| 19e9145d |
#' unique the microarray data
#'
#' @description get unique the microarray data for each gene id.
#'
#' @param exprsData dataset of expression comparison data
#' @param method method must be Max, Median or Min
#' @param condenseName column names to be condensed
#'
#' @return a dataframe of expression data without duplicates
#' @keywords network
#' @examples
#' data("example.data")
#' example.microarrayData<-uniqueExprsData(example.data$example.microarrayData,
#' method="Max", condenseName='logFC')
|
| 99f4c5eb |
#'
|
| 19e9145d |
#' @export
#' @importFrom plyr . ddply
|
| 99f4c5eb |
#' @importFrom graphics symbols
#' @importFrom stats median
|
| 19e9145d |
#'
uniqueExprsData<-function(exprsData, method='Max', condenseName='logFC'){
if(!(method %in% c("Max", "Median", "Min"))){
stop("method must be Max, Median or Min")
}
|
| 99f4c5eb |
if(!checkCName("symbols", exprsData)){
|
| 19e9145d |
stop("symbols is not a valide colname of exprsData")
}
|
| 99f4c5eb |
if(!checkCName(condenseName, exprsData)){
|
| 19e9145d |
stop(paste(condenseName," is not a valide colname of exprsData"))
}
if(!is.data.frame(exprsData)){
exprsData<-as.data.frame(exprsData)
}
if(!is.numeric(exprsData[ , condenseName])){
stop(paste("class of", condenseName, "is not a numeric column"))
}
exprsData<-switch(method,
Max =plyr::ddply(exprsData, plyr::.(symbols),
|
| 99f4c5eb |
getMax,
|
| 19e9145d |
condenseName),
Median=plyr::ddply(exprsData, plyr::.(symbols),
|
| 99f4c5eb |
getMedian,
|
| 19e9145d |
condenseName),
Min =plyr::ddply(exprsData, plyr::.(symbols),
|
| 99f4c5eb |
getMin,
|
| 19e9145d |
condenseName)
)
exprsData
}
#' convert gene IDs by id map
#' @description For same gene, there are multple gene alias.
#' In order to eliminate the possibility of missing any connections,
#' convert the gene symbols to unique gene ids is important.
#' This function can convert the gene symbols to unique ids and
#' convert it back according a giving map.
#' @param x a matrix or dataframe contain the columns to be converted.
#' @param IDsMap a character vector of the identifier map
#' @param ByName the column names to be converted
#' @return a matrix or dataframe with converted gene IDs
#' @examples
#' data("ce.IDsMap")
#' bind<-cbind(from="daf-16", to=c("fkh-7", "hlh-13", "mxl-3", "nhr-3", "lfi-1"))
#' convertID(toupper(bind), ce.IDsMap, ByName=c("from", "to"))
#' @keywords convert
#' @export
#'
convertID<-function(x, IDsMap, ByName=c("from", "to")){
if((!is.character(IDsMap)) | (is.null(IDsMap))){
stop("invalide IDsMap")
}
for(i in 1:length(ByName)){
|
| 99f4c5eb |
if(!checkCName(ByName[i],x)){
|
| 19e9145d |
stop(paste(ByName[i],"is not a valide colname of x"))
}
x[,ByName[i]]<-IDsMap[as.character(x[,ByName[i]])]
}
x
}
#' construct the regulatory network
#' @description Get all the connections of interesting genes from regulatory map.
#' @param TFbindingTable a matrix or data.frame with interesting genes.
#' Column names must be 'from', 'to'
#' @param interactionmap Transcription regulatory map.
#' Column names of interactionmap must be 'from','to'
#' @param level Depth of node path
#'
#' @return a dataframe or matrix of all the connections of interesting genes
#' @keywords network
#' @examples
#' data("ce.interactionmap")
#' data("example.data")
#' xx<-buildNetwork(example.data$ce.bind, ce.interactionmap, level=2)
#' @export
buildNetwork<-function(TFbindingTable, interactionmap, level=3){
|
| 99f4c5eb |
checkMap(interactionmap, TFbindingTable)
|
| 19e9145d |
if(level>0){
|
| ff7e46bd |
y<-interactionmap[interactionmap[ , "from"] %in% unique(as.character(TFbindingTable[ , "to"])), ,drop=F]
|
| 19e9145d |
y<-unique(y)
z<-y[!(y[,"to"] %in% TFbindingTable[,"to"]), , drop=F]
nrow1<-nrow(TFbindingTable)
TFbindingTable<-rbind(TFbindingTable, y)
TFbindingTable<-unique(TFbindingTable)
level<-level-1
if(level>0){
nrow2<-nrow(TFbindingTable)
if(nrow2>nrow1){
y<-buildNetwork(z, interactionmap, level)
TFbindingTable<-rbind(TFbindingTable, y)
}
TFbindingTable<-unique(TFbindingTable)
}
}
TFbindingTable
}
#' filter the regulatory network table by expression profile
#' @description verify every nodes in the regulatory network by expression profile
#' @param rootgene name of root gene. It must be the ID used in xx regulatory network
#' @param sifNetwork Transcription regulatory network table.
#' Column names of xx must be 'from','to'
#' @param exprsData dataset of expression comparison data,
#' which should contain column logFC and column given by exprsDataByName
#' @param mergeBy The column name contains ID information used to merge with
#' 'to' column of sifNetwork in exprsData
#' @param miRNAlist vector of microRNA ids.
#' @param remove_miRNA remove miRNA from the network or not.
#' Bool value, TRUE or FALSE
#' @param tolerance maximum number of unverified nodes in each path
#' @param cutoffPVal cutoff p value of valid differential expressed gene/miRNA
#' @param cutoffLFC cutoff log fold change value of a valid differential
#' expressed gene/miRNA
#' @param minify Only keep the best path if multiple paths exists for single node?
#' Bool value, TRUE or FALSE
#' @param miRNAtol take miRNA expression into account for tolerance calculation.
#' Bool value, TRUE or FALSE
#' @return a dataframe of filtered regulatory network by expression profile
#' @import Rcpp
#' @useDynLib GeneNetworkBuilder
#' @export
#'
#' @examples
#' data("ce.miRNA.map")
#' data("example.data")
#' data("ce.interactionmap")
#' data("ce.IDsMap")
#' sifNetwork<-buildNetwork(example.data$ce.bind, ce.interactionmap, level=2)
#' cifNetwork<-filterNetwork(rootgene=ce.IDsMap["DAF-16"], sifNetwork=sifNetwork,
|
| 99f4c5eb |
#' exprsData=uniqueExprsData(example.data$ce.exprData, "Max", condenseName='logFC'),
#' mergeBy="symbols",
#' miRNAlist=as.character(ce.miRNA.map[ , 1]), tolerance=1)
|
| 19e9145d |
#' @keywords network
#'
filterNetwork<-function(rootgene, sifNetwork, exprsData, mergeBy="symbols", miRNAlist, remove_miRNA=FALSE,
tolerance=0, cutoffPVal=0.01, cutoffLFC=0.5, minify=TRUE, miRNAtol=FALSE)
{
|
| 99f4c5eb |
checkMCName(sifNetwork)
|
| 19e9145d |
if(!missing(miRNAlist)){
if(!is.vector(miRNAlist)){
stop("miRNAlist should be a vector")
}
}
|
| 99f4c5eb |
if(!checkCName(mergeBy, exprsData)){
|
| 19e9145d |
stop(paste(mergeBy, "is not a column name of exprsData"))
}
|
| 99f4c5eb |
if(!checkCName("logFC", exprsData)){
|
| 19e9145d |
stop("logFC is not a column name of exprsData")
}
if(!is.numeric(exprsData[ , "logFC"])){
stop("class of exprsData[ , \"logFC\"] is not a numeric column")
}
|
| 99f4c5eb |
if(!checkCName("P.Value", exprsData)){
|
| 19e9145d |
stop("P.Value is not a column name of exprsData")
}
if(!is.numeric(exprsData[ , "P.Value"])){
stop("class of exprsData[ , \"P.Value\"] is not a numeric column")
}
if(!is.numeric(cutoffLFC)){
stop("cutoffLFC is not a numeric")
}
if(!is.numeric(cutoffPVal)){
stop("cutoffPVal is not a numeric")
}
if(any(duplicated(exprsData[,mergeBy]))){
stop("expresData has multiple logFC for same ID. Please try ?uniqueExprsData")
}
if(!is.logical(minify)){
stop("minify is not a logical")
}
if(!is.logical(miRNAtol)){
stop("miRNAtol is not a logical")
}
tolerance<-round(tolerance)
|
| e6873ca4 |
cifNetwork<-merge(sifNetwork, exprsData,
by.x="to", by.y=mergeBy, all.x=TRUE)
if(missing(rootgene)){## unrooted
rootgene <- NA
}
if(is.na(rootgene) | is.null(rootgene)){## unrooted
## create a fake rootgene
## the fake rootgene will connect to all the genes with filtered conditions
rootgene <- "NANANA"
fakeroot <- TRUE
if(rootgene %in% c(cifNetwork$from, cifNetwork$to)){
rootgene <- make.names(c(cifNetwork$from, cifNetwork$to, rootgene))
rootgene <- rootgene[length(rootgene)]
}
rootlogFC <- cutoffLFC + 1
cifNetwork_filtered <- cifNetwork[!is.na(cifNetwork[, "logFC"]) &
!is.na(cifNetwork[, "P.Value"]),
, drop=FALSE]
cifNetwork_filtered <- cifNetwork_filtered[
abs(cifNetwork_filtered[, "logFC"])>=cutoffLFC &
cifNetwork_filtered[, "P.Value"]<=cutoffPVal,
, drop=FALSE]
cifNetwork_filtered <-
cifNetwork[cifNetwork$to %in% cifNetwork_filtered$from, , drop=FALSE]
cifNetwork_filtered$from <- rootgene
cifNetwork_filtered <- unique(cifNetwork_filtered)
cifNetwork <- rbind(cifNetwork, cifNetwork_filtered)
}else{
fakeroot <- FALSE
rootlogFC<-exprsData[exprsData[ , mergeBy] == rootgene, "logFC"]
rootlogFC<-rootlogFC[!is.na(rootlogFC)]
rootlogFC<-ifelse(length(rootlogFC) < 1, 0.0, rootlogFC[1])
}
|
| 19e9145d |
## convert NA to 0 for logFC
cifNetwork.logFC<-cifNetwork[,"logFC"]
cifNetwork.logFC[is.na(cifNetwork.logFC)]<-0.0
cifNetwork.pValue<-cifNetwork[,"P.Value"]
|
| e6873ca4 |
cifNetwork.pValue[is.na(cifNetwork.pValue)]<-1.0
|
| 19e9145d |
## label microRNA
cifNetwork$miRNA<-FALSE
cifNetwork$dir<-2
if(!missing(miRNAlist)){
if(length(miRNAlist)>0){
cifNetwork$miRNA<-ifelse(cifNetwork$to %in% miRNAlist, TRUE, FALSE)
cifNetwork$dir<-ifelse(cifNetwork$from %in% miRNAlist, 0, 2)
}
}
## remove micorRNA
if(remove_miRNA){
cifNetwork.logFC<-cifNetwork.logFC[!cifNetwork$miRNA]
|
| e6873ca4 |
cifNetwork.pValue <- cifNetwork.pValue[!cifNetwork$miRNA]
cifNetwork<-cifNetwork[!cifNetwork$miRNA, ]
|
| 19e9145d |
}
|
| e6873ca4 |
|
| 19e9145d |
cifNetwork.list <- .Call("filterNodes",
as.character(cifNetwork$from),
as.character(cifNetwork$to),
cifNetwork$miRNA,
cifNetwork.logFC,
cifNetwork.pValue,
cifNetwork$dir,
nrow(cifNetwork),
rootgene[1],
rootlogFC[1],
tolerance[1],
minify[1],
miRNAtol[1],
cutoffLFC[1],
cutoffPVal[1]
)
cifNetwork.list <- do.call(rbind, lapply(names(cifNetwork.list),
function(.name, .ele){
if(length(.ele[[.name]])>0){
cbind(from=.ele[[.name]], to=.name)
}else{
cbind(from=NA, to=.name)
}
},
cifNetwork.list)
)
|
| e6873ca4 |
if(minify){
cifNetwork <- merge(cifNetwork, cifNetwork.list)
}else{
cifNetwork_s <- merge(cifNetwork, cifNetwork.list)
nodes <- unique(c(cifNetwork_s$from, cifNetwork_s$to))
cifNetwork <- cifNetwork[cifNetwork$from %in% nodes &
cifNetwork$to %in% nodes, , drop=FALSE]
}
if(fakeroot){
cifNetwork$from[cifNetwork$from==rootgene] <- NA
}
|
| 19e9145d |
cifNetwork
}
#' generate an object of grahpNEL to represent the regulation network
#' @description generate an object of grahpNEL to represent the regulation network.
|
| 6f1e44c1 |
#' Each node will has three attributes: size, borderColor and fill.
#' The size will be mapped to the length of its edges. The node fill color will
#' be mapped to logFC.
|
| 19e9145d |
#' @param cifNetwork dataframe used to draw network graph. column names of
#' cifNetwork must contain 'from', 'to', 'logFC' and 'miRNA'
|
| 69f41bbc |
#' @param nodeData The node data. If it is not provide, node data will be
#' retrieved from cifNetwork for the 'to' nodes.
|
| 19e9145d |
#' @param nodesDefaultSize nodes default size
#' @param nodecolor a character vector of color set.
|
| 0bd90be9 |
#' The node color will be mapped to color set by log fold change.
#' Or the column names for the colors.
|
| 19e9145d |
#' @param nodeBg background of node
#' @param nodeBorderColor a list of broder node color set.
#' nodeBorderColor's element must be gene and miRNA
|
| ff7e46bd |
#' @param edgeWeight the weight of edge. It can be a column name of cifNetwork.
|
| 6f1e44c1 |
#' @param edgelwd the default width of edge. If edgeWeight is set, the edgelwd
#' will be mapped to the edgeWeight.
|
| 0aac1f49 |
#' @param ... any parameters can be passed to \link[graph:settings]{graph.par}
|
| 19e9145d |
#' @return An object of graphNEL class of the network
#' @import graph
|
| 99f4c5eb |
#' @importFrom grDevices colorRampPalette
#' @importFrom methods new
|
| 19e9145d |
#' @export
#' @examples
#' data("ce.miRNA.map")
#' data("example.data")
#' data("ce.interactionmap")
#' data("ce.IDsMap")
#' sifNetwork<-buildNetwork(example.data$ce.bind, ce.interactionmap, level=2)
#' cifNetwork<-filterNetwork(rootgene=ce.IDsMap["DAF-16"], sifNetwork=sifNetwork,
|
| 99f4c5eb |
#' exprsData=uniqueExprsData(example.data$ce.exprData, "Max", condenseName='logFC'),
#' mergeBy="symbols",
#' miRNAlist=as.character(ce.miRNA.map[ , 1]), tolerance=1)
|
| 19e9145d |
#' gR<-polishNetwork(cifNetwork)
#' ## browseNetwork(gR)
#' @keywords network
#'
|
| 69f41bbc |
polishNetwork<-function(cifNetwork,
nodeData,
|
| 6f1e44c1 |
nodesDefaultSize=48,
nodecolor=colorRampPalette(c("green", "yellow", "red"))(5),
nodeBg="white",
|
| 19e9145d |
nodeBorderColor=list(gene='darkgreen',miRNA='darkblue'),
|
| 69f41bbc |
edgeWeight=NA, edgelwd=0.5, ...)
|
| 19e9145d |
{
cname<-c("from", "to")
if(!is.data.frame(cifNetwork)){
stop("cifNetwork should be a data.frame")
}
|
| 69f41bbc |
|
| 19e9145d |
cifNetwork<-cifNetwork[!duplicated(cifNetwork[,cname]), ]
edge<-cifNetwork[cifNetwork$from!="" & cifNetwork$to!="", cname]
node<-c(as.character(unlist(edge)))
node<-node[!is.na(node)]
|
| e6873ca4 |
node<-node[node!=""]
|
| 19e9145d |
node<-unique(node)
if(length(node) <= 1){
stop("Can not built network for the inputs. Too less connections.")
}
|
| 69f41bbc |
if(missing(nodeData)){
if(length(intersect(c("from", "to", "logFC"), colnames(cifNetwork)))<3){
stop("colnames of cifNetwork must contain 'from', 'to', and 'logFC'");
}
nodeData <- cifNetwork[, !colnames(cifNetwork) %in% c("from", "dir"),
drop=FALSE]
nodeData <- nodeData[!duplicated(nodeData[, "to"]), , drop=FALSE]
rownames(nodeData) <- nodeData[, 'to', drop=TRUE]
nodeData$to <- NULL
}else{
stopifnot(length(dim(nodeData))==2)
if(length(rownames(nodeData))==0){
stop('nodeData must have rownames ',
'and the rownames should be the names ofthe nodes.')
}
if(!all(node %in% rownames(nodeData))){
warning('not all nodes is in the rownames of nodeData')
}
}
preDefinedColor <- FALSE
if(length(nodecolor) < 2){
if(nodecolor %in% colnames(nodeData)){
preDefinedColor <- TRUE
}else{
stop("nodecolor should have more than 1 elements")
}
}
if(all(c('gene', 'miRNA') %in% colnames(cifNetwork))){
if(length(setdiff(c('gene', 'miRNA'), names(nodeBorderColor))) > 0){
stop("nodeBorderColor's element must be 'gene' and 'miRNA'")
}
}else{
if(!'gene' %in% names(nodeBorderColor)){
nodeBorderColor[["gene"]] <- 'darkgreen'
}
}
|
| 6f1e44c1 |
if(length(edgeWeight)==1 && edgeWeight %in% colnames(cifNetwork)){
cifNetwork[is.na(cifNetwork[, edgeWeight]), edgeWeight] <- 0
}
|
| 69f41bbc |
edL<-split(cifNetwork, cifNetwork[,"from"])
|
| 6f1e44c1 |
edL<-lapply(node,function(.ele){
|
| 19e9145d |
.ele<-edL[[.ele]]
if(is.null(.ele)){
.ele<-list(edges=c(),weights=c())
}else{
|
| 6f1e44c1 |
if(length(edgeWeight)==1 && edgeWeight %in% colnames(.ele)){
.ele<-list(edges=as.character(.ele$to),
weights=abs(.ele[, edgeWeight, drop=TRUE]))
|
| 19e9145d |
}else{
.ele<-list(edges=as.character(.ele$to),weights=rep(1,length(.ele$to)))
}
}
|
| 6f1e44c1 |
})
|
| 19e9145d |
names(edL)<-node
gR<-new("graphNEL", nodes=node, edgeL=edL, edgemode="directed")
## set node default data
nodeDataDefaults(gR, attr="label") <- NA
|
| 69f41bbc |
nodeDataDefaults(gR, attr="fill")<-nodeBg
nodeDataDefaults(gR, attr="size")<-nodesDefaultSize
nodeDataDefaults(gR, attr='borderColor') <- nodeBorderColor[["gene"]]
|
| 19e9145d |
for(i in node) {
nodeData(gR, n=i, attr="label") <- i
}
|
| 0bd90be9 |
## add additional message
|
| 69f41bbc |
additionalInfoColAll <- colnames(nodeData)
|
| 53a14951 |
additionalInfoColAll <-
additionalInfoColAll[!additionalInfoColAll %in%
|
| 69f41bbc |
c("to", "from", "dir")]
|
| 53a14951 |
addAdditionalInfo <- function(gR, types, defaultValue){
additionalInfoCol <- additionalInfoColAll[
vapply(additionalInfoColAll, FUN=function(.e){
|
| 69f41bbc |
inherits(nodeData[, .e], types) &&
length(unique(nodeData[, .e])) > 1
|
| 53a14951 |
}, FUN.VALUE=FALSE)
]
if(length(additionalInfoCol)){
for(j in additionalInfoCol){
nodeDataDefaults(gR, attr=j)<-defaultValue
|
| 69f41bbc |
for(i in intersect(node, rownames(nodeData))){
nodeData(gR, n=i, attr=j) <- nodeData[i, j]
|
| 53a14951 |
}
|
| 0bd90be9 |
}
}
|
| 53a14951 |
return(gR)
|
| 0bd90be9 |
}
|
| 53a14951 |
## additional characters
gR <- addAdditionalInfo(gR, c("character", "factor"), "")
## additional numeric logical
gR <- addAdditionalInfo(gR, c("numeric", "logical"), NA)
|
| 69f41bbc |
## set node size
if(!'size' %in% colnames(nodeData)){
for(i in unique(as.character(cifNetwork$from))){
if(!is.na(i)){
nodeData(gR, n=i, attr="size")<-
ceiling(5*length(edL[[i]]$edges)/length(node)) *
nodesDefaultSize/2 + nodesDefaultSize
}
}
}
|
| 53a14951 |
|
| 69f41bbc |
## set node color
if(!'fill' %in% colnames(nodeData)){
if(!preDefinedColor){
if('logFC' %in% colnames(nodeData)){
lfc <- nodeData[, 'logFC', drop=TRUE]
lfcMax<-ceiling(max(abs(lfc[!is.infinite(lfc)]), na.rm = TRUE))
lfcSeq<-seq(-1*lfcMax,lfcMax,length.out=length(nodecolor)+1)
lfc[is.infinite(lfc)] <- sign(lfc) * lfcMax
colors <- nodecolor[findInterval(lfc, lfcSeq, all.inside = TRUE)]
names(colors) <- rownames(nodeData)
colors[is.na(colors)] <- nodeBg
for(i in intersect(node, names(colors))){
nodeData(gR, n=i, attr="fill") <- colors[i]
|
| 0bd90be9 |
}
}
|
| 69f41bbc |
}else{
colors <- nodeData[, nodecolor, drop=FALSE]
for(i in intersect(node, rownames(colors))) {
nodeData(gR, n=i, attr="fill") <- colors[i, nodecolor, drop=TRUE]
|
| 19e9145d |
}
}
}
|
| 69f41bbc |
## set node border color
if('miRNA' %in% colnames(nodeData) &&
'miRNA' %in% names(nodeBorderColor) &&
!'borderColor' %in% colnames(nodeData)){
if(is.logical(nodeData$miRNA)){
miRNAs<-rownames(nodeData[nodeData[,"miRNA"], , drop=FALSE])
for(i in intersect(node, miRNAs)){
nodeData(gR, n=i, attr="borderColor")<-nodeBorderColor$miRNA
}
|
| 19e9145d |
}
}
|
| 69f41bbc |
graph::nodeRenderInfo(gR) <- list(col=nodeData(gR, attr='borderColor'),
fill=nodeData(gR, attr='fill'),
...)
|
| ff7e46bd |
cifNetwork.s <- cifNetwork[!is.na(cifNetwork$from) & !is.na(cifNetwork$to),
, drop=FALSE]
if(length(edgeWeight)==1 && edgeWeight %in% colnames(cifNetwork)){
|
| 6f1e44c1 |
graph::edgeRenderInfo(gR) <- list(lwd=edgelwd)
|
| 69f41bbc |
lwdScore <- cifNetwork.s[, edgeWeight, drop=TRUE]
|
| dcbbd4b5 |
rg <- range(lwdScore)
lwd <- findInterval(lwdScore, seq(from=rg[1], to=rg[2], length.out=10),
all.inside = TRUE)
|
| 6f1e44c1 |
lwd <- lwd*edgelwd
|
| 69f41bbc |
names(lwd) <- paste(cifNetwork.s$from, cifNetwork.s$to, sep='~')
|
| dcbbd4b5 |
lwd <- lwd[names(graph::edgeRenderInfo(gR, 'lwd'))]
graph::edgeRenderInfo(gR) <- list(lwd=lwd)
}else{
|
| 6f1e44c1 |
graph::edgeRenderInfo(gR) <- list(lwd=edgelwd)
|
| dcbbd4b5 |
}
|
| 19e9145d |
gR
}
|
