Package 'DOSE'

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], Erqiang Hu [ctb]
Maintainer: Guangchuang Yu <[email protected]>
License: Artistic-2.0
Version: 3.31.2
Built: 2024-07-14 03:19:46 UTC
Source: https://github.com/bioc/DOSE

Help Index

clusterSim

Description

semantic similarity between two gene clusters

Usage

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

Arguments

cluster1

a vector of gene IDs
cluster2

another vector of gene IDs
ont

one of "DO" and "MPO"
organism

organism
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

cluster1 <- c("835", "5261","241", "994")
cluster2 <- c("307", "308", "317", "321", "506", "540", "378", "388", "396")
clusterSim(cluster1, cluster2)

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

Author(s)

Guangchuang Yu https://yulab-smu.top

See Also

enrichResult

compute information content

Description

compute information content

Usage

computeIC(ont = "DO")

Arguments

ont

one of "DO" and "MPO"

Author(s)

Guangchuang Yu https://yulab-smu.top

Datasets

Description

Information content and DO term to entrez gene IDs mapping

doSim

Description

measuring similarities between two DO term vectors.

Usage

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

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 "DO" and "MPO"

Details

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

Value

score matrix

doSim

Description

measuring similarities between two MPO term vectors.

Usage

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

Arguments

DOID1

DO term vector
DOID2

DO term vector
measure

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

Details

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

Value

score matrix

Author(s)

Guangchuang Yu https://guangchuangyu.github.io

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
)

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 NCG category 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
)

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 NCG category 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 = "DO",
  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 DO and DOLite.
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 NCG category 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 http://guangchuangyu.github.io

See Also

enrichResult-class

Examples

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

enrich.internal

Description

interal method for enrichment analysis

Usage

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

Arguments

gene

a vector of entrez gene id.
pvalueCutoff

Cutoff value of pvalue.
pAdjustMethod

one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"
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 genes annotated by Ontology term for testing.
maxGSSize

maximal size of each geneSet for analyzing
qvalueCutoff

cutoff of qvalue
USER_DATA

ontology information

Details

using the hypergeometric model

Value

A enrichResult instance.

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

enrichHPO(
  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 NCG category 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)

Erqiang Hu

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

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

enrichMPO(
  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 NCG category 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)

Erqiang Hu

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

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
)

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 NCG category 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

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

See Also

enrichDO

EXTID2NAME

Description

mapping gene ID to gene Symbol

Usage

EXTID2NAME(OrgDb, geneID, keytype)

Arguments

OrgDb

OrgDb
geneID

entrez gene ID
keytype

keytype

Value

gene symbol

Author(s)

Guangchuang Yu https://yulab-smu.top

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 = "DO")

Arguments

gene

entrez gene ID
organism

organism
ont

ont

Value

DO Terms

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 <- 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 <- enrichDO(de)
geneInCategory(x)

geneSim

Description

measuring similarities bewteen two gene vectors.

Usage

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

Arguments

geneID1

entrez gene vector
geneID2

entrez gene vector
ont

one of "DO" 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.

Details

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

Value

score matrix

Author(s)

Guangchuang Yu http://ygc.name

GSEA_internal

Description

generic function for gene set enrichment analysis

Usage

GSEA_internal(
  geneList,
  exponent,
  minGSSize,
  maxGSSize,
  eps,
  pvalueCutoff,
  pAdjustMethod,
  verbose,
  seed = FALSE,
  USER_DATA,
  by = "fgsea",
  ...
)

Arguments

geneList

order ranked geneList
exponent

weight of each step
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of each geneSet for analyzing
eps

This parameter sets the boundary for calculating the p value.
pvalueCutoff

p value Cutoff
pAdjustMethod

p value adjustment method
verbose

print message or not
seed

set seed inside the function to make result reproducible. FALSE by default.
USER_DATA

annotation data
by

one of 'fgsea' or 'DOSE'
...

other parameter

Value

gseaResult object

Author(s)

Yu Guangchuang

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

DisGeNET Gene Set Enrichment Analysis

Description

perform gsea analysis

Usage

gseDGN(
  geneList,
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  seed = FALSE,
  by = "fgsea",
  ...
)

Arguments

geneList

order ranked geneList
exponent

weight of each step
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of each geneSet for analyzing
pvalueCutoff

pvalue Cutoff
pAdjustMethod

p value adjustment method
verbose

print message or not
seed

logical
by

one of 'fgsea' or 'DOSE'
...

other parameter

Value

gseaResult object

Author(s)

Yu Guangchuang

DO Gene Set Enrichment Analysis

Description

perform gsea analysis

Usage

gseDO(
  geneList,
  organism = "hsa",
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  seed = FALSE,
  by = "fgsea",
  ...
)

Arguments

geneList

order ranked geneList
organism

one of "hsa" and "mmu"
exponent

weight of each step
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of each geneSet for analyzing
pvalueCutoff

pvalue Cutoff
pAdjustMethod

p value adjustment method
verbose

print message or not
seed

logical
by

one of 'fgsea' or 'DOSE'
...

other parameter

Value

gseaResult object

Author(s)

Yu Guangchuang

MPO Gene Set Enrichment Analysis

Description

perform gsea analysis

Usage

gseHPO(
  geneList,
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  seed = FALSE,
  by = "fgsea",
  ...
)

Arguments

geneList

order ranked geneList
exponent

weight of each step
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of each geneSet for analyzing
pvalueCutoff

pvalue Cutoff
pAdjustMethod

p value adjustment method
verbose

print message or not
seed

logical
by

one of 'fgsea' or 'DOSE'
...

other parameter

Value

gseaResult object

Author(s)

Erqiang Hu

MPO Gene Set Enrichment Analysis

Description

perform gsea analysis

Usage

gseMPO(
  geneList,
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  seed = FALSE,
  by = "fgsea",
  ...
)

Arguments

geneList

order ranked geneList
exponent

weight of each step
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of each geneSet for analyzing
pvalueCutoff

pvalue Cutoff
pAdjustMethod

p value adjustment method
verbose

print message or not
seed

logical
by

one of 'fgsea' or 'DOSE'
...

other parameter

Value

gseaResult object

Author(s)

Erqiang Hu

NCG Gene Set Enrichment Analysis

Description

perform gsea analysis

Usage

gseNCG(
  geneList,
  exponent = 1,
  minGSSize = 10,
  maxGSSize = 500,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  verbose = TRUE,
  seed = FALSE,
  by = "fgsea",
  ...
)

Arguments

geneList

order ranked geneList
exponent

weight of each step
minGSSize

minimal size of each geneSet for analyzing
maxGSSize

maximal size of each geneSet for analyzing
pvalueCutoff

pvalue Cutoff
pAdjustMethod

p value adjustment method
verbose

print message or not
seed

logical
by

one of 'fgsea' or 'DOSE'
...

other parameter

Value

gseaResult object

Author(s)

Yu Guangchuang

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

doSim

Description

measuring similarities between two MPO term vectors.

Usage

hpoSim(DOID1, DOID2, measure = "Wang")

Arguments

DOID1

HPO term vector
DOID2

HPO term vector
measure

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

Details

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

Value

score matrix

mclusterSim

Description

Pairwise semantic similarity for a list of gene clusters

Usage

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

Arguments

clusters

A list of gene clusters
ont

one of "DO" 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)

Yu Guangchuang

Examples

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")

doSim

Description

measuring similarities between two MPO term vectors.

Usage

mpoSim(DOID1, DOID2, measure = "Wang")

Arguments

DOID1

MPO term vector
DOID2

MPO term vector
measure

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

Details

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

Value

score matrix

parse_ratio

Description

parse character ratio to double value, such as 1/5 to 0.2

Usage

parse_ratio(ratio)

Arguments

ratio

character vector of ratio to parse

Value

A numeric vector (double) of parsed ratio

Author(s)

Guangchuang Yu

rebuiding annotation data

Description

rebuilding entrez and DO mapping datasets

Usage

rebuildAnnoData(file)

Arguments

file

do_rif.human.txt

Author(s)

Guangchuang Yu https://yulab-smu.top

setReadable

Description

mapping geneID to gene Symbol

Usage

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

Arguments

x

enrichResult Object
OrgDb

OrgDb
keyType

keyType of gene

Value

enrichResult Object

Author(s)

Yu Guangchuang

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

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
)

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

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://guangchuangyu.github.io

Guangchuang Yu http://guangchuangyu.github.io

theme_dose

Description

ggplot theme of DOSE

Usage

theme_dose(font.size = 14)

Arguments

font.size

font size

Value

ggplot theme

Examples

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