Package 'DOSE'

clusterSim

Description

semantic similarity between two gene clusters

Usage

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)

---

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 = "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

---

doseSim

Description

measuring similarities between two DO term vectors.

Usage

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
)

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 = "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 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 https://yulab-smu.top

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

---

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 = "HDO")

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 = "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)

---

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

Arguments

geneList	order ranked geneList
ont	one of "HDO", "HPO" or "MPO"
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

---

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

---

mclusterSim

Description

Pairwise semantic similarity for a list of gene clusters

Usage

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)

---

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

---

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()

---