Package 'DOSE' reference manual

Title:	Disease Ontology Semantic and Enrichment analysis
Description:	This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data.
Authors:	Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk [ctb], Giovanni Dall'Olio [ctb]
Maintainer:	Guangchuang Yu <[email protected]>
License:	Artistic-2.0
Version:	4.7.0
Built:	2026-05-29 10:31:02 UTC
Source:	https://github.com/bioc/DOSE

clusterSim

Description

semantic similarity between two gene clusters

Usage

clusterSim(
  cluster1,
  cluster2,
  ont = "HDO",
  organism = "hsa",
  measure = "Wang",
  combine = "BMA"
)
clusterSim(
  cluster1,
  cluster2,
  ont = "HDO",
  organism = "hsa",
  measure = "Wang",
  combine = "BMA"
)

Arguments

cluster1

a vector of gene IDs

cluster2

another vector of gene IDs

ont

one of "HDO", "HPO" and "MPO"

organism

one of "hsa" and "mmu"

measure

One of "Resnik", "Lin", "Rel", "Jiang" and "Wang" methods.

combine

One of "max", "avg", "rcmax", "BMA" methods, for combining

Details

given two gene clusters, this function calculates semantic similarity between them.

Value

similarity

Author(s)

Yu Guangchuang

Examples

## Not run: 
cluster1 <- c("835", "5261","241", "994")
cluster2 <- c("307", "308", "317", "321", "506", "540", "378", "388", "396")
clusterSim(cluster1, cluster2)

## End(Not run)
## Not run: 
cluster1 <- c("835", "5261","241", "994")
cluster2 <- c("307", "308", "317", "321", "506", "540", "378", "388", "396")
clusterSim(cluster1, cluster2)

## End(Not run)

compute information content

Description

compute information content

Usage

computeIC(ont = "HDO")
computeIC(ont = "HDO")

Arguments

ont

one of "DO", "HPO" and "MPO"

Author(s)

Guangchuang Yu https://yulab-smu.top

Datasets

Description

Information content and DO term to entrez gene IDs mapping

Shared parameters for DOSE functions

Description

Shared parameters for DOSE functions

Arguments

gene

a vector of entrez gene id

organism

one of "hsa" and "mmu"

ont

one of "HDO", "HPO" or "MPO"

pvalueCutoff

pvalue cutoff

pAdjustMethod

one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"

universe

background genes

minGSSize

minimal size of genes annotated by ontology term for testing

maxGSSize

maximal size of each geneSet for analyzing

qvalueCutoff

qvalue cutoff

readable

whether mapping gene ID to gene Name

geneList

order ranked geneList

exponent

weight of each step

nPerm

permutation numbers

verbose

print message or not

adaptive

logical, use adaptive permutation or not (default: FALSE)

minPerm

minimum number of permutations for adaptive mode (default: 1000)

maxPerm

maximum number of permutations for adaptive mode (default: 10000)

method

method of GSEA, one of "multilevel", "permute", "sample"

doseSim

Description

measuring similarities between two DO term vectors.

Usage

doseSim(DOID1, DOID2, measure = "Wang", ont = "HDO")

doSim(DOID1, DOID2, measure = "Wang", ont = "HDO")
doseSim(DOID1, DOID2, measure = "Wang", ont = "HDO")

doSim(DOID1, DOID2, measure = "Wang", ont = "HDO")

Arguments

DOID1

DO term, MPO term or HPO term vector

DOID2

DO term, MPO term or HPO term vector

measure

one of "Wang", "Resnik", "Rel", "Jiang", "Lin", and "TCSS".

ont

one of "HDO", "HPO" and "MPO"

Details

provide two term vectors, this function will calculate their similarities.

Value

score matrix

Author(s)

Guangchuang Yu https://yulab-smu.top

Enrichment analysis based on the DisGeNET (http://www.disgenet.org/)

Description

given a vector of genes, this function will return the enrichment NCG categories with FDR control

Usage

enrichDGN(
  gene,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  readable = FALSE
)
enrichDGN(
  gene,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  readable = FALSE
)

Arguments

gene

a vector of entrez gene id

pvalueCutoff

pvalue cutoff

pAdjustMethod

one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"

universe

background genes

minGSSize

minimal size of genes annotated by ontology term for testing

maxGSSize

maximal size of each geneSet for analyzing

qvalueCutoff

qvalue cutoff

readable

whether mapping gene ID to gene Name

Value

A enrichResult instance

Author(s)

Guangchuang Yu

References

Janet et al. (2015) DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database bav028 http://database.oxfordjournals.org/content/2015/bav028.long

enrichDGN

Description

Enrichment analysis based on the DisGeNET (http://www.disgenet.org/)

Usage

enrichDGNv(
  snp,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  readable = FALSE
)
enrichDGNv(
  snp,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  readable = FALSE
)

Arguments

snp

a vector of SNP

pvalueCutoff

pvalue cutoff

pAdjustMethod

one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"

universe

background genes

minGSSize

minimal size of genes annotated by ontology term for testing

maxGSSize

maximal size of each geneSet for analyzing

qvalueCutoff

qvalue cutoff

readable

whether mapping gene ID to gene Name

Details

given a vector of genes, this function will return the enrichment NCG categories with FDR control

Value

A enrichResult instance

Author(s)

Guangchuang Yu

References

Janet et al. (2015) DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database bav028 http://database.oxfordjournals.org/content/2015/bav028.long

DO Enrichment Analysis

Description

Given a vector of genes, this function will return the enrichment DO categories with FDR control.

Usage

enrichDO(
  gene,
  ont = "HDO",
  organism = "hsa",
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  readable = FALSE
)
enrichDO(
  gene,
  ont = "HDO",
  organism = "hsa",
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  readable = FALSE
)

Arguments

gene

a vector of entrez gene id

ont

one of "HDO", "HPO" or "MPO"

organism

one of "hsa" and "mmu"

pvalueCutoff

pvalue cutoff

pAdjustMethod

one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"

universe

background genes

minGSSize

minimal size of genes annotated by ontology term for testing

maxGSSize

maximal size of each geneSet for analyzing

qvalueCutoff

qvalue cutoff

readable

whether mapping gene ID to gene Name

Value

A enrichResult instance.

Author(s)

Guangchuang Yu https://yulab-smu.top

Examples


data(geneList)
	gene = names(geneList)[geneList > 1]
	yy = enrichDO(gene, pvalueCutoff=0.05)
	summary(yy)

data(geneList)
	gene = names(geneList)[geneList > 1]
	yy = enrichDO(gene, pvalueCutoff=0.05)
	summary(yy)

enrichNCG

Description

Enrichment analysis based on the Network of Cancer Genes database (http://ncg.kcl.ac.uk/)

Usage

enrichNCG(
  gene,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  readable = FALSE
)
enrichNCG(
  gene,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  universe,
  minGSSize = 10,
  maxGSSize = 500,
  qvalueCutoff = 0.2,
  readable = FALSE
)

Arguments

gene

a vector of entrez gene id

pvalueCutoff

pvalue cutoff

pAdjustMethod

one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"

universe

background genes

minGSSize

minimal size of genes annotated by ontology term for testing

maxGSSize

maximal size of each geneSet for analyzing

qvalueCutoff

qvalue cutoff

readable

whether mapping gene ID to gene Name

Details

given a vector of genes, this function will return the enrichment NCG categories with FDR control

Value

A enrichResult instance

Author(s)

Guangchuang Yu

convert Gene ID to DO Terms

Description

provide gene ID, this function will convert to the corresponding DO Terms

Usage

gene2DO(gene, organism = "hsa", ont = "HDO")
gene2DO(gene, organism = "hsa", ont = "HDO")

Arguments

gene

entrez gene ID

organism

organism

ont

ont

Value

DO Terms

Author(s)

Guangchuang Yu https://yulab-smu.top

geneSim

Description

measuring similarities bewteen two gene vectors.

Usage

geneSim(
  geneID1,
  geneID2 = NULL,
  ont = "HDO",
  organism = "hsa",
  measure = "Wang",
  combine = "BMA"
)
geneSim(
  geneID1,
  geneID2 = NULL,
  ont = "HDO",
  organism = "hsa",
  measure = "Wang",
  combine = "BMA"
)

Arguments

geneID1

entrez gene vector

geneID2

entrez gene vector

ont

one of "HDO" and "MPO"

organism

one of "hsa" and "mmu"

measure

one of "Wang", "Resnik", "Rel", "Jiang", and "Lin".

combine

One of "max", "avg", "rcmax", "BMA" methods, for combining semantic similarity scores of multiple DO terms associated with gene/protein.

Details

provide two entrez gene vectors, this function will calculate their similarity.

Value

score matrix

Author(s)

Guangchuang Yu https://yulab-smu.top

Examples

g <- c("835", "5261","241", "994")
geneSim(g)
g <- c("835", "5261","241", "994")
geneSim(g)

DisGeNET Gene Set Enrichment Analysis

Description

perform gsea analysis

Usage

gseDGN(
  geneList,
  exponent = 1,
  nPerm = 1000,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 1000,
  maxPerm = 10000,
  ...
)
gseDGN(
  geneList,
  exponent = 1,
  nPerm = 1000,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 1000,
  maxPerm = 10000,
  ...
)

Arguments

geneList

order ranked geneList

exponent

weight of each step

nPerm

permutation numbers

minGSSize

minimal size of genes annotated by ontology term for testing

maxGSSize

maximal size of each geneSet for analyzing

pvalueCutoff

pvalue cutoff

pAdjustMethod

one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"

verbose

print message or not

method

method of GSEA, one of "multilevel", "permute", "sample"

adaptive

logical, use adaptive permutation or not (default: FALSE)

minPerm

minimum number of permutations for adaptive mode (default: 1000)

maxPerm

maximum number of permutations for adaptive mode (default: 10000)

...

other parameter

Value

gseaResult object

Author(s)

Guangchuang Yu

DO Gene Set Enrichment Analysis

Description

perform gsea analysis

Usage

gseDO(
  geneList,
  ont = "HDO",
  organism = "hsa",
  exponent = 1,
  nPerm = 1000,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 1000,
  maxPerm = 10000,
  ...
)
gseDO(
  geneList,
  ont = "HDO",
  organism = "hsa",
  exponent = 1,
  nPerm = 1000,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 1000,
  maxPerm = 10000,
  ...
)

Arguments

geneList

order ranked geneList

ont

one of "HDO", "HPO" or "MPO"

organism

one of "hsa" and "mmu"

exponent

weight of each step

nPerm

permutation numbers

minGSSize

minimal size of genes annotated by ontology term for testing

maxGSSize

maximal size of each geneSet for analyzing

pvalueCutoff

pvalue cutoff

pAdjustMethod

one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"

verbose

print message or not

method

method of GSEA, one of "multilevel", "permute", "sample"

adaptive

logical, use adaptive permutation or not (default: FALSE)

minPerm

minimum number of permutations for adaptive mode (default: 1000)

maxPerm

maximum number of permutations for adaptive mode (default: 10000)

...

other parameter

Value

gseaResult object

Author(s)

Guangchuang Yu

NCG Gene Set Enrichment Analysis

Description

perform gsea analysis

Usage

gseNCG(
  geneList,
  exponent = 1,
  nPerm = 1000,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 1000,
  maxPerm = 10000,
  ...
)
gseNCG(
  geneList,
  exponent = 1,
  nPerm = 1000,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  method = "multilevel",
  adaptive = FALSE,
  minPerm = 1000,
  maxPerm = 10000,
  ...
)

Arguments

geneList

order ranked geneList

exponent

weight of each step

nPerm

permutation numbers

minGSSize

minimal size of genes annotated by ontology term for testing

maxGSSize

maximal size of each geneSet for analyzing

pvalueCutoff

pvalue cutoff

pAdjustMethod

one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"

verbose

print message or not

method

method of GSEA, one of "multilevel", "permute", "sample"

adaptive

logical, use adaptive permutation or not (default: FALSE)

minPerm

minimum number of permutations for adaptive mode (default: 1000)

maxPerm

maximum number of permutations for adaptive mode (default: 10000)

...

other parameter

Value

gseaResult object

Author(s)

Guangchuang Yu

mclusterSim

Description

Pairwise semantic similarity for a list of gene clusters

Usage

mclusterSim(
  clusters,
  ont = "HDO",
  organism = "hsa",
  measure = "Wang",
  combine = "BMA"
)
mclusterSim(
  clusters,
  ont = "HDO",
  organism = "hsa",
  measure = "Wang",
  combine = "BMA"
)

Arguments

clusters

A list of gene clusters

ont

one of "HDO", "HPO" and "MPO"

organism

organism

measure

one of "Wang", "Resnik", "Rel", "Jiang", and "Lin".

combine

One of "max", "avg", "rcmax", "BMA" methods, for combining semantic similarity scores of multiple DO terms associated with gene/protein.

Value

similarity matrix

Author(s)

Guangchuang Yu

Examples

## Not run: 
cluster1 <- c("835", "5261","241")
cluster2 <- c("578","582")
cluster3 <- c("307", "308", "317")
clusters <- list(a=cluster1, b=cluster2, c=cluster3)
mclusterSim(clusters, measure="Wang")

## End(Not run)
## Not run: 
cluster1 <- c("835", "5261","241")
cluster2 <- c("578","582")
cluster3 <- c("307", "308", "317")
clusters <- list(a=cluster1, b=cluster2, c=cluster3)
mclusterSim(clusters, measure="Wang")

## End(Not run)

simplot

Description

plotting similarity matrix

Usage

simplot(
  sim,
  xlab = "",
  ylab = "",
  color.low = "white",
  color.high = "red",
  labs = TRUE,
  digits = 2,
  labs.size = 3,
  font.size = 14
)
simplot(
  sim,
  xlab = "",
  ylab = "",
  color.low = "white",
  color.high = "red",
  labs = TRUE,
  digits = 2,
  labs.size = 3,
  font.size = 14
)

Arguments

sim

similarity matrix

xlab

xlab

ylab

ylab

color.low

color of low value

color.high

color of high value

labs

logical, add text label or not

digits

round digit numbers

labs.size

lable size

font.size

font size

Value

ggplot object

Author(s)

Yu Guangchuang

theme_dose

Description

ggplot theme of DOSE

Usage

theme_dose(font.size = 14)
theme_dose(font.size = 14)

Arguments

font.size

font size

Value

ggplot theme

Examples

library(ggplot2)
qplot(1:10) + theme_dose()
library(ggplot2)
qplot(1:10) + theme_dose()

Package 'DOSE'

Help Index

clusterSim

Description

Usage

Arguments

Details

Value

Author(s)

Examples

compute information content

Description

Usage

Arguments

Author(s)

Datasets

Description

Shared parameters for DOSE functions

Description

Arguments

doseSim

Description

Usage

Arguments

Details

Value

Author(s)

Enrichment analysis based on the DisGeNET (http://www.disgenet.org/)

Description

Usage

Arguments

Value

Author(s)

References

enrichDGN

Description

Usage

Arguments

Details

Value

Author(s)

References

DO Enrichment Analysis

Description

Usage

Arguments

Value

Author(s)

Examples

enrichNCG

Description

Usage

Arguments

Details

Value

Author(s)

convert Gene ID to DO Terms

Description

Usage

Arguments

Value

Author(s)

geneSim

Description

Usage

Arguments

Details

Value

Author(s)

Examples

DisGeNET Gene Set Enrichment Analysis

Description

Usage

Arguments

Value

Author(s)

DO Gene Set Enrichment Analysis

Description

Usage

Arguments