify = FALSE)

Arguments

geneList

a named numeric vector of gene statistics (e.g., t-statistics or log-fold changes), sorted in decreasing order.
geneSet

a character vector of gene IDs belonging to the gene set.
exponent

a numeric value defining the weight of the running enrichment score. Default is 1.
fortify

logical. If TRUE, returns a data frame with columns x, runningScore, and position. If FALSE (default), returns the enrichment score (ES).

Value

If fortify = TRUE, a data frame containing the running enrichment scores and positions. If fortify = FALSE, a numeric value representing the Enrichment Score (ES).

Author(s)

Guangchuang Yu

gsfilter

Description

filter enriched result by gene set size or gene count

Usage

gsfilter(x, by = "GSSize", min = NA, max = NA)

Arguments

x

instance of enrichResult or compareClusterResult
by

one of 'GSSize' or 'Count'
min

minimal size
max

maximal size

Value

update object

Author(s)

Guangchuang Yu

Over-Representation Analysis (ORA)

Description

Perform over-representation analysis using hypergeometric test (Fisher's exact test).

Usage

ora(gene, gene_sets, universe)

Arguments

gene

Character vector of differentially expressed genes (or gene list of interest).
gene_sets

A named list of gene sets. Each element is a character vector of genes.
universe

Character vector of background genes (e.g., all genes in the platform).

Value

A data.frame with columns:

GeneSet

Gene set name
SetSize

Number of genes in the gene set (intersected with universe)
DEInSet

Number of differentially expressed genes in the gene set
DESize

Total number of differentially expressed genes in universe
PValue

Raw p-value from hypergeometric test

Examples

# Example data
de_genes <- c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5")
all_genes <- paste0("Gene", 1:1000)

gs1 <- paste0("Gene", 1:50)
gs2 <- paste0("Gene", 51:150)
gs3 <- paste0("Gene", 151:300)
gene_sets <- list(Pathway1 = gs1, Pathway2 = gs2, Pathway3 = gs3)

result <- ora(gene=de_genes, gene_sets=gene_sets, universe=all_genes)
head(result)

ora-gson

Description

interal method for enrichment analysis

Usage

ora_gson(
  gene,
  pvalueCutoff,
  pAdjustMethod = "BH",
  universe = NULL,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  gson
)

Arguments

gene

a vector of entrez gene id.
pvalueCutoff

P-value cutoff.
pAdjustMethod

P-value adjustment method (e.g., "BH").
universe

background genes, default is the intersection of the 'universe' with genes that have annotations. Users can set options(enrichment_force_universe = TRUE) to force the 'universe' untouched.
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of each geneSet for analyzing
qvalueCutoff

cutoff of qvalue
gson

A GSON object containing gene set information.

Details

using the hypergeometric model

Value

A enrichResult instance.

Author(s)

Guangchuang Yu https://yulab-smu.top

setReadable

Description

mapping geneID to gene Symbol

Usage

setReadable(x, OrgDb, keyType = "auto", toType = "SYMBOL")

Arguments

x

enrichResult Object
OrgDb

OrgDb
keyType

keyType of gene
toType

ID type of the output

Value

enrichResult Object

Author(s)

Guangchuang Yu

show method

Description

show method for gseaResult instance

show method for enrichResult instance

Usage

show(object)

show(object)

Arguments

object

A enrichResult instance.

Value

message

message

Author(s)

Guangchuang Yu https://yulab-smu.top

summary method

Description

summary method for gseaResult instance

summary method for enrichResult instance

Usage

summary(object, ...)

summary(object, ...)

Arguments

object

A enrichResult instance.
...

additional parameter

Value

A data frame

A data frame

Author(s)

Guangchuang Yu https://yulab-smu.top