# This is a demo for customised analysis using InterPro2GO mapping
# 
# InterPro2GO mapping is derived from manual annotations of InterPro from GO (http://www.ncbi.nlm.nih.gov/pubmed/22301074.
# This demo assumes that the user has collected three bits (files) of information:
## 1) InterPro domains (see InterPro.txt);
## 2) GO Molecular Function (see igraph_GOMF_nodes.txt for GOMF term information and igraph_GOMF_edges.txt for parent-child relations between GOMF terms);
## 3) Annotations of InterPro domains by GOMF terms (see GO.txt for all GO term information and Domain2GOMF.txt for annotations)
#
# From this demo, the user can learn how to customise analysis based on their own data (domains, ontology and annotations). The key to this customised analysis is to prepare input files exactly the same as those mentioned above. 
###############################################################################
library(dcGOR)

#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Build (domain, ontology and annotations) objects from user-input files
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------

## 1) InterPro domains
domain <- dcBuildInfoDataFrame(input.file="http://dcgor.r-forge.r-project.org/data/InterPro/InterPro.txt", output.file="domain.RData")

An object of S4 class 'InfoDataFrame' has been built and saved into '/Users/hfang/Sites/SUPERFAMILY/dcGO/dcGOR/domain.RData'.
domain

An object of S4 class 'InfoDataFrame'
  rowNames: IPR000003 IPR000006 IPR000008 ... IPR029784 IPR029785
    (11638 total)
  colNames: id level description

## 2) ontology (GO Molecular Function, GOMF)
g <- 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")

An object of S4 class 'Onto' has been built and saved into '/Users/hfang/Sites/SUPERFAMILY/dcGO/dcGOR/ontology.RData'.
g

An object of S4 class 'Onto'
@adjMatrix: a direct matrix of 9595 terms (parents/from) X 9595 terms (children/to) 
@nodeInfo (InfoDataFrame)
  nodeNames: GO:0003674 GO:0000988 GO:0001071 ... GO:0004008 GO:0086037
    (9595 total)
  nodeAttr: term_id term_name term_namespace term_distance

## 3) annotations (between InterPro domains and GOMF terms)
Anno <- 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")

An object of S4 class 'Anno' has been built and saved into '/Users/hfang/Sites/SUPERFAMILY/dcGO/dcGOR/annotations.RData'.
Anno

An object of S4 class 'Anno'
@annoData: 8899 domains, 2219 terms 
@termData (InfoDataFrame)
  termNames: GO:0001071 GO:0003824 GO:0004872 ... GO:0015467 GO:0072345
    (2219 total)
  tvarLabels: ID Name Namespace Distance
@domainData (InfoDataFrame)
  domainNames: IPR000003 IPR000006 IPR000008 ... IPR029784 IPR029785
    (8899 total)
  dvarLabels: id level description

## In your working directory, you should see three RData-formatted files: "domain.RData", "ontology.RData", "annotations.RData"

#---------------------------------------------------------------------------
#---------------------------------------------------------------------------
# Enrichment analysis
#---------------------------------------------------------------------------
#---------------------------------------------------------------------------

## prepare data and background for enrichment analysis
### randomly select 100 domains as a list of domains of interest
data <- sample(rowNames(domain), 100)
length(data)

[1] 100

### randomly select 1000 domains as background
background <- sample(rowNames(domain), 1000)
length(background)

[1] 1000


## 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')

Start at 2015-07-23 12:18:56

First, load customised ontology 'ontology.RData', the domain 'domain.RData', and their associations 'annotations.RData' (2015-07-23 12:18:56)...
Second, perform enrichment analysis using HypergeoTest (2015-07-23 12:19:14) ...
	There are 140 terms being used, each restricted within [10,1000] annotations
Last, adjust the p-values using the BH method (2015-07-23 12:19:14) ...

End at 2015-07-23 12:19:14
Runtime in total is: 18 secs

eoutput

An object of S4 class 'Eoutput', containing following slots:
  @domain: 'domain.RData'
  @ontology: 'ontology.RData'
  @term_info: a data.frame of 65 terms X 5 information
  @anno: a list of 65 terms, each storing annotated domains
  @data: a vector containing a group of 73 input domains (annotatable)
  @background: a vector containing a group of 830 background domains (annotatable)
  @overlap: a list of 65 terms, each containing domains overlapped with input domains
  @zscore: a vector of 65 terms, containing z-scores
  @pvalue: a vector of 65 terms, containing p-values
  @adjp: a vector of 65 terms, containing adjusted p-values
In summary, a total of 65 terms ('ontology.RData') are analysed for a group of 73 input domains ('domain.RData')


## view the top 10 significance terms 
view(eoutput, top_num=10, sortBy="pvalue", details=FALSE)

              term_id nAnno nGroup nOverlap zscore pvalue adjp
GO:0016866 GO:0016866    11     73        3   2.18  0.011  0.3
GO:0050112 GO:0050112    20     73        4   1.79  0.025  0.3
GO:0016787 GO:0016787   112     73       15   1.85  0.026  0.3
GO:0016746 GO:0016746    14     73        3   1.68  0.028  0.3
GO:0004022 GO:0004022    21     73        4   1.68  0.031  0.3
GO:0015002 GO:0015002    38     73        6   1.56  0.042  0.3
GO:0004518 GO:0004518    16     73        3   1.42  0.044  0.3
GO:0016817 GO:0016817    31     73        5   1.47  0.047  0.3
GO:0016818 GO:0016818    31     73        5   1.47  0.047  0.3
GO:0016462 GO:0016462    31     73        5   1.47  0.047  0.3
                                                                                    term_name
GO:0016866                                                intramolecular transferase activity
GO:0050112                                                  inositol 2-dehydrogenase activity
GO:0016787                                                                 hydrolase activity
GO:0016746                                     transferase activity, transferring acyl groups
GO:0004022                                               alcohol dehydrogenase (NAD) activity
GO:0015002                                              heme-copper terminal oxidase activity
GO:0004518                                                                  nuclease activity
GO:0016817                                      hydrolase activity, acting on acid anhydrides
GO:0016818 hydrolase activity, acting on acid anhydrides, in phosphorus-containing anhydrides
GO:0016462                                                           pyrophosphatase activity

### 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)

Ontology 'ontology.RData' containing 12 nodes/terms (including 5 in query; also highlighted in frame) has been shown in your screen, with colorbar indicating -1*log10(adjusted p-values)


#---------------------------------------------------------------------
#---------------------------------------------------------------------
# Pair-wise semantic similarity between domains
#---------------------------------------------------------------------
#---------------------------------------------------------------------

## prepare for ontology appended with annotation information
dag <- dcDAGannotate(g, annotations=Anno, path.mode="shortest_paths", verbose=FALSE)
dag

An object of S4 class 'Onto'
@adjMatrix: a direct matrix of 2578 terms (parents/from) X 2578 terms (children/to) 
@nodeInfo (InfoDataFrame)
  nodeNames: GO:0003674 GO:0000988 GO:0001071 ... GO:0015272 GO:0015467
    (2578 total)
  nodeAttr: term_id term_name term_namespace term_distance annotations
    IC

## calculate pair-wise semantic similarity between 8 randomly chosen domains
alldomains <- unique(unlist(nInfo(dag)$annotations))
domains <- sample(alldomains,8)
dnetwork <- dcDAGdomainSim(g=dag, domains=domains, method.domain="BM.average", method.term="Resnik", parallel=FALSE, verbose=TRUE)

Start at 2015-07-23 12:19:46

First, extract all annotatable domains (2015-07-23 12:19:46)...
	there are 8 input domains amongst 8899 annotatable domains
Second, pre-compute semantic similarity between 11 terms (forced to be the most specific for each domain) using Resnik method (2015-07-23 12:19:47)...
Last, calculate pair-wise semantic similarity between 8 domains using BM.average method (2015-07-23 12:19:47)...
	1 out of 8 (2015-07-23 12:19:48)
	2 out of 8 (2015-07-23 12:19:48)
	3 out of 8 (2015-07-23 12:19:48)
	4 out of 8 (2015-07-23 12:19:48)
	5 out of 8 (2015-07-23 12:19:48)
	6 out of 8 (2015-07-23 12:19:48)
	7 out of 8 (2015-07-23 12:19:48)

Finish at 2015-07-23 12:19:48
Runtime in total is: 2 secs

dnetwork

An object of S4 class 'Dnetwork'
@adjMatrix: a weighted symmetric matrix of 8 domains X 8 domains 
@nodeInfo (InfoDataFrame)
  nodeNames: IPR024652 IPR009042 IPR023527 ... IPR016067 IPR004485 (8
    total)
  nodeAttr: id

## convert it to an object of class 'igraph'
ig <- dcConverter(dnetwork, from='Dnetwork', to='igraph')

Your input object 'dnetwork' of class 'Dnetwork' has been converted into an object of class 'igraph'.
ig

IGRAPH UNW- 8 18 -- 
+ attr: name (v/c), id (v/c), weight (e/n)
+ edges (vertex names):
 [1] IPR024652--IPR009042 IPR024652--IPR023527 IPR009042--IPR023527
 [4] IPR024652--IPR008388 IPR009042--IPR008388 IPR023527--IPR008388
 [7] IPR009042--IPR002548 IPR008388--IPR002548 IPR009042--IPR016652
[10] IPR024652--IPR016067 IPR009042--IPR016067 IPR023527--IPR016067
[13] IPR008388--IPR016067 IPR024652--IPR004485 IPR009042--IPR004485
[16] IPR023527--IPR004485 IPR008388--IPR004485 IPR016067--IPR004485

## visualise the domain network
### extract edge weight (with 2-digit precision)
x <- signif(E(ig)$weight, digits=2)
### rescale into an interval [1,4] as edge width
edge.width <- 1 + (x-min(x))/(max(x)-min(x))*3
### do visualisation
dnet::visNet(g=ig, vertex.shape="sphere", edge.width=edge.width, edge.label=x, edge.label.cex=0.7)

      

Source demo

Functions used in this demo

• dcBuildInfoDataFrame
• dcBuildOnto
• dcBuildAnno
• dcEnrichment
• visEnrichment
• dcDAGannotate
• dcDAGdomainSim
• dcConverter