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R/dEnricher.r
In dnet: Integrative Analysis of Omics Data in Terms of Network, Evolution and Ontology
Defines functions
dEnricher
Documented in
dEnricher
#' Function to conduct enrichment analysis given the input data and the ontology in query
#'
#' \code{dEnricher} is supposed to conduct enrichment analysis given the input data and the ontology in query. It returns an object of class "eTerm". Enrichment analysis is based on either Fisher's exact test or Hypergeometric test. The test can respect the hierarchy of the ontology.
#'
#' @param data an input vector. It contains either Entrez Gene ID or Symbol
#' @param identity the type of gene identity (i.e. row names of input data), either "symbol" for gene symbols (by default) or "entrez" for Entrez Gene ID. The option "symbol" is preferred as it is relatively stable from one update to another; also it is possible to search against synonyms (see the next parameter)
#' @param check.symbol.identity logical to indicate whether synonyms will be searched against when gene symbols cannot be matched. By default, it sets to FALSE since it may take a while to do such check using all possible synoyms
#' @param genome the genome identity. It can be one of "Hs" for human, "Mm" for mouse, "Rn" for rat, "Gg" for chicken, "Ce" for c.elegans, "Dm" for fruitfly, "Da" for zebrafish, and "At" for arabidopsis
#' @param ontology the ontology supported currently. It can be "GOBP" for Gene Ontology Biological Process, "GOMF" for Gene Ontology Molecular Function, "GOCC" for Gene Ontology Cellular Component, "PS" for phylostratific age information, "PS2" for the collapsed PS version (inferred ancestors being collapsed into one with the known taxonomy information), "SF" for domain superfamily assignments, "DO" for Disease Ontology, "HPPA" for Human Phenotype Phenotypic Abnormality, "HPMI" for Human Phenotype Mode of Inheritance, "HPCM" for Human Phenotype Clinical Modifier, "HPMA" for Human Phenotype Mortality Aging, "MP" for Mammalian Phenotype, and Drug-Gene Interaction database (DGIdb) and the molecular signatures database (Msigdb) only in human (including "MsigdbH", "MsigdbC1", "MsigdbC2CGP", "MsigdbC2CP", "MsigdbC2KEGG", "MsigdbC2REACTOME", "MsigdbC2BIOCARTA", "MsigdbC3TFT", "MsigdbC3MIR", "MsigdbC4CGN", "MsigdbC4CM", "MsigdbC5BP", "MsigdbC5MF", "MsigdbC5CC", "MsigdbC6", "MsigdbC7"). Note: These four ("GOBP", "GOMF", "GOCC" and "PS") are availble for all genomes/species; for "Hs" and "Mm", these six ("DO", "HPPA", "HPMI", "HPCM", "HPMA" and "MP") are also supported; all "Msigdb" are only supported in "Hs". For details on the eligibility for pairs of input genome and ontology, please refer to the online Documentations at \url{http://supfam.org/dnet/docs.html}
#' @param sizeRange the minimum and maximum size of members of each gene set in consideration. By default, it sets to a minimum of 10 but no more than 1000
#' @param min.overlap the minimum number of overlaps. Only those gene sets that overlap with input data at least min.overlap (3 by default) will be processed
#' @param 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
#' @param 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 gene group (genes belonging to a group or not) and gene annotation (genes 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 genes, 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
#' @param 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
#' @param 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'
#' @param 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 genes in this term are eliminated from all its ancestors)
#' @param 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 genes in these children term are eliminated from the use for the recalculation of the signifance at this term)
#' @param verbose logical to indicate whether the messages will be displayed in the screen. By default, it sets to false for no display
#' @param RData.location the characters to tell the location of built-in RData files. By default, it remotely locates at \url{https://github.com/hfang-bristol/RDataCentre/blob/master/dnet} and \url{http://dnet.r-forge.r-project.org/RData}. Be aware of several versions and the latest one is matched to the current package version. For the user equipped with fast internet connection, this option can be just left as default. But it is always advisable to download these files locally. Especially when the user needs to run this function many times, there is no need to ask the function to remotely download every time (also it will unnecessarily increase the runtime). For examples, these files (as a whole or part of them) can be first downloaded into your current working directory, and then set this option as: \eqn{RData.location="."}. Surely, the location can be anywhere as long as the user provides the correct path pointing to (otherwise, the script will have to remotely download each time). Here is the UNIX command for downloading all RData files (preserving the directory structure): \eqn{wget -r -l2 -A "*.RData" -np -nH --cut-dirs=0 "http://dnet.r-forge.r-project.org/RData"}
#' @return
#' an object of class "eTerm", a list with following components:
#' \itemize{
#' \item{\code{set_info}: a matrix of nSet X 4 containing gene set information, where nSet is the number of gene set in consideration, and the 4 columns are "setID" (i.e. "Term ID"), "name" (i.e. "Term Name"), "namespace" and "distance"}
#' \item{\code{gs}: a list of gene sets, each storing gene members. Always, gene sets are identified by "setID" and gene members identified by "Entrez ID"}
#' \item{\code{data}: a vector containing input data in consideration. It is not always the same as the input data as only those mappable are retained}
#' \item{\code{overlap}: a list of overlapped gene sets, each storing genes overlapped between a gene set and the given input data (i.e. the genes of interest). Always, gene sets are identified by "setID" and gene members identified by "Entrez ID"}
#' \item{\code{zscore}: a vector containing z-scores}
#' \item{\code{pvalue}: a vector containing p-values}
#' \item{\code{adjp}: a vector containing adjusted p-values. It is the p value but after being adjusted for multiple comparisons}
#' \item{\code{call}: the call that produced this result}
#' }
#' @note The interpretation of the algorithms used to account for the hierarchy of the ontology is:
#' \itemize{
#' \item{"none": does not consider the ontology hierarchy at all.}
#' \item{"lea": computers the significance of a term in terms of the significance of its children at the maximum depth (e.g. 2). Precisely, once genes are already annotated to any children terms with a more signficance than itself, then all these genes are eliminated from the use for the recalculation of the signifance at that term. The final p-values takes the maximum of the original p-value and the recalculated p-value.}
#' \item{"elim": computers the significance of a term in terms of the significance of its all children. Precisely, once genes are already annotated to a signficantly enriched term under the cutoff of e.g. pvalue<1e-2, all these genes are eliminated from the ancestors of that term).}
#' \item{"pc": requires the significance of a term not only using the whole genes as background but also using genes annotated to all its direct parents/ancestors as background. The final p-value takes the maximum of both p-values in these two calculations.}
#' \item{"Notes": the order of the number of significant terms is: "none" > "lea" > "elim" > "pc".}
#' }
#' @export
#' @seealso \code{\link{dEnricherView}}
#' @include dEnricher.r
#' @examples
#' \dontrun{
#' # load data
#' #library(Biobase)
#' #TCGA_mutations <- dRDataLoader(RData='TCGA_mutations')
#' #symbols <- as.character(fData(TCGA_mutations)$Symbol)
#'
#' # Enrichment analysis using Disease Ontology (DO)
#' #data <- symbols[1:100] # select the first 100 human genes
#' #eTerm <- dEnricher(data, identity="symbol", genome="Hs", ontology="DO")
#'
#' # visualise the top significant terms in the ontology hierarchy
#' #ig.DO <- dRDataLoader(RData='ig.DO')
#' #g <- ig.DO
#' #nodes_query <- names(sort(eTerm$adjp)[1:5])
#' #nodes.highlight <- rep("red", length(nodes_query))
#' #names(nodes.highlight) <- nodes_query
#' #subg <- dDAGinduce(g, nodes_query)
#' # color-code terms according to the adjust p-values (taking the form of 10-based negative logarithm)
#' #data <- -1*log10(eTerm$adjp[V(subg)$name])
#' #visDAG(g=subg, data=data, node.info="both", zlim=c(0,2), node.attrs=list(color=nodes.highlight))
#' # color-code terms according to the z-scores
#' #data <- eTerm$zscore[V(subg)$name]
#' #visDAG(g=subg, data=data, node.info="both", node.attrs=list(color=nodes.highlight))
#' }
dEnricher <- function(data, identity=c("symbol","entrez"), check.symbol.identity=FALSE, genome=c("Hs", "Mm", "Rn", "Gg", "Ce", "Dm", "Da", "At"), ontology=c("GOBP","GOMF","GOCC","PS","PS2","SF","DO","HPPA","HPMI","HPCM","HPMA","MP", "MsigdbH", "MsigdbC1", "MsigdbC2CGP", "MsigdbC2CP", "MsigdbC2KEGG", "MsigdbC2REACTOME", "MsigdbC2BIOCARTA", "MsigdbC3TFT", "MsigdbC3MIR", "MsigdbC4CGN", "MsigdbC4CM", "MsigdbC5BP", "MsigdbC5MF", "MsigdbC5CC", "MsigdbC6", "MsigdbC7", "DGIdb"), 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=1e-2, lea.depth=2, verbose=T, RData.location="https://github.com/hfang-bristol/RDataCentre/blob/master/dnet/1.0.7")
{
startT <- Sys.time()
message(paste(c("Start at ",as.character(startT)), collapse=""), appendLF=T)
message("", appendLF=T)
####################################################################################
## match.arg matches arg against a table of candidate values as specified by choices, where NULL means to take the first one
identity <- match.arg(identity)
genome <- match.arg(genome)
ontology <- match.arg(ontology)
test <- match.arg(test)
p.adjust.method <- match.arg(p.adjust.method)
ontology.algorithm <- match.arg(ontology.algorithm)
if (is.vector(data)){
data <- unique(data)
}else{
stop("The input data must be a vector.\n")
}
if(!is.na(genome) & !is.na(ontology)){
if(verbose){
now <- Sys.time()
message(sprintf("First, load the ontology %s and its gene associations in the genome %s (%s) ...", ontology, genome, as.character(now)), appendLF=T)
}
#########
## load Enterz Gene information
EG <- dRDataLoader(paste('org.', genome, '.eg', sep=''), RData.location=RData.location)
#########
## load GS information
## flag the simplified version of PS
flag_PS2 <- FALSE
if(ontology=="PS2"){
flag_PS2 <- TRUE
ontology <- "PS"
}
GS <- dRDataLoader(paste('org.', genome, '.eg', ontology, sep=''), RData.location=RData.location)
################
if(flag_PS2){
tmp <- as.character(unique(GS$set_info$name))
inds <- sapply(tmp,function(x) which(GS$set_info$name==x))
## new set_info
set_info <- data.frame()
for(i in 1:length(inds)){
set_info<- rbind(set_info,as.matrix(GS$set_info[max(inds[[i]]),]))
}
## new gs
gs <- list()
for(i in 1:length(inds)){
gs[[i]] <- unlist(GS$gs[inds[[i]]], use.names=F)
}
names(gs) <- rownames(set_info)
## new GS
GS$set_info <- set_info
GS$gs <- gs
}
################
}else{
stop("There is no input for genome and/or ontology.\n")
}
###############################
# A function converting from symbol to entrezgene
symbol2entrezgene <- function(Symbol, check.symbol.identity, allGeneID, allSymbol, allSynonyms, verbose){
## correct for those symbols being shown as DATE format
if(1){
## for those starting with 'Mar' in a excel-input date format
a <- Symbol
flag <- grep("-Mar$", a, ignore.case=T, perl=T, value=F)
if(length(flag)>=1){
b <- a[flag]
c <- sub("-Mar$", "", b, ignore.case=T, perl=T)
d <- sub("^0", "", c, ignore.case=T, perl=T)
e <- sapply(d, function(x) paste(c("March",x), collapse=""))
a[flag] <- e
Symbol <- a
}
## for those starting with 'Sep' in a excel-input date format
a <- Symbol
flag <- grep("-Sep$", a, ignore.case=T, perl=T, value=F)
if(length(flag)>=1){
b <- a[flag]
c <- sub("-Sep$", "", b, ignore.case=T, perl=T)
d <- sub("^0", "", c, ignore.case=T, perl=T)
e <- sapply(d, function(x) paste(c("Sept",x), collapse=""))
a[flag] <- e
Symbol <- a
}
}
## case-insensitive
match_flag <- match(tolower(Symbol),tolower(allSymbol))
## match vis Synonyms for those unmatchable by official gene symbols
if(check.symbol.identity){
## match Synonyms (if not found via Symbol)
na_flag <- is.na(match_flag)
a <- Symbol[na_flag]
###
tmp_flag <- is.na(match(tolower(allSymbol), tolower(Symbol)))
tmp_Synonyms <- allSynonyms[tmp_flag]
Orig.index <- seq(1,length(allSynonyms))
Orig.index <- Orig.index[tmp_flag]
###
b <- sapply(1:length(a), function(x){
tmp_pattern1 <- paste("^",a[x],"\\|", sep="")
tmp_pattern2 <- paste("\\|",a[x],"\\|", sep="")
tmp_pattern3 <- paste("\\|",a[x],"$", sep="")
tmp_pattern <- paste(tmp_pattern1,"|",tmp_pattern2,"|",tmp_pattern3, sep="")
tmp_result <- grep(tmp_pattern, tmp_Synonyms, ignore.case=T, perl=T, value=F)
ifelse(length(tmp_result)==1, Orig.index[tmp_result[1]], NA)
})
match_flag[na_flag] <- b
if(verbose){
now <- Sys.time()
message(sprintf("\tAmong %d symbols of input data, there are %d mappable via official gene symbols, %d mappable via gene alias but %d left unmappable", length(Symbol), (length(Symbol)-length(a)), sum(!is.na(b)), sum(is.na(b))), appendLF=T)
}
}else{
if(verbose){
now <- Sys.time()
message(sprintf("\tAmong %d symbols of input data, there are %d mappable via official gene symbols but %d left unmappable", length(Symbol), (sum(!is.na(match_flag))), (sum(is.na(match_flag)))), appendLF=T)
}
}
## convert into GeneID
GeneID <- allGeneID[match_flag]
return(GeneID)
}
###############################
allGeneID <- EG$gene_info$GeneID
allSymbol <- as.vector(EG$gene_info$Symbol)
allSynonyms <- as.vector(EG$gene_info$Synonyms)
if(verbose){
now <- Sys.time()
message(sprintf("Then, do mapping based on %s (%s) ...", identity, as.character(now)), appendLF=T)
}
if(identity == "symbol"){
Symbol <- data
GeneID <- symbol2entrezgene(Symbol=Symbol, check.symbol.identity=check.symbol.identity, allGeneID=allGeneID, allSymbol=allSymbol, allSynonyms=allSynonyms, verbose=verbose)
}else{
GeneID <- data
match_flag <- match(GeneID,allGeneID)
GeneID <- allGeneID[match_flag]
}
genes.group <- GeneID[!is.na(GeneID)]
## filter based on "which_distance"
if(!is.null(which_distance) & sum(is.na(GS$set_info$distance))==0){
set_filtered <- sapply(which_distance, function(x) {
GS$set_info$setID[(GS$set_info$distance==as.integer(x))]
})
set_filtered <- unlist(set_filtered)
}else{
set_filtered <- names(GS$gs)
}
ind.distance <- match(set_filtered,names(GS$gs))
## derive the "gs" of interest
gs.length <- sapply(GS$gs, length)
ind.length <- which(gs.length >= sizeRange[1] & gs.length <= sizeRange[2])
ind <- intersect(ind.distance, ind.length)
gs <- GS$gs[ind]
set_info <- GS$set_info[ind,]
nSet <- length(gs)
if(nSet==0){
stop("There is no gene set being used.\n")
}
##############################################################################################
## Fisher's exact test: testing the independence between gene group (genes belonging to a group or not) and gene annotation (genes annotated by a term or not); thus compare sampling to the left part of background (after sampling without replacement)
doFisherTest <- function(genes.group, genes.term, genes.universe){
genes.hit <- intersect(genes.group, genes.term)
# num of success in sampling
X <- length(genes.hit)
# num of sampling
K <- length(genes.group)
# num of success in background
M <- length(genes.term)
# num in background
N <- length(genes.universe)
## Prepare a two-dimensional contingency table: #success in sampling, #success in background, #failure in sampling, and #failure in left part
cTab <- matrix(c(X, K-X, M-X, N-M-K+X), nrow=2, dimnames=list(c("anno", "notAnno"), c("group", "notGroup")))
p.value <- ifelse(all(cTab==0), 1, stats::fisher.test(cTab, alternative="greater")$p.value)
return(p.value)
}
## Hypergeometric test: sampling at random from the background containing annotated and non-annotated genes (without replacement); thus compare sampling to background
doHypergeoTest <- function(genes.group, genes.term, genes.universe){
genes.hit <- intersect(genes.group, genes.term)
# num of success in sampling
X <- length(genes.hit)
# num of sampling
K <- length(genes.group)
# num of success in background
M <- length(genes.term)
# num in background
N <- length(genes.universe)
x <- X
m <- M
n <- N-M # num of failure in background
k <- K
p.value <- ifelse(m==0 || k==0, 1, stats::phyper(x,m,n,k, lower.tail=F, log.p=F))
return(p.value)
}
## Binomial test: sampling at random from the background with the constant probability of having annotated genes (with replacement)
doBinomialTest <- function(genes.group, genes.term, genes.universe){
genes.hit <- intersect(genes.group, genes.term)
# num of success in sampling
X <- length(genes.hit)
# num of sampling
K <- length(genes.group)
# num of success in background
M <- length(genes.term)
# num in background
N <- length(genes.universe)
p.value <- ifelse(K==0 || M==0 || N==0, 1, stats::pbinom(X,K,M/N, lower.tail=F, log.p=F))
return(p.value)
}
## Z-score from hypergeometric distribution
zscoreHyper <- function(genes.group, genes.term, genes.universe){
genes.hit <- intersect(genes.group, genes.term)
# num of success in sampling
X <- length(genes.hit)
# num of sampling
K <- length(genes.group)
# num of success in background
M <- length(genes.term)
# num in background
N <- length(genes.universe)
## calculate z-score
if(1){
## Z-score based on theoretical calculation
x.exp <- K*M/N
var.exp <- K*M/N*(N-M)/N*(N-K)/(N-1)
if(var.exp==0){
z <- NA
}else{
suppressWarnings(z <- (X-x.exp)/sqrt(var.exp))
}
}else{
## Z-score equivalents for deviates from hypergeometric distribution
x <- X
m <- M
n <- N-M # num of failure in background
k <- K
suppressWarnings(d <- stats::dhyper(x,m,n,k,log=TRUE)-log(2))
suppressWarnings(pupper <- stats::phyper(x,m,n,k,lower.tail=FALSE,log.p=TRUE))
suppressWarnings(plower <- stats::phyper(x-1,m,n,k,lower.tail=TRUE,log.p=TRUE))
d[is.na(d)] <- -Inf
pupper[is.na(pupper)] <- -Inf
plower[is.na(plower)] <- -Inf
# Add half point probability to upper tail probability preserving log-accuracy
a <- pupper
b <- d-pupper
a[b>0] <- d[b>0]
b <- -abs(b)
pmidupper <- a+log1p(exp(b))
pmidupper[is.infinite(a)] <- a[is.infinite(a)]
# Similarly for lower tail probability preserving log-accuracy
a <- plower
b <- d-plower
a[b>0] <- d[b>0]
b <- -abs(b)
pmidlower <- a+log1p(exp(b))
pmidlower[is.infinite(a)] <- a[is.infinite(a)]
up <- pmidupper<pmidlower
if(any(up)) z <- stats::qnorm(pmidupper,lower.tail=FALSE,log.p=TRUE)
if(any(!up)) z <- stats::qnorm(pmidlower,lower.tail=TRUE,log.p=TRUE)
}
return(z)
}
##############################################################################################
## force use classic ontology.algorithm when the ontology is derived from "Msigdb" or "PS"
if(length(grep("Msigdb",ontology))>0 || ontology=="PS" || ontology=="SF" || ontology=="DGIdb"){
ontology.algorithm <- "none"
}
terms <- names(gs)
genes.universe <- as.numeric(unique(unlist(gs[terms])))
genes.group <- intersect(genes.universe, genes.group)
if(length(genes.group)==0){
#stop("There is no gene being used.\n")
warnings("There is no gene being used.\n")
return(F)
}
if(ontology.algorithm=="none"){
if(verbose){
now <- Sys.time()
message(sprintf("Third, perform enrichment analysis using %s (%s) ...", test, as.character(now)), appendLF=T)
if(is.null(which_distance)){
message(sprintf("\tThere are %d terms being used, each restricted within [%s] annotations", length(terms), paste(sizeRange,collapse=",")), appendLF=T)
}else{
message(sprintf("\tThere are %d terms being used, each restricted within [%s] annotations and [%s] distance", length(terms), paste(sizeRange,collapse=","), paste(which_distance,collapse=",")), appendLF=T)
}
}
pvals <- sapply(terms, function(term){
genes.term <- as.numeric(unique(unlist(gs[term])))
p.value <- switch(test,
FisherTest = doFisherTest(genes.group, genes.term, genes.universe),
HypergeoTest = doHypergeoTest(genes.group, genes.term, genes.universe),
BinomialTest = doBinomialTest(genes.group, genes.term, genes.universe)
)
})
zscores <- sapply(terms, function(term){
genes.term <- as.numeric(unique(unlist(gs[term])))
zscoreHyper(genes.group, genes.term, genes.universe)
})
}else if(ontology.algorithm=="pc" || ontology.algorithm=="elim" || ontology.algorithm=="lea"){
if(verbose){
now <- Sys.time()
message(sprintf("Third, perform enrichment analysis using %s based on %s algorithm to respect ontology structure (%s) ...", test, ontology.algorithm, as.character(now)), appendLF=T)
}
#########
## load ontology information
g <- dRDataLoader(paste('ig.', ontology, sep=''), RData.location=RData.location)
###############################
subg <- dDAGinduce(g, terms, path.mode="all_paths")
if(verbose){
message(sprintf("\tThere are %d terms being used", length(V(subg))), appendLF=T)
}
level2node <- dDAGlevel(subg, level.mode="longest_path", return.mode="level2node")
## build a hash environment from the named list "level2node"
## level2node.Hash: key (level), value (a list of nodes/terms)
level2node.Hash <- list2env(level2node)
## ls(level2node.Hash)
nLevels <- length(level2node)
## create a new (empty) hash environment
## node2pval.Hash: key (node), value (pvalue)
node2pval.Hash <- new.env(hash=T, parent=emptyenv())
## node2zscore.Hash: key (node), value (zscore)
node2zscore.Hash <- new.env(hash=T, parent=emptyenv())
if(ontology.algorithm=="pc"){
for(i in nLevels:2) {
currNodes <- get(as.character(i), envir=level2node.Hash, mode="character")
for(currNode in currNodes){
genes.term <- unique(unlist(GS$gs[currNode]))
## do test based on the whole genes as background
pvalue_whole <- switch(test,
FisherTest = doFisherTest(genes.group, genes.term, genes.universe),
HypergeoTest = doHypergeoTest(genes.group, genes.term, genes.universe),
BinomialTest = doBinomialTest(genes.group, genes.term, genes.universe)
)
zscore_whole <- zscoreHyper(genes.group, genes.term, genes.universe)
## get the incoming neighbors/parents (including self) that are reachable
neighs.in <- igraph::neighborhood(subg, order=1, nodes=currNode, mode="in")
adjNodes <- setdiff(V(subg)[unlist(neighs.in)]$name, currNode)
## genes annotated in parents are as background
genes.parent <- unique(unlist(GS$gs[adjNodes]))
## make sure genes in group (genes in term) are also in parents
genes.group.parent <- intersect(genes.group, genes.parent)
genes.term.parent <- intersect(genes.term, genes.parent)
## do test based on the genes in parents as background
pvalue_relative <- switch(test,
FisherTest = doFisherTest(genes.group.parent, genes.term.parent, genes.parent),
HypergeoTest = doHypergeoTest(genes.group.parent, genes.term.parent, genes.parent),
BinomialTest = doBinomialTest(genes.group.parent, genes.term.parent, genes.parent)
)
zscore_relative <- zscoreHyper(genes.group.parent, genes.term.parent, genes.parent)
## take the maximum value of pvalue_whole and pvalue_relative
pvalue <- max(pvalue_whole, pvalue_relative)
## store the result (the p-value)
assign(currNode, pvalue, envir=node2pval.Hash)
## take the miminum value of zscore_whole and zscore_relative
zscore <- ifelse(pvalue_whole>pvalue_relative, zscore_whole, zscore_relative)
## store the result (the z-score)
assign(currNode, zscore, envir=node2zscore.Hash)
}
if(verbose){
message(sprintf("\tAt level %d, there are %d nodes/terms", i, length(currNodes), appendLF=T))
}
}
## the root always has p-value=1 and z-score=0
root <- dDAGroot(subg)
assign(root, 1, envir=node2pval.Hash)
assign(root, 0, envir=node2zscore.Hash)
}else if(ontology.algorithm=="elim"){
## sigNode2pval.Hash: key (node called significant), value (pvalue)
sigNode2pval.Hash <- new.env(hash=T, parent=emptyenv())
## ancNode2gene.Hash: key (node at ancestor), value (genes to be eliminated)
ancNode2gene.Hash <- new.env(hash=T, parent=emptyenv())
if(is.null(elim.pvalue) || is.na(elim.pvalue) || elim.pvalue>1 || elim.pvalue<0){
elim.pvalue <- 1e-2
}
pval.cutoff <- elim.pvalue
#pval.cutoff <- 1e-2 / length(V(subg))
for(i in nLevels:1) {
currNodes <- get(as.character(i), envir=level2node.Hash, mode="character")
currAnno <- GS$gs[currNodes]
## update "ancNode2gene.Hash" for each node/term
for(currNode in currNodes){
genes.term <- unique(unlist(GS$gs[currNode]))
## remove the genes (if any already marked) from annotations by the current node/term
if(exists(currNode, envir=ancNode2gene.Hash, mode="numeric")){
genes.elim <- get(currNode, envir=ancNode2gene.Hash, mode="numeric")
genes.term <- setdiff(genes.term, genes.elim)
#message(sprintf("\t\t%d %d", length(genes.elim), length(genes.term)), appendLF=T)
}
## do test
pvalue <- switch(test,
FisherTest = doFisherTest(genes.group, genes.term, genes.universe),
HypergeoTest = doHypergeoTest(genes.group, genes.term, genes.universe),
BinomialTest = doBinomialTest(genes.group, genes.term, genes.universe)
)
zscore <- zscoreHyper(genes.group, genes.term, genes.universe)
## store the result (the p-value)
assign(currNode, pvalue, envir=node2pval.Hash)
## store the result (the z-score)
assign(currNode, zscore, envir=node2zscore.Hash)
## condition to update "ancNode2gene.Hash"
if(pvalue < pval.cutoff) {
## mark the significant node
assign(currNode, pvalue, envir=sigNode2pval.Hash)
## retrieve genes annotated by the significant node for the subsequent eliminating
elimGenesID <- currAnno[[currNode]]
## find all the ancestors of the significant node
dag.ancestors <- dDAGinduce(subg, currNode, path.mode="all_paths")
ancestors <- setdiff(V(dag.ancestors)$name, currNode)
## get only those ancestors that are already in "ancNode2gene.Hash"
oldAncestors2GenesID <- sapply(ancestors, function(ancestor){
if(exists(ancestor, envir=ancNode2gene.Hash, mode="numeric")){
get(ancestor, envir=ancNode2gene.Hash, mode='numeric')
}
})
## add the new GenesID to the ancestors
newAncestors2GenesID <- lapply(oldAncestors2GenesID, function(oldGenes){
union(oldGenes, elimGenesID)
})
## update the "ancNode2gene.Hash" table
if(length(newAncestors2GenesID) > 0){
sapply(names(newAncestors2GenesID), function(ancestor){
assign(ancestor, newAncestors2GenesID[[ancestor]], envir=ancNode2gene.Hash)
})
}
}
}
if(verbose){
num.signodes <- length(ls(sigNode2pval.Hash))
num.ancnodes <- length(ls(ancNode2gene.Hash))
num.elimgenes <- length(unique(unlist(as.list(ancNode2gene.Hash))))
message(sprintf("\tAt level %d, there are %d nodes/terms: up to %d significant nodes, %d ancestral nodes changed (%d genes eliminated)", i, length(currNodes), num.signodes, num.ancnodes, num.elimgenes), appendLF=T)
}
}
}else if(ontology.algorithm=="lea"){
## node2pvalo.Hash: key (node called significant), value (original pvalue)
node2pvalo.Hash <- new.env(hash=T, parent=emptyenv())
if(is.null(lea.depth) || is.na(lea.depth) || lea.depth<0){
lea.depth <- 2
}
depth.cutoff <- as.integer(lea.depth)
for(i in nLevels:1) {
currNodes <- get(as.character(i), envir=level2node.Hash, mode="character")
currAnno <- GS$gs[currNodes]
num.recalculate <- 0
## update "node2pval.Hash" for each node/term
for(currNode in currNodes){
genes.term <- unique(unlist(GS$gs[currNode]))
## do test
pvalue.old <- switch(test,
FisherTest = doFisherTest(genes.group, genes.term, genes.universe),
HypergeoTest = doHypergeoTest(genes.group, genes.term, genes.universe),
BinomialTest = doBinomialTest(genes.group, genes.term, genes.universe)
)
zscore.old <- zscoreHyper(genes.group, genes.term, genes.universe)
## store the result (old pvalue)
assign(currNode, pvalue.old, envir=node2pvalo.Hash)
## get the outgoing neighbors/children (including self) that are reachable at most of given depth
neighs.out <- igraph::neighborhood(subg, order=depth.cutoff, nodes=currNode, mode="out")
adjNodes <- setdiff(V(subg)[unlist(neighs.out)]$name, currNode)
if(length(adjNodes)!=0){
## get children with the lower p-value
if(1){
pvalue.children <- sapply(adjNodes, function(child){
if(exists(child, envir=node2pvalo.Hash, mode="numeric")){
get(child, envir=node2pvalo.Hash, mode="numeric")
}
})
}else{
pvalue.children <- sapply(adjNodes, function(child){
if(exists(child, envir=node2pval.Hash, mode="numeric")){
get(child, envir=node2pval.Hash, mode="numeric")
}
})
}
chNodes <- names(pvalue.children[pvalue.children < pvalue.old])
## whether there exist any children with the lower p-value
if(length(chNodes)>0){
num.recalculate <- num.recalculate + 1
## if yes, get genes that are annotated by children with the lower p-value
## they will be removed
genes.elim <- unique(unlist(GS$gs[chNodes]))
genes.term.new <- setdiff(genes.term, genes.elim)
## recalculate the significance
pvalue.new <- switch(test,
FisherTest = doFisherTest(genes.group, genes.term.new, genes.universe),
HypergeoTest = doHypergeoTest(genes.group, genes.term.new, genes.universe),
BinomialTest = doBinomialTest(genes.group, genes.term.new, genes.universe)
)
zscore.new <- zscoreHyper(genes.group, genes.term.new, genes.universe)
## take the maximum value of pvalue_new and the original pvalue
pvalue <- max(pvalue.new, pvalue.old)
## take the minimum value of zscore_new and the original zscore
zscore <- ifelse(pvalue.new>pvalue.old, zscore.new, zscore.old)
}else{
pvalue <- pvalue.old
zscore <- zscore.old
}
}else{
pvalue <- pvalue.old
zscore <- zscore.old
}
## store the result (recalculated pvalue if have to)
assign(currNode, pvalue, envir=node2pval.Hash)
## store the result (recalculated zscore if have to)
assign(currNode, zscore, envir=node2zscore.Hash)
}
if(verbose){
message(sprintf("\tAt level %d, there are %d nodes/terms and %d being recalculated", i, length(currNodes), num.recalculate), appendLF=T)
}
}
}
pvals <- unlist(as.list(node2pval.Hash))
zscores <- unlist(as.list(node2zscore.Hash))
}
if(verbose){
now <- Sys.time()
message(sprintf("Last, adjust the p-values using the %s method (%s) ...", p.adjust.method, as.character(now)), appendLF=T)
}
overlaps <- sapply(names(gs), function(term){
genes.term <- as.numeric(unique(unlist(gs[term])))
intersect(genes.group, genes.term)
})
## for those with "min.overlap" overlaps will be processed and reported
flag_filter <- sapply(overlaps, function(x) ifelse(length(x)>=min.overlap,T,F))
if(sum(flag_filter)==0){
#stop("It seems there are no terms meeting the specified 'sizeRange' and 'min.overlap'.\n")
warnings("It seems there are no terms meeting the specified 'sizeRange' and 'min.overlap'.\n")
return(F)
}
set_info <- set_info[flag_filter,]
gs <- gs[flag_filter]
overlaps <- overlaps[flag_filter]
## common terms
common <- intersect(names(gs), names(zscores))
ind_gs <- match(common,names(gs))
ind_zscores <- match(common, names(zscores))
## restrict to the common terms (and sorted too)
set_info <- set_info[ind_gs[!is.na(ind_gs)],]
gs <- gs[ind_gs[!is.na(ind_gs)]]
overlaps <- overlaps[ind_gs[!is.na(ind_gs)]]
zscores <- zscores[ind_zscores[!is.na(ind_zscores)]]
pvals <- pvals[ind_zscores[!is.na(ind_zscores)]]
## remove those with zscores=NA
flag <- !is.na(zscores)
set_info <- set_info[flag,]
gs <- gs[flag]
overlaps <- overlaps[flag]
zscores <- zscores[flag]
pvals <- pvals[flag]
zscores <- signif(zscores, digits=3)
pvals <- sapply(pvals, function(x) min(x,1))
## Adjust P-values for multiple comparisons
adjpvals <- stats::p.adjust(pvals, method=p.adjust.method)
pvals <- signif(pvals, digits=2)
adjpvals <- sapply(adjpvals, function(x) min(x,1))
adjpvals <- signif(adjpvals, digits=2)
####################################################################################
endT <- Sys.time()
message(paste(c("\nEnd at ",as.character(endT)), collapse=""), appendLF=T)
runTime <- as.numeric(difftime(strptime(endT, "%Y-%m-%d %H:%M:%S"), strptime(startT, "%Y-%m-%d %H:%M:%S"), units="secs"))
message(paste(c("Runtime in total is: ",runTime," secs\n"), collapse=""), appendLF=T)
########################################
if(1){
## overlaps
overlaps <- lapply(overlaps, function(x){
ind <- match(x, allGeneID)
names(x) <- allSymbol[ind]
x
})
}
########################################
eTerm <- list(set_info = set_info,
gs = gs,
data = data,
overlap = overlaps,
zscore = zscores,
pvalue = pvals,
adjp = adjpvals,
call = match.call()
)
class(eTerm) <- "eTerm"
invisible(eTerm)
}
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Defines functions dEnricher
Documented in dEnricher
#' Function to conduct enrichment analysis given the input data and the ontology in query
#'
#' \code{dEnricher} is supposed to conduct enrichment analysis given the input data and the ontology in query. It returns an object of class "eTerm". Enrichment analysis is based on either Fisher's exact test or Hypergeometric test. The test can respect the hierarchy of the ontology.
#'
#' @param data an input vector. It contains either Entrez Gene ID or Symbol
#' @param identity the type of gene identity (i.e. row names of input data), either "symbol" for gene symbols (by default) or "entrez" for Entrez Gene ID. The option "symbol" is preferred as it is relatively stable from one update to another; also it is possible to search against synonyms (see the next parameter)
#' @param check.symbol.identity logical to indicate whether synonyms will be searched against when gene symbols cannot be matched. By default, it sets to FALSE since it may take a while to do such check using all possible synoyms
#' @param genome the genome identity. It can be one of "Hs" for human, "Mm" for mouse, "Rn" for rat, "Gg" for chicken, "Ce" for c.elegans, "Dm" for fruitfly, "Da" for zebrafish, and "At" for arabidopsis
#' @param ontology the ontology supported currently. It can be "GOBP" for Gene Ontology Biological Process, "GOMF" for Gene Ontology Molecular Function, "GOCC" for Gene Ontology Cellular Component, "PS" for phylostratific age information, "PS2" for the collapsed PS version (inferred ancestors being collapsed into one with the known taxonomy information), "SF" for domain superfamily assignments, "DO" for Disease Ontology, "HPPA" for Human Phenotype Phenotypic Abnormality, "HPMI" for Human Phenotype Mode of Inheritance, "HPCM" for Human Phenotype Clinical Modifier, "HPMA" for Human Phenotype Mortality Aging, "MP" for Mammalian Phenotype, and Drug-Gene Interaction database (DGIdb) and the molecular signatures database (Msigdb) only in human (including "MsigdbH", "MsigdbC1", "MsigdbC2CGP", "MsigdbC2CP", "MsigdbC2KEGG", "MsigdbC2REACTOME", "MsigdbC2BIOCARTA", "MsigdbC3TFT", "MsigdbC3MIR", "MsigdbC4CGN", "MsigdbC4CM", "MsigdbC5BP", "MsigdbC5MF", "MsigdbC5CC", "MsigdbC6", "MsigdbC7"). Note: These four ("GOBP", "GOMF", "GOCC" and "PS") are availble for all genomes/species; for "Hs" and "Mm", these six ("DO", "HPPA", "HPMI", "HPCM", "HPMA" and "MP") are also supported; all "Msigdb" are only supported in "Hs". For details on the eligibility for pairs of input genome and ontology, please refer to the online Documentations at \url{http://supfam.org/dnet/docs.html}
#' @param sizeRange the minimum and maximum size of members of each gene set in consideration. By default, it sets to a minimum of 10 but no more than 1000
#' @param min.overlap the minimum number of overlaps. Only those gene sets that overlap with input data at least min.overlap (3 by default) will be processed
#' @param 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
#' @param 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 gene group (genes belonging to a group or not) and gene annotation (genes 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 genes, 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
#' @param 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
#' @param 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'
#' @param 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 genes in this term are eliminated from all its ancestors)
#' @param 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 genes in these children term are eliminated from the use for the recalculation of the signifance at this term)
#' @param verbose logical to indicate whether the messages will be displayed in the screen. By default, it sets to false for no display
#' @param RData.location the characters to tell the location of built-in RData files. By default, it remotely locates at \url{https://github.com/hfang-bristol/RDataCentre/blob/master/dnet} and \url{http://dnet.r-forge.r-project.org/RData}. Be aware of several versions and the latest one is matched to the current package version. For the user equipped with fast internet connection, this option can be just left as default. But it is always advisable to download these files locally. Especially when the user needs to run this function many times, there is no need to ask the function to remotely download every time (also it will unnecessarily increase the runtime). For examples, these files (as a whole or part of them) can be first downloaded into your current working directory, and then set this option as: \eqn{RData.location="."}. Surely, the location can be anywhere as long as the user provides the correct path pointing to (otherwise, the script will have to remotely download each time). Here is the UNIX command for downloading all RData files (preserving the directory structure): \eqn{wget -r -l2 -A "*.RData" -np -nH --cut-dirs=0 "http://dnet.r-forge.r-project.org/RData"}
#' @return
#' an object of class "eTerm", a list with following components:
#' \itemize{
#' \item{\code{set_info}: a matrix of nSet X 4 containing gene set information, where nSet is the number of gene set in consideration, and the 4 columns are "setID" (i.e. "Term ID"), "name" (i.e. "Term Name"), "namespace" and "distance"}
#' \item{\code{gs}: a list of gene sets, each storing gene members. Always, gene sets are identified by "setID" and gene members identified by "Entrez ID"}
#' \item{\code{data}: a vector containing input data in consideration. It is not always the same as the input data as only those mappable are retained}
#' \item{\code{overlap}: a list of overlapped gene sets, each storing genes overlapped between a gene set and the given input data (i.e. the genes of interest). Always, gene sets are identified by "setID" and gene members identified by "Entrez ID"}
#' \item{\code{zscore}: a vector containing z-scores}
#' \item{\code{pvalue}: a vector containing p-values}
#' \item{\code{adjp}: a vector containing adjusted p-values. It is the p value but after being adjusted for multiple comparisons}
#' \item{\code{call}: the call that produced this result}
#' }
#' @note The interpretation of the algorithms used to account for the hierarchy of the ontology is:
#' \itemize{
#' \item{"none": does not consider the ontology hierarchy at all.}
#' \item{"lea": computers the significance of a term in terms of the significance of its children at the maximum depth (e.g. 2). Precisely, once genes are already annotated to any children terms with a more signficance than itself, then all these genes are eliminated from the use for the recalculation of the signifance at that term. The final p-values takes the maximum of the original p-value and the recalculated p-value.}
#' \item{"elim": computers the significance of a term in terms of the significance of its all children. Precisely, once genes are already annotated to a signficantly enriched term under the cutoff of e.g. pvalue<1e-2, all these genes are eliminated from the ancestors of that term).}
#' \item{"pc": requires the significance of a term not only using the whole genes as background but also using genes annotated to all its direct parents/ancestors as background. The final p-value takes the maximum of both p-values in these two calculations.}
#' \item{"Notes": the order of the number of significant terms is: "none" > "lea" > "elim" > "pc".}
#' }
#' @export
#' @seealso \code{\link{dEnricherView}}
#' @include dEnricher.r
#' @examples
#' \dontrun{
#' # load data
#' #library(Biobase)
#' #TCGA_mutations <- dRDataLoader(RData='TCGA_mutations')
#' #symbols <- as.character(fData(TCGA_mutations)$Symbol)
#'
#' # Enrichment analysis using Disease Ontology (DO)
#' #data <- symbols[1:100] # select the first 100 human genes
#' #eTerm <- dEnricher(data, identity="symbol", genome="Hs", ontology="DO")
#'
#' # visualise the top significant terms in the ontology hierarchy
#' #ig.DO <- dRDataLoader(RData='ig.DO')
#' #g <- ig.DO
#' #nodes_query <- names(sort(eTerm$adjp)[1:5])
#' #nodes.highlight <- rep("red", length(nodes_query))
#' #names(nodes.highlight) <- nodes_query
#' #subg <- dDAGinduce(g, nodes_query)
#' # color-code terms according to the adjust p-values (taking the form of 10-based negative logarithm)
#' #data <- -1*log10(eTerm$adjp[V(subg)$name])
#' #visDAG(g=subg, data=data, node.info="both", zlim=c(0,2), node.attrs=list(color=nodes.highlight))
#' # color-code terms according to the z-scores
#' #data <- eTerm$zscore[V(subg)$name]
#' #visDAG(g=subg, data=data, node.info="both", node.attrs=list(color=nodes.highlight))
#' }
dEnricher <- function(data, identity=c("symbol","entrez"), check.symbol.identity=FALSE, genome=c("Hs", "Mm", "Rn", "Gg", "Ce", "Dm", "Da", "At"), ontology=c("GOBP","GOMF","GOCC","PS","PS2","SF","DO","HPPA","HPMI","HPCM","HPMA","MP", "MsigdbH", "MsigdbC1", "MsigdbC2CGP", "MsigdbC2CP", "MsigdbC2KEGG", "MsigdbC2REACTOME", "MsigdbC2BIOCARTA", "MsigdbC3TFT", "MsigdbC3MIR", "MsigdbC4CGN", "MsigdbC4CM", "MsigdbC5BP", "MsigdbC5MF", "MsigdbC5CC", "MsigdbC6", "MsigdbC7", "DGIdb"), 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=1e-2, lea.depth=2, verbose=T, RData.location="https://github.com/hfang-bristol/RDataCentre/blob/master/dnet/1.0.7")
{
startT <- Sys.time()
message(paste(c("Start at ",as.character(startT)), collapse=""), appendLF=T)
message("", appendLF=T)
####################################################################################
## match.arg matches arg against a table of candidate values as specified by choices, where NULL means to take the first one
identity <- match.arg(identity)
genome <- match.arg(genome)
ontology <- match.arg(ontology)
test <- match.arg(test)
p.adjust.method <- match.arg(p.adjust.method)
ontology.algorithm <- match.arg(ontology.algorithm)
if (is.vector(data)){
data <- unique(data)
}else{
stop("The input data must be a vector.\n")
}
if(!is.na(genome) & !is.na(ontology)){
if(verbose){
now <- Sys.time()
message(sprintf("First, load the ontology %s and its gene associations in the genome %s (%s) ...", ontology, genome, as.character(now)), appendLF=T)
}
#########
## load Enterz Gene information
EG <- dRDataLoader(paste('org.', genome, '.eg', sep=''), RData.location=RData.location)
#########
## load GS information
## flag the simplified version of PS
flag_PS2 <- FALSE
if(ontology=="PS2"){
flag_PS2 <- TRUE
ontology <- "PS"
}
GS <- dRDataLoader(paste('org.', genome, '.eg', ontology, sep=''), RData.location=RData.location)
################
if(flag_PS2){
tmp <- as.character(unique(GS$set_info$name))
inds <- sapply(tmp,function(x) which(GS$set_info$name==x))
## new set_info
set_info <- data.frame()
for(i in 1:length(inds)){
set_info<- rbind(set_info,as.matrix(GS$set_info[max(inds[[i]]),]))
}
## new gs
gs <- list()
for(i in 1:length(inds)){
gs[[i]] <- unlist(GS$gs[inds[[i]]], use.names=F)
}
names(gs) <- rownames(set_info)
## new GS
GS$set_info <- set_info
GS$gs <- gs
}
################
}else{
stop("There is no input for genome and/or ontology.\n")
}
###############################
# A function converting from symbol to entrezgene
symbol2entrezgene <- function(Symbol, check.symbol.identity, allGeneID, allSymbol, allSynonyms, verbose){
## correct for those symbols being shown as DATE format
if(1){
## for those starting with 'Mar' in a excel-input date format
a <- Symbol
flag <- grep("-Mar$", a, ignore.case=T, perl=T, value=F)
if(length(flag)>=1){
b <- a[flag]
c <- sub("-Mar$", "", b, ignore.case=T, perl=T)
d <- sub("^0", "", c, ignore.case=T, perl=T)
e <- sapply(d, function(x) paste(c("March",x), collapse=""))
a[flag] <- e
Symbol <- a
}
## for those starting with 'Sep' in a excel-input date format
a <- Symbol
flag <- grep("-Sep$", a, ignore.case=T, perl=T, value=F)
if(length(flag)>=1){
b <- a[flag]
c <- sub("-Sep$", "", b, ignore.case=T, perl=T)
d <- sub("^0", "", c, ignore.case=T, perl=T)
e <- sapply(d, function(x) paste(c("Sept",x), collapse=""))
a[flag] <- e
Symbol <- a
}
}
## case-insensitive
match_flag <- match(tolower(Symbol),tolower(allSymbol))
## match vis Synonyms for those unmatchable by official gene symbols
if(check.symbol.identity){
## match Synonyms (if not found via Symbol)
na_flag <- is.na(match_flag)
a <- Symbol[na_flag]
###
tmp_flag <- is.na(match(tolower(allSymbol), tolower(Symbol)))
tmp_Synonyms <- allSynonyms[tmp_flag]
Orig.index <- seq(1,length(allSynonyms))
Orig.index <- Orig.index[tmp_flag]
###
b <- sapply(1:length(a), function(x){
tmp_pattern1 <- paste("^",a[x],"\\|", sep="")
tmp_pattern2 <- paste("\\|",a[x],"\\|", sep="")
tmp_pattern3 <- paste("\\|",a[x],"$", sep="")
tmp_pattern <- paste(tmp_pattern1,"|",tmp_pattern2,"|",tmp_pattern3, sep="")
tmp_result <- grep(tmp_pattern, tmp_Synonyms, ignore.case=T, perl=T, value=F)
ifelse(length(tmp_result)==1, Orig.index[tmp_result[1]], NA)
})
match_flag[na_flag] <- b
if(verbose){
now <- Sys.time()
message(sprintf("\tAmong %d symbols of input data, there are %d mappable via official gene symbols, %d mappable via gene alias but %d left unmappable", length(Symbol), (length(Symbol)-length(a)), sum(!is.na(b)), sum(is.na(b))), appendLF=T)
}
}else{
if(verbose){
now <- Sys.time()
message(sprintf("\tAmong %d symbols of input data, there are %d mappable via official gene symbols but %d left unmappable", length(Symbol), (sum(!is.na(match_flag))), (sum(is.na(match_flag)))), appendLF=T)
}
}
## convert into GeneID
GeneID <- allGeneID[match_flag]
return(GeneID)
}
###############################
allGeneID <- EG$gene_info$GeneID
allSymbol <- as.vector(EG$gene_info$Symbol)
allSynonyms <- as.vector(EG$gene_info$Synonyms)
if(verbose){
now <- Sys.time()
message(sprintf("Then, do mapping based on %s (%s) ...", identity, as.character(now)), appendLF=T)
}
if(identity == "symbol"){
Symbol <- data
GeneID <- symbol2entrezgene(Symbol=Symbol, check.symbol.identity=check.symbol.identity, allGeneID=allGeneID, allSymbol=allSymbol, allSynonyms=allSynonyms, verbose=verbose)
}else{
GeneID <- data
match_flag <- match(GeneID,allGeneID)
GeneID <- allGeneID[match_flag]
}
genes.group <- GeneID[!is.na(GeneID)]
## filter based on "which_distance"
if(!is.null(which_distance) & sum(is.na(GS$set_info$distance))==0){
set_filtered <- sapply(which_distance, function(x) {
GS$set_info$setID[(GS$set_info$distance==as.integer(x))]
})
set_filtered <- unlist(set_filtered)
}else{
set_filtered <- names(GS$gs)
}
ind.distance <- match(set_filtered,names(GS$gs))
## derive the "gs" of interest
gs.length <- sapply(GS$gs, length)
ind.length <- which(gs.length >= sizeRange[1] & gs.length <= sizeRange[2])
ind <- intersect(ind.distance, ind.length)
gs <- GS$gs[ind]
set_info <- GS$set_info[ind,]
nSet <- length(gs)
if(nSet==0){
stop("There is no gene set being used.\n")
}
##############################################################################################
## Fisher's exact test: testing the independence between gene group (genes belonging to a group or not) and gene annotation (genes annotated by a term or not); thus compare sampling to the left part of background (after sampling without replacement)
doFisherTest <- function(genes.group, genes.term, genes.universe){
genes.hit <- intersect(genes.group, genes.term)
# num of success in sampling
X <- length(genes.hit)
# num of sampling
K <- length(genes.group)
# num of success in background
M <- length(genes.term)
# num in background
N <- length(genes.universe)
## Prepare a two-dimensional contingency table: #success in sampling, #success in background, #failure in sampling, and #failure in left part
cTab <- matrix(c(X, K-X, M-X, N-M-K+X), nrow=2, dimnames=list(c("anno", "notAnno"), c("group", "notGroup")))
p.value <- ifelse(all(cTab==0), 1, stats::fisher.test(cTab, alternative="greater")$p.value)
return(p.value)
}
## Hypergeometric test: sampling at random from the background containing annotated and non-annotated genes (without replacement); thus compare sampling to background
doHypergeoTest <- function(genes.group, genes.term, genes.universe){
genes.hit <- intersect(genes.group, genes.term)
# num of success in sampling
X <- length(genes.hit)
# num of sampling
K <- length(genes.group)
# num of success in background
M <- length(genes.term)
# num in background
N <- length(genes.universe)
x <- X
m <- M
n <- N-M # num of failure in background
k <- K
p.value <- ifelse(m==0 || k==0, 1, stats::phyper(x,m,n,k, lower.tail=F, log.p=F))
return(p.value)
}
## Binomial test: sampling at random from the background with the constant probability of having annotated genes (with replacement)
doBinomialTest <- function(genes.group, genes.term, genes.universe){
genes.hit <- intersect(genes.group, genes.term)
# num of success in sampling
X <- length(genes.hit)
# num of sampling
K <- length(genes.group)
# num of success in background
M <- length(genes.term)
# num in background
N <- length(genes.universe)
p.value <- ifelse(K==0 || M==0 || N==0, 1, stats::pbinom(X,K,M/N, lower.tail=F, log.p=F))
return(p.value)
}
## Z-score from hypergeometric distribution
zscoreHyper <- function(genes.group, genes.term, genes.universe){
genes.hit <- intersect(genes.group, genes.term)
# num of success in sampling
X <- length(genes.hit)
# num of sampling
K <- length(genes.group)
# num of success in background
M <- length(genes.term)
# num in background
N <- length(genes.universe)
## calculate z-score
if(1){
## Z-score based on theoretical calculation
x.exp <- K*M/N
var.exp <- K*M/N*(N-M)/N*(N-K)/(N-1)
if(var.exp==0){
z <- NA
}else{
suppressWarnings(z <- (X-x.exp)/sqrt(var.exp))
}
}else{
## Z-score equivalents for deviates from hypergeometric distribution
x <- X
m <- M
n <- N-M # num of failure in background
k <- K
suppressWarnings(d <- stats::dhyper(x,m,n,k,log=TRUE)-log(2))
suppressWarnings(pupper <- stats::phyper(x,m,n,k,lower.tail=FALSE,log.p=TRUE))
suppressWarnings(plower <- stats::phyper(x-1,m,n,k,lower.tail=TRUE,log.p=TRUE))
d[is.na(d)] <- -Inf
pupper[is.na(pupper)] <- -Inf
plower[is.na(plower)] <- -Inf
# Add half point probability to upper tail probability preserving log-accuracy
a <- pupper
b <- d-pupper
a[b>0] <- d[b>0]
b <- -abs(b)
pmidupper <- a+log1p(exp(b))
pmidupper[is.infinite(a)] <- a[is.infinite(a)]
# Similarly for lower tail probability preserving log-accuracy
a <- plower
b <- d-plower
a[b>0] <- d[b>0]
b <- -abs(b)
pmidlower <- a+log1p(exp(b))
pmidlower[is.infinite(a)] <- a[is.infinite(a)]
up <- pmidupper<pmidlower
if(any(up)) z <- stats::qnorm(pmidupper,lower.tail=FALSE,log.p=TRUE)
if(any(!up)) z <- stats::qnorm(pmidlower,lower.tail=TRUE,log.p=TRUE)
}
return(z)
}
##############################################################################################
## force use classic ontology.algorithm when the ontology is derived from "Msigdb" or "PS"
if(length(grep("Msigdb",ontology))>0 || ontology=="PS" || ontology=="SF" || ontology=="DGIdb"){
ontology.algorithm <- "none"
}
terms <- names(gs)
genes.universe <- as.numeric(unique(unlist(gs[terms])))
genes.group <- intersect(genes.universe, genes.group)
if(length(genes.group)==0){
#stop("There is no gene being used.\n")
warnings("There is no gene being used.\n")
return(F)
}
if(ontology.algorithm=="none"){
if(verbose){
now <- Sys.time()
message(sprintf("Third, perform enrichment analysis using %s (%s) ...", test, as.character(now)), appendLF=T)
if(is.null(which_distance)){
message(sprintf("\tThere are %d terms being used, each restricted within [%s] annotations", length(terms), paste(sizeRange,collapse=",")), appendLF=T)
}else{
message(sprintf("\tThere are %d terms being used, each restricted within [%s] annotations and [%s] distance", length(terms), paste(sizeRange,collapse=","), paste(which_distance,collapse=",")), appendLF=T)
}
}
pvals <- sapply(terms, function(term){
genes.term <- as.numeric(unique(unlist(gs[term])))
p.value <- switch(test,
FisherTest = doFisherTest(genes.group, genes.term, genes.universe),
HypergeoTest = doHypergeoTest(genes.group, genes.term, genes.universe),
BinomialTest = doBinomialTest(genes.group, genes.term, genes.universe)
)
})
zscores <- sapply(terms, function(term){
genes.term <- as.numeric(unique(unlist(gs[term])))
zscoreHyper(genes.group, genes.term, genes.universe)
})
}else if(ontology.algorithm=="pc" || ontology.algorithm=="elim" || ontology.algorithm=="lea"){
if(verbose){
now <- Sys.time()
message(sprintf("Third, perform enrichment analysis using %s based on %s algorithm to respect ontology structure (%s) ...", test, ontology.algorithm, as.character(now)), appendLF=T)
}
#########
## load ontology information
g <- dRDataLoader(paste('ig.', ontology, sep=''), RData.location=RData.location)
###############################
subg <- dDAGinduce(g, terms, path.mode="all_paths")
if(verbose){
message(sprintf("\tThere are %d terms being used", length(V(subg))), appendLF=T)
}
level2node <- dDAGlevel(subg, level.mode="longest_path", return.mode="level2node")
## build a hash environment from the named list "level2node"
## level2node.Hash: key (level), value (a list of nodes/terms)
level2node.Hash <- list2env(level2node)
## ls(level2node.Hash)
nLevels <- length(level2node)
## create a new (empty) hash environment
## node2pval.Hash: key (node), value (pvalue)
node2pval.Hash <- new.env(hash=T, parent=emptyenv())
## node2zscore.Hash: key (node), value (zscore)
node2zscore.Hash <- new.env(hash=T, parent=emptyenv())
if(ontology.algorithm=="pc"){
for(i in nLevels:2) {
currNodes <- get(as.character(i), envir=level2node.Hash, mode="character")
for(currNode in currNodes){
genes.term <- unique(unlist(GS$gs[currNode]))
## do test based on the whole genes as background
pvalue_whole <- switch(test,
FisherTest = doFisherTest(genes.group, genes.term, genes.universe),
HypergeoTest = doHypergeoTest(genes.group, genes.term, genes.universe),
BinomialTest = doBinomialTest(genes.group, genes.term, genes.universe)
)
zscore_whole <- zscoreHyper(genes.group, genes.term, genes.universe)
## get the incoming neighbors/parents (including self) that are reachable
neighs.in <- igraph::neighborhood(subg, order=1, nodes=currNode, mode="in")
adjNodes <- setdiff(V(subg)[unlist(neighs.in)]$name, currNode)
## genes annotated in parents are as background
genes.parent <- unique(unlist(GS$gs[adjNodes]))
## make sure genes in group (genes in term) are also in parents
genes.group.parent <- intersect(genes.group, genes.parent)
genes.term.parent <- intersect(genes.term, genes.parent)
## do test based on the genes in parents as background
pvalue_relative <- switch(test,
FisherTest = doFisherTest(genes.group.parent, genes.term.parent, genes.parent),
HypergeoTest = doHypergeoTest(genes.group.parent, genes.term.parent, genes.parent),
BinomialTest = doBinomialTest(genes.group.parent, genes.term.parent, genes.parent)
)
zscore_relative <- zscoreHyper(genes.group.parent, genes.term.parent, genes.parent)
## take the maximum value of pvalue_whole and pvalue_relative
pvalue <- max(pvalue_whole, pvalue_relative)
## store the result (the p-value)
assign(currNode, pvalue, envir=node2pval.Hash)
## take the miminum value of zscore_whole and zscore_relative
zscore <- ifelse(pvalue_whole>pvalue_relative, zscore_whole, zscore_relative)
## store the result (the z-score)
assign(currNode, zscore, envir=node2zscore.Hash)
}
if(verbose){
message(sprintf("\tAt level %d, there are %d nodes/terms", i, length(currNodes), appendLF=T))
}
}
## the root always has p-value=1 and z-score=0
root <- dDAGroot(subg)
assign(root, 1, envir=node2pval.Hash)
assign(root, 0, envir=node2zscore.Hash)
}else if(ontology.algorithm=="elim"){
## sigNode2pval.Hash: key (node called significant), value (pvalue)
sigNode2pval.Hash <- new.env(hash=T, parent=emptyenv())
## ancNode2gene.Hash: key (node at ancestor), value (genes to be eliminated)
ancNode2gene.Hash <- new.env(hash=T, parent=emptyenv())
if(is.null(elim.pvalue) || is.na(elim.pvalue) || elim.pvalue>1 || elim.pvalue<0){
elim.pvalue <- 1e-2
}
pval.cutoff <- elim.pvalue
#pval.cutoff <- 1e-2 / length(V(subg))
for(i in nLevels:1) {
currNodes <- get(as.character(i), envir=level2node.Hash, mode="character")
currAnno <- GS$gs[currNodes]
## update "ancNode2gene.Hash" for each node/term
for(currNode in currNodes){
genes.term <- unique(unlist(GS$gs[currNode]))
## remove the genes (if any already marked) from annotations by the current node/term
if(exists(currNode, envir=ancNode2gene.Hash, mode="numeric")){
genes.elim <- get(currNode, envir=ancNode2gene.Hash, mode="numeric")
genes.term <- setdiff(genes.term, genes.elim)
#message(sprintf("\t\t%d %d", length(genes.elim), length(genes.term)), appendLF=T)
}
## do test
pvalue <- switch(test,
FisherTest = doFisherTest(genes.group, genes.term, genes.universe),
HypergeoTest = doHypergeoTest(genes.group, genes.term, genes.universe),
BinomialTest = doBinomialTest(genes.group, genes.term, genes.universe)
)
zscore <- zscoreHyper(genes.group, genes.term, genes.universe)
## store the result (the p-value)
assign(currNode, pvalue, envir=node2pval.Hash)
## store the result (the z-score)
assign(currNode, zscore, envir=node2zscore.Hash)
## condition to update "ancNode2gene.Hash"
if(pvalue < pval.cutoff) {
## mark the significant node
assign(currNode, pvalue, envir=sigNode2pval.Hash)
## retrieve genes annotated by the significant node for the subsequent eliminating
elimGenesID <- currAnno[[currNode]]
## find all the ancestors of the significant node
dag.ancestors <- dDAGinduce(subg, currNode, path.mode="all_paths")
ancestors <- setdiff(V(dag.ancestors)$name, currNode)
## get only those ancestors that are already in "ancNode2gene.Hash"
oldAncestors2GenesID <- sapply(ancestors, function(ancestor){
if(exists(ancestor, envir=ancNode2gene.Hash, mode="numeric")){
get(ancestor, envir=ancNode2gene.Hash, mode='numeric')
}
})
## add the new GenesID to the ancestors
newAncestors2GenesID <- lapply(oldAncestors2GenesID, function(oldGenes){
union(oldGenes, elimGenesID)
})
## update the "ancNode2gene.Hash" table
if(length(newAncestors2GenesID) > 0){
sapply(names(newAncestors2GenesID), function(ancestor){
assign(ancestor, newAncestors2GenesID[[ancestor]], envir=ancNode2gene.Hash)
})
}
}
}
if(verbose){
num.signodes <- length(ls(sigNode2pval.Hash))
num.ancnodes <- length(ls(ancNode2gene.Hash))
num.elimgenes <- length(unique(unlist(as.list(ancNode2gene.Hash))))
message(sprintf("\tAt level %d, there are %d nodes/terms: up to %d significant nodes, %d ancestral nodes changed (%d genes eliminated)", i, length(currNodes), num.signodes, num.ancnodes, num.elimgenes), appendLF=T)
}
}
}else if(ontology.algorithm=="lea"){
## node2pvalo.Hash: key (node called significant), value (original pvalue)
node2pvalo.Hash <- new.env(hash=T, parent=emptyenv())
if(is.null(lea.depth) || is.na(lea.depth) || lea.depth<0){
lea.depth <- 2
}
depth.cutoff <- as.integer(lea.depth)
for(i in nLevels:1) {
currNodes <- get(as.character(i), envir=level2node.Hash, mode="character")
currAnno <- GS$gs[currNodes]
num.recalculate <- 0
## update "node2pval.Hash" for each node/term
for(currNode in currNodes){
genes.term <- unique(unlist(GS$gs[currNode]))
## do test
pvalue.old <- switch(test,
FisherTest = doFisherTest(genes.group, genes.term, genes.universe),
HypergeoTest = doHypergeoTest(genes.group, genes.term, genes.universe),
BinomialTest = doBinomialTest(genes.group, genes.term, genes.universe)
)
zscore.old <- zscoreHyper(genes.group, genes.term, genes.universe)
## store the result (old pvalue)
assign(currNode, pvalue.old, envir=node2pvalo.Hash)
## get the outgoing neighbors/children (including self) that are reachable at most of given depth
neighs.out <- igraph::neighborhood(subg, order=depth.cutoff, nodes=currNode, mode="out")
adjNodes <- setdiff(V(subg)[unlist(neighs.out)]$name, currNode)
if(length(adjNodes)!=0){
## get children with the lower p-value
if(1){
pvalue.children <- sapply(adjNodes, function(child){
if(exists(child, envir=node2pvalo.Hash, mode="numeric")){
get(child, envir=node2pvalo.Hash, mode="numeric")
}
})
}else{
pvalue.children <- sapply(adjNodes, function(child){
if(exists(child, envir=node2pval.Hash, mode="numeric")){
get(child, envir=node2pval.Hash, mode="numeric")
}
})
}
chNodes <- names(pvalue.children[pvalue.children < pvalue.old])
## whether there exist any children with the lower p-value
if(length(chNodes)>0){
num.recalculate <- num.recalculate + 1
## if yes, get genes that are annotated by children with the lower p-value
## they will be removed
genes.elim <- unique(unlist(GS$gs[chNodes]))
genes.term.new <- setdiff(genes.term, genes.elim)
## recalculate the significance
pvalue.new <- switch(test,
FisherTest = doFisherTest(genes.group, genes.term.new, genes.universe),
HypergeoTest = doHypergeoTest(genes.group, genes.term.new, genes.universe),
BinomialTest = doBinomialTest(genes.group, genes.term.new, genes.universe)
)
zscore.new <- zscoreHyper(genes.group, genes.term.new, genes.universe)
## take the maximum value of pvalue_new and the original pvalue
pvalue <- max(pvalue.new, pvalue.old)
## take the minimum value of zscore_new and the original zscore
zscore <- ifelse(pvalue.new>pvalue.old, zscore.new, zscore.old)
}else{
pvalue <- pvalue.old
zscore <- zscore.old
}
}else{
pvalue <- pvalue.old
zscore <- zscore.old
}
## store the result (recalculated pvalue if have to)
assign(currNode, pvalue, envir=node2pval.Hash)
## store the result (recalculated zscore if have to)
assign(currNode, zscore, envir=node2zscore.Hash)
}
if(verbose){
message(sprintf("\tAt level %d, there are %d nodes/terms and %d being recalculated", i, length(currNodes), num.recalculate), appendLF=T)
}
}
}
pvals <- unlist(as.list(node2pval.Hash))
zscores <- unlist(as.list(node2zscore.Hash))
}
if(verbose){
now <- Sys.time()
message(sprintf("Last, adjust the p-values using the %s method (%s) ...", p.adjust.method, as.character(now)), appendLF=T)
}
overlaps <- sapply(names(gs), function(term){
genes.term <- as.numeric(unique(unlist(gs[term])))
intersect(genes.group, genes.term)
})
## for those with "min.overlap" overlaps will be processed and reported
flag_filter <- sapply(overlaps, function(x) ifelse(length(x)>=min.overlap,T,F))
if(sum(flag_filter)==0){
#stop("It seems there are no terms meeting the specified 'sizeRange' and 'min.overlap'.\n")
warnings("It seems there are no terms meeting the specified 'sizeRange' and 'min.overlap'.\n")
return(F)
}
set_info <- set_info[flag_filter,]
gs <- gs[flag_filter]
overlaps <- overlaps[flag_filter]
## common terms
common <- intersect(names(gs), names(zscores))
ind_gs <- match(common,names(gs))
ind_zscores <- match(common, names(zscores))
## restrict to the common terms (and sorted too)
set_info <- set_info[ind_gs[!is.na(ind_gs)],]
gs <- gs[ind_gs[!is.na(ind_gs)]]
overlaps <- overlaps[ind_gs[!is.na(ind_gs)]]
zscores <- zscores[ind_zscores[!is.na(ind_zscores)]]
pvals <- pvals[ind_zscores[!is.na(ind_zscores)]]
## remove those with zscores=NA
flag <- !is.na(zscores)
set_info <- set_info[flag,]
gs <- gs[flag]
overlaps <- overlaps[flag]
zscores <- zscores[flag]
pvals <- pvals[flag]
zscores <- signif(zscores, digits=3)
pvals <- sapply(pvals, function(x) min(x,1))
## Adjust P-values for multiple comparisons
adjpvals <- stats::p.adjust(pvals, method=p.adjust.method)
pvals <- signif(pvals, digits=2)
adjpvals <- sapply(adjpvals, function(x) min(x,1))
adjpvals <- signif(adjpvals, digits=2)
####################################################################################
endT <- Sys.time()
message(paste(c("\nEnd at ",as.character(endT)), collapse=""), appendLF=T)
runTime <- as.numeric(difftime(strptime(endT, "%Y-%m-%d %H:%M:%S"), strptime(startT, "%Y-%m-%d %H:%M:%S"), units="secs"))
message(paste(c("Runtime in total is: ",runTime," secs\n"), collapse=""), appendLF=T)
########################################
if(1){
## overlaps
overlaps <- lapply(overlaps, function(x){
ind <- match(x, allGeneID)
names(x) <- allSymbol[ind]
x
})
}
########################################
eTerm <- list(set_info = set_info,
gs = gs,
data = data,
overlap = overlaps,
zscore = zscores,
pvalue = pvals,
adjp = adjpvals,
call = match.call()
)
class(eTerm) <- "eTerm"
invisible(eTerm)
}
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