dcEnrichment: Function to conduct ontology enrichment analysis given a...

Description Usage Arguments Value Note See Also Examples

View source: R/dcEnrichment.r

Description

dcEnrichment is supposed to conduct enrichment analysis for an input group of domains using a specified ontology. It returns an object of S4 class "Eoutput". Enrichment analysis is based on either Fisher's exact test or Hypergeometric test. The test can respect the hierarchy of the ontology. The user can customise the background domains; otherwise, the function will use all annotatable domains as the test background

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
dcEnrichment(data, background = NULL, domain = c(NA, "SCOP.sf",
"SCOP.fa",
"Pfam", "InterPro", "Rfam"), ontology = c(NA, "GOBP", "GOMF", "GOCC",
"DO",
"HPPA", "HPMI", "HPON", "MP", "EC", "KW", "UP"), sizeRange = c(10,
1000),
min.overlap = 3, which_distance = NULL, test = c("HypergeoTest",
"FisherTest", "BinomialTest"), p.adjust.method = c("BH", "BY",
"bonferroni",
"holm", "hochberg", "hommel"), ontology.algorithm = c("none", "pc",
"elim",
"lea"), elim.pvalue = 0.01, lea.depth = 2, verbose = T,
domain.RData = NULL, ontology.RData = NULL, annotations.RData = NULL,
RData.location = "http://dcgor.r-forge.r-project.org/data")

Arguments

data

an input vector. It contains id for a list of domains, for example, sunids for SCOP domains

background

a background vector. It contains id for a list of background domains, for example, sunids for SCOP domains. If NULL, by default all annotatable domains are used as background

domain

the domain identity. It can be one of 'SCOP.sf' for SCOP superfamilies, 'SCOP.fa' for SCOP families, 'Pfam' for Pfam domains, 'InterPro' for InterPro domains, 'Rfam' for Rfam RNA families

ontology

the ontology identity. It can be "GOBP" for Gene Ontology Biological Process, "GOMF" for Gene Ontology Molecular Function, "GOCC" for Gene Ontology Cellular Component, "DO" for Disease Ontology, "HPPA" for Human Phenotype Phenotypic Abnormality, "HPMI" for Human Phenotype Mode of Inheritance, "HPON" for Human Phenotype ONset and clinical course, "MP" for Mammalian Phenotype, "EC" for Enzyme Commission, "KW" for UniProtKB KeyWords, "UP" for UniProtKB UniPathway. For details on the eligibility for pairs of input domain and ontology, please refer to the online Documentations at http://supfam.org/dcGOR/docs.html

sizeRange

the minimum and maximum size of members of each term in consideration. By default, it sets to a minimum of 10 but no more than 1000

min.overlap

the minimum number of overlaps. Only those terms that overlap with input data at least min.overlap (3 domains by default) will be processed

which_distance

which distance of terms in the ontology is used to restrict terms in consideration. By default, it sets to 'NULL' to consider all distances

test

the statistic test used. It can be "FisherTest" for using fisher's exact test, "HypergeoTest" for using hypergeometric test, or "BinomialTest" for using binomial test. Fisher's exact test is to test the independence between domain group (domains belonging to a group or not) and domain annotation (domains annotated by a term or not), and thus compare sampling to the left part of background (after sampling without replacement). Hypergeometric test is to sample at random (without replacement) from the background containing annotated and non-annotated domains, and thus compare sampling to background. Unlike hypergeometric test, binomial test is to sample at random (with replacement) from the background with the constant probability. In terms of the ease of finding the significance, they are in order: hypergeometric test > binomial test > fisher's exact test. In other words, in terms of the calculated p-value, hypergeometric test < binomial test < fisher's exact test

p.adjust.method

the method used to adjust p-values. It can be one of "BH", "BY", "bonferroni", "holm", "hochberg" and "hommel". The first two methods "BH" (widely used) and "BY" control the false discovery rate (FDR: the expected proportion of false discoveries amongst the rejected hypotheses); the last four methods "bonferroni", "holm", "hochberg" and "hommel" are designed to give strong control of the family-wise error rate (FWER). Notes: FDR is a less stringent condition than FWER

ontology.algorithm

the algorithm used to account for the hierarchy of the ontology. It can be one of "none", "pc", "elim" and "lea". For details, please see 'Note'

elim.pvalue

the parameter only used when "ontology.algorithm" is "elim". It is used to control how to declare a signficantly enriched term (and subsequently all domains in this term are eliminated from all its ancestors)

lea.depth

the parameter only used when "ontology.algorithm" is "lea". It is used to control how many maximum depth is uded to consider the children of a term (and subsequently all domains in these children term are eliminated from the use for the recalculation of the signifance at this term)

verbose

logical to indicate whether the messages will be displayed in the screen. By default, it sets to TRUE for display

domain.RData

a file name for RData-formatted file containing an object of S4 class 'InfoDataFrame' (i.g. domain). By default, it is NULL. It is only needed when the user wants to customise enrichment analysis using their own data. See dcBuildInfoDataFrame for how to creat this object

ontology.RData

a file name for RData-formatted file containing an object of S4 class 'Onto' (i.g. ontology). By default, it is NULL. It is only needed when the user wants to customise enrichment analysis using their own data. See dcBuildOnto for how to creat this object

annotations.RData

a file name for RData-formatted file containing an object of S4 class 'Anno' (i.g. annotations). By default, it is NULL. It is only needed when the user wants to customise enrichment analysis using their own data. See dcBuildAnno for how to creat this object

RData.location

the characters to tell the location of built-in RData files. See dcRDataLoader for details

Value

an object of S4 class Eoutput, with following slots:

Note

The interpretation of the algorithms used to account for the hierarchy of the ontology is:

See Also

dcRDataLoader, dcDAGannotate, Eoutput-class, visEnrichment, dcConverter

Examples

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
## Not run: 
# 1) Enrichment analysis for SCOP domain superfamilies (sf)
## 1a) load SCOP.sf (as 'InfoDataFrame' object)
SCOP.sf <- dcRDataLoader('SCOP.sf')
### randomly select 50 domains as a list of domains of interest
data <- sample(rowNames(SCOP.sf), 50)
## 1b) perform enrichment analysis, producing an object of S4 class 'Eoutput'
eoutput <- dcEnrichment(data, domain="SCOP.sf", ontology="GOMF")
eoutput
## 1c) view the top 10 significance terms
view(eoutput, top_num=10, sortBy="pvalue", details=TRUE)
## 1d) visualise the top 10 significant terms in the ontology hierarchy
### color-coded according to 10-based negative logarithm of adjusted p-values (adjp)
visEnrichment(eoutput)
## 1e) the same as above but using a customised background
### randomly select 500 domains as background
background <- sample(rowNames(SCOP.sf), 500)
### perform enrichment analysis, producing an object of S4 class 'Eoutput'
eoutput <- dcEnrichment(data, background=background, domain="SCOP.sf",
ontology="GOMF")
eoutput
### view the top 10 significance terms
view(eoutput, top_num=10, sortBy="pvalue", details=TRUE)
### visualise the top 10 significant terms in the ontology hierarchy
### color-coded according to 10-based negative logarithm of adjusted p-values (adjp)
visEnrichment(eoutput)

###########################################################
# 2) Enrichment analysis for Pfam domains (Pfam)
## 2a) load Pfam (as 'InfoDataFrame' object)
Pfam <- dcRDataLoader('Pfam')
### randomly select 100 domains as a list of domains of interest
data <- sample(rowNames(Pfam), 100)
## 2b) perform enrichment analysis, producing an object of S4 class 'Eoutput'
eoutput <- dcEnrichment(data, domain="Pfam", ontology="GOMF")
eoutput
## 2c) view the top 10 significance terms
view(eoutput, top_num=10, sortBy="pvalue", details=TRUE)
## 2d) visualise the top 10 significant terms in the ontology hierarchy
### color-coded according to 10-based negative logarithm of adjusted p-values (adjp)
visEnrichment(eoutput)
## 2e) the same as above but using a customised background
### randomly select 1000 domains as background
background <- sample(rowNames(Pfam), 1000)
### perform enrichment analysis, producing an object of S4 class 'Eoutput'
eoutput <- dcEnrichment(data, background=background, domain="Pfam",
ontology="GOMF")
eoutput
### view the top 10 significance terms
view(eoutput, top_num=10, sortBy="pvalue", details=TRUE)
### visualise the top 10 significant terms in the ontology hierarchy
### color-coded according to 10-based negative logarithm of adjusted p-values (adjp)
visEnrichment(eoutput)

###########################################################
# 3) Enrichment analysis for InterPro domains (InterPro)
## 3a) load InterPro (as 'InfoDataFrame' object)
InterPro <- dcRDataLoader('InterPro')
### randomly select 100 domains as a list of domains of interest
data <- sample(rowNames(InterPro), 100)
## 3b) perform enrichment analysis, producing an object of S4 class 'Eoutput'
eoutput <- dcEnrichment(data, domain="InterPro", ontology="GOMF")
eoutput
## 3c) view the top 10 significance terms
view(eoutput, top_num=10, sortBy="pvalue", details=TRUE)
## 3d) visualise the top 10 significant terms in the ontology hierarchy
### color-coded according to 10-based negative logarithm of adjusted p-values (adjp)
visEnrichment(eoutput)
## 3e) the same as above but using a customised background
### randomly select 1000 domains as background
background <- sample(rowNames(InterPro), 1000)
### perform enrichment analysis, producing an object of S4 class 'Eoutput'
eoutput <- dcEnrichment(data, background=background, domain="InterPro",
ontology="GOMF")
eoutput
### view the top 10 significance terms
view(eoutput, top_num=10, sortBy="pvalue", details=TRUE)
### visualise the top 10 significant terms in the ontology hierarchy
### color-coded according to 10-based negative logarithm of adjusted p-values (adjp)
visEnrichment(eoutput)

###########################################################
# 4) Enrichment analysis for Rfam RNA families (Rfam)
## 4a) load Rfam (as 'InfoDataFrame' object)
Rfam <- dcRDataLoader('Rfam')
### randomly select 100 RNAs as a list of RNAs of interest
data <- sample(rowNames(Rfam), 100)
## 4b) perform enrichment analysis, producing an object of S4 class 'Eoutput'
eoutput <- dcEnrichment(data, domain="Rfam", ontology="GOBP")
eoutput
## 4c) view the top 10 significance terms
view(eoutput, top_num=10, sortBy="pvalue", details=FALSE)
## 4d) visualise the top 10 significant terms in the ontology hierarchy
### color-coded according to 10-based negative logarithm of adjusted p-values (adjp)
visEnrichment(eoutput)
## 4e) the same as above but using a customised background
### randomly select 1000 RNAs as background
background <- sample(rowNames(Rfam), 1000)
### perform enrichment analysis, producing an object of S4 class 'Eoutput'
eoutput <- dcEnrichment(data, background=background, domain="Rfam",
ontology="GOBP")
eoutput
### view the top 10 significance terms
view(eoutput, top_num=10, sortBy="pvalue", details=FALSE)
### visualise the top 10 significant terms in the ontology hierarchy
### color-coded according to 10-based negative logarithm of adjusted p-values (adjp)
visEnrichment(eoutput)

###########################################################
# 5) Advanced usage: customised data for domain, ontology and annotations
# 5a) create domain, ontology and annotations
## for domain
domain <-
dcBuildInfoDataFrame(input.file="http://dcgor.r-forge.r-project.org/data/InterPro/InterPro.txt",
output.file="domain.RData")
## for ontology
dcBuildOnto(relations.file="http://dcgor.r-forge.r-project.org/data/onto/igraph_GOMF_edges.txt",
nodes.file="http://dcgor.r-forge.r-project.org/data/onto/igraph_GOMF_nodes.txt",
output.file="ontology.RData")
## for annotations
dcBuildAnno(domain_info.file="http://dcgor.r-forge.r-project.org/data/InterPro/InterPro.txt",
term_info.file="http://dcgor.r-forge.r-project.org/data/InterPro/GO.txt",
association.file="http://dcgor.r-forge.r-project.org/data/InterPro/Domain2GOMF.txt",
output.file="annotations.RData")
## 5b) prepare data and background
### randomly select 100 domains as a list of domains of interest
data <- sample(rowNames(domain), 100)
### randomly select 1000 domains as background
background <- sample(rowNames(domain), 1000)
## 5c) perform enrichment analysis, producing an object of S4 class 'Eoutput'
eoutput <- dcEnrichment(data, background=background,
domain.RData='domain.RData', ontology.RData='ontology.RData',
annotations.RData='annotations.RData')
eoutput
## 5d) view the top 10 significance terms
view(eoutput, top_num=10, sortBy="pvalue", details=TRUE)
### visualise the top 10 significant terms in the ontology hierarchy
### color-coded according to 10-based negative logarithm of adjusted p-values (adjp)
visEnrichment(eoutput)

## End(Not run)

dcGOR documentation built on May 30, 2017, 7:09 a.m.

Related to dcEnrichment in dcGOR...