R/enrichment.R
In angy89/TinderMIX: TinderMIX: An R package to cluster gene expression by contour plots
#' #'
#' #' This function perform enrichment for each gene label and compute a pathway wordcloud for every label
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gProfileR gprofiler
#' #' @importFrom wordcloud wordcloud
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param Mat matrix of gene labels
#' #' @param max.words max number of words in wordcluds
#' #' @param scale a vector of length 2 indicating the range of the size of the words.
#' #' @param random.order plot words in random order. If false, they will be plotted in decreasing frequency
#' #' @param min.freq words with frequency below min.freq will not be plotted
#' #' @param toplot boolean specifying if plotting wordcloud
#' #' @return a list with the enriched pathways for each cluster of genes
#' #'
#'
#'
#' create_tic_tac_toe_wordcloud = function(Enriched_list = Enriched_list,max.words = 200,scale = c(0.8,2.5),random.order=FALSE,min.freq = 0,toplot=TRUE){
#' # library(wordcloud)
#' # library(clusterProfiler)
#'
#' # Enriched_list = list()
#' # for(i in c(1,4,7,2,5,8,3,6,9)){
#' # gi = Mat[,i]
#' # all_gi = names(gi[gi!=0])
#' # # gi_pos = names(gi[gi>0])
#' # # gi_neg = names(gi[gi<0])
#' #
#' # EP_all = compute_pathways(geneList = all_gi,corrType = corrType,type_enrich=type_enrich, org_enrich = org_enrich,pth = pth,sig = sig,mis = mis,only_annotated=only_annotated )
#' # # toRem = which(EP_all$Description %in% "Reactome")
#' # # if(length(toRem)>0) EP_all = EP_all[-toRem,]
#' #
#' # Enriched_list[[colnames(Mat)[i]]] = EP_all
#' #
#' # }
#'
#' if(toplot) par(mfrow = c(3,3), oma = c(0,0,0,0) + 0.1, mar = c(0,0,0,0) + 0.1)
#' WCDFList = list()
#' WCDF = c()
#' EL = c()
#'
#' for(i in 1:length(Enriched_list)){
#' EP_all = Enriched_list[[i]]
#' if(nrow(EP_all)==0){
#' if(toplot)
#' plot(1, type = "n", axes=FALSE, xlab="", ylab="")
#' }else{
#'
#' if(nrow(EP_all)==1 & (sum(EP_all[1,] == rep("",5))==5)){
#' if(toplot)
#' plot(1, type = "n", axes=FALSE, xlab="", ylab="")
#' }else{
#' #d = data.frame(word = substring(text = EP_all$Description,first = 1,last = 20), freq = log(EP_all$pValueAdj) * -2)
#' d = data.frame(word = EP_all$Description, freq = log(EP_all$pValueAdj) * -2)
#' WCDFList[[i]] = d
#' WCDF = rbind(WCDF, cbind(d, names(Enriched_list)[i]))
#'
#' EL = rbind(EL, data.frame(word = EP_all$Description, pValueAdj = EP_all$pValueAdj, label = names(Enriched_list)[i]))
#'
#' if(toplot){
#' # set.seed(1234)
#' wordcloud::wordcloud(words = d$word, freq = d$freq, min.freq = min.freq,
#' max.words=max.words, random.order=random.order,
#' colors=brewer.pal(8, "Dark2"), scale = scale)
#'
#' }
#' }
#' }
#' }
#' colnames(WCDF)[3] = "label"
#' return(list(Enriched_list=Enriched_list,WCDFList=WCDFList,WCDF=WCDF,EL=EL))
#' }
#'
#' #'
#' #' This function create prototypes for a list of pathways
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gProfileR gprofiler
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param enrichedPath dataframe of enriched pathways coming from the compute_pathways function
#' #' @param annIDs vector with the pathways IDs for which the prototype will be computed
#' #' @param contour_res a list with the contours object returned in output by the create_contour function
#' #' @param nPerm number of permutation to run to compute the pvalue associated to pathway correaltion
#' #' @return a list with the prototypes of the pathways, their genes correlation and a pvalue
#' #'
#' #'
#'
#' create_pathway_prototypes = function(enrichedPath = enrichedPath, annIDs, contour_res = contour_res, nPerm = 100){
#' # diss.cor <-1- abs(stats::cor(contour_res$GenesMap,method="pearson"))
#' rownames(enrichedPath) = enrichedPath[,"annID"]
#'
#' if((sum(annIDs %in% rownames(enrichedPath)))< length(annIDs)){
#' print("ERROR: pathways IDs not present in the dataset")
#' return(NULL)
#' }
#'
#' RPGenes = contour_res$RPGenes
#' names(RPGenes) = toupper(names(RPGenes))
#' GenesMap = contour_res$GenesMap
#' colnames(GenesMap) = toupper(colnames(GenesMap))
#'
#' Pathways_prot = list()
#'
#' for(i in 1:length(annIDs)){
#'
#' print(paste(i,"/", length(annIDs)))
#'
#' genes = unlist(strsplit(x = enrichedPath[annIDs[i],2],split = ","))
#' pt = create_prot(RPGenes = RPGenes, genes=genes)
#'
#' if(length(genes)>1){
#' cor_pt = mean(abs(stats::cor(GenesMap[,genes],method="pearson")))
#' randomCor = c()
#' pb = txtProgressBar(min = 1, max = nPerm, style = 3)
#' for(permIdx in 1:nPerm){
#' randomCor = c(randomCor, mean(abs(stats::cor(GenesMap[,sample(x = 1:ncol(GenesMap),size = length(genes))],method="pearson"))))
#' setTxtProgressBar(pb,permIdx)
#' }
#' close(pb)
#' pval_pt = 1 - sum(randomCor < cor_pt) / length(randomCor)
#' protInfo = list(prototype = pt, correlation = cor_pt, pvalue = pval_pt)
#' }else{
#' protInfo = list(prototype = pt, correlation = NA, pvalue = NA)
#'
#' }
#'
#' Pathways_prot[[enrichedPath[annIDs[i],"Description"]]] = protInfo
#' }
#'
#' return(Pathways_prot)
#' }
#'
#'
#' create_prot = function(RPGenes,genes, nDosesInt = 50, nTimesInt = 50){
#' mx = rep(0, nDosesInt)
#' my = rep(0, nTimesInt)
#' mz = matrix(0,nDosesInt,nTimesInt)
#' for(j in 1:length(genes)){
#' mz = mz + RPGenes[[genes[j]]][[3]]
#' }
#' mx = RPGenes[[genes[1]]][[1]]
#' my = RPGenes[[genes[1]]][[2]]
#' mz = mz/length(genes)
#' L = list(mx, my, mz)
#' return(L)
#' }
#'
#'
#' #'
#' #' This function perform enchment of the genes in each cluster
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gProfileR gprofiler
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param optimal_clustering vector of final clustering
#' #' @param corrType string specifing the algorithm used for determining the significance threshold, one of gSCS, fdr, bonferroni. Default: fdr
#' #' @param type_enrich string specifying the enrichment type. Default = KEGG
#' #' @param org_enrich string specifying the organism. Default = rnorvegicus
#' #' @param pth numeric value specifyint the pvalue threshold. Default = 0.05
#' #' @param sig whether all or only statistically significant results should be returned
#' #' @param mis minimum size of functional category, smaller categories are excluded
#' #' @param only_annotated statistical domain size, one of "annotated", "known"
#' #' @return a list with the enriched pathways for each cluster of genes
#' #'
#' #'
#'
#' compute_enrichment_for_clusters = function(optimal_clustering,corrType = "fdr",type_enrich="KEGG", org_enrich = "rnorvegicus",pth = 0.05,sig = FALSE,mis = 0,only_annotated=FALSE){
#'
#' nClust = length(unique(optimal_clustering))
#'
#' GList = list()
#' for(i in 1:nClust){
#' genelist = names(optimal_clustering)[optimal_clustering == i]
#' GList[[i]] = cbind(genelist, 1:length(genelist))
#'
#' }
#'
#' GList = convert_genes(organism = org_enrich, GList=GList, annType = "SYMBOL")
#'
#'
#' if(corrType == "none"){
#' print("Nominal PValue")
#' EnrichDatList = lapply(GList,enrich,type_enrich,org_enrich,pth,"bonferroni", sig = sig, mis = mis, only_annotated=only_annotated)
#' for(i in 1:length(EnrichDatList)){
#' ERi = EnrichDatList[[i]]
#' ERi$pValueAdj = ERi$pValueAdj / length(ERi$pValueAdj)
#' ERi$pValue = ERi$pValueAdj / length(ERi$pValue)
#' EnrichDatList[[i]] = ERi
#' }
#' }else{
#' EnrichDatList = lapply(GList,enrich,type_enrich,org_enrich,pth,corrType, sig = sig, mis = mis, only_annotated=only_annotated)
#' }
#'
#' return(EnrichDatList)
#' }
#'
#' #'
#' #' This function perform enchment of a set of genes
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gProfileR gprofiler
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param geneList vector of gene identifiers
#' #' @param corrType string specifing the algorithm used for determining the significance threshold, one of gSCS, fdr, bonferroni. Default: fdr
#' #' @param type_enrich string specifying the enrichment type. Default = KEGG
#' #' @param org_enrich string specifying the organism. Default = rnorvegicus
#' #' @param pth numeric value specifyint the pvalue threshold. Default = 0.05
#' #' @param sig whether all or only statistically significant results should be returned
#' #' @param mis minimum size of functional category, smaller categories are excluded
#' #' @param only_annotated statistical domain size, one of "annotated", "known"
#' #' @param annType gene annotation type. default = SYMBOL
#' #' @return a list with the enriched pathways for each cluster of genes
#' #'
#' #'
#' compute_pathways = function(geneList = rownames(res$Mat),corrType = "fdr",type_enrich="KEGG", annType = "SYMBOL",org_enrich = "hsapiens",pth = 0.05,sig = FALSE,mis = 0,only_annotated=FALSE ){
#' GList = list(matrix(geneList, ncol = 1))
#'
#' tryCatch(
#' EnrichDatList = {
#' GList = convert_genes(organism = org_enrich, GList=GList, annType = annType)
#'
#' if(corrType == "none"){
#' print("Nominal PValue")
#' EnrichDatList = lapply(GList,enrich,type_enrich,org_enrich,pth,"bonferroni", sig = sig, mis = mis, only_annotated=only_annotated)
#' for(i in 1:length(EnrichDatList)){
#' ERi = EnrichDatList[[i]]
#' ERi$pValueAdj = ERi$pValueAdj / length(ERi$pValueAdj)
#' ERi$pValue = ERi$pValueAdj / length(ERi$pValue)
#' EnrichDatList[[i]] = ERi
#' }
#' }else{
#' EnrichDatList = lapply(GList,enrich,type_enrich,org_enrich,pth,corrType, sig = sig, mis = mis, only_annotated=only_annotated)
#' }
#' EnrichDatList = EnrichDatList[[1]]
#' },
#' error = function(e){
#' EnrichDatList = matrix(data = "",nrow = 1,ncol = 5)
#' colnames(EnrichDatList) = c("annID","gID" ,"pValue","pValueAdj","Description")
#' }
#' )
#'
#' return(EnrichDatList)
#'
#' }
#'
#' #'
#' #' This function perform enchment of the genes in each cluster
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gProfileR gprofiler
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param x a dataframe with the gene names on the first column
#' #' @param adjust_method string specifing the algorithm used for determining the significance threshold, one of gSCS, fdr, bonferroni. Default: fdr
#' #' @param type string specifying the enrichment type. Default = KEGG
#' #' @param org string specifying the organism. Default = rnorvegicus
#' #' @param pval numeric value specifyint the pvalue threshold. Default = 0.05
#' #' @param sig whether all or only statistically significant results should be returned
#' #' @param mis minimum size of functional category, smaller categories are excluded
#' #' @param only_annotated statistical domain size, one of "annotated", "known"
#' #' @return a list with the enriched pathways for each cluster of genes
#' #'
#'
#' enrich = function(x, type, org, pval, adjust_method,sig = FALSE, mis = 0, only_annotated = TRUE){
#' if(only_annotated){
#' domain_size = "annotated"
#' }else{
#' domain_size = "known"
#' }
#'
#' print("before gprofiler")
#' out = gProfileR::gprofiler(query = as.character(x[,1]),src_filter=type,organism=org,domain_size = domain_size,
#' max_p_value = pval, correction_method = adjust_method,significant = sig,min_isect_size = mis)
#'
#' #out = gProfileR::gprofiler(query = x[,1],src_filter=type,organism=org,max_p_value = pval,domain_size = "known")
#' print(out)
#'
#' ##print("after gprofiler")
#' out=out[,c("term.id","intersection","p.value","p.value","term.name")]
#' colnames(out) = c( "annID","gID","pValue","pValueAdj","Description")
#'
#' print(out)
#'
#' if(nrow(out)>0)
#' out$annID= gsub("KEGG:|REAC:","",out$annID)
#'
#' return(out)
#' }
#'
#'
#' #'
#' #' This function convert genes identifiers
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param organism a string specifying the organism under analysis
#' #' @param GList a list of genes identifier
#' #' @param annType string specifying the wanted gene identifier
#' #' @return a list with the converted genes identifiers
#' #'
#' convert_genes = function(organism = "hsapiens", GList, annType = "SYMBOL"){
#'
#' orgLibs <- list("hsapiens"=org.Hs.eg.db, "mmusculus"=org.Mm.eg.db, "rnorvegicus" = org.Rn.eg.db)
#' orgDB <- orgLibs[[organism]]
#'
#' if(annType == "SYMBOL"){
#' tmp = GList[[1]]
#' tmp <- AnnotationDbi::select(orgDB, keys=as.character(tmp[,1]), columns="SYMBOL", keytype=annType)
#' return(GList)
#' }
#'
#' for(i in 1:length(GList)){
#' M=GList[[i]]
#' genes = M[,1]
#'
#' selectAnnDF <- AnnotationDbi::select(orgDB, keys=genes, columns="SYMBOL", keytype=annType)
#'
#' toRem = which(is.na(selectAnnDF[,2]))
#' if(length(toRem)>0){
#' selectAnnDF = selectAnnDF[-toRem,]
#' }
#'
#' #remove eventual duplicates
#' selectAnnDF = selectAnnDF[!duplicated(selectAnnDF$SYMBOL),]
#'
#' M = M[M[,1] %in% selectAnnDF[,1],]
#'
#' M = as.matrix(M)
#'
#' # macke sure they match by the key type
#' matches = match(selectAnnDF[,1],M[,1])
#'
#' #all(M$ID[matches] == selectAnnDF[,1])
#' M[matches,1] = selectAnnDF[,2]
#'
#' #update gene symbols
#' GList[[i]] = M
#'
#' }
#'
#' return(GList)
#' }
#'
#' #' This function create an excel file with the same format of the input need by the FunMappOne tool
#' #' @import xlsx
#' #'
#' #' @param optimal_clustering is a numeric vector with the clustering result for every gene
#' #' @param filePath is a string specifying the path of the xlsx file
#' #'
#'
#' write_xlsx_for_funmappone = function(optimal_clustering,filePath = "../contour_clustering/gene_clustering.xlsx"){
#' genes1 = names(optimal_clustering)[optimal_clustering == 1]
#' xlsx::write.xlsx(genes1, filePath, sheetName = "Cluster_1",row.names = FALSE)
#'
#' nClust = length(unique(optimal_clustering))
#'
#' for(i in 2:nClust){
#' genesi = names(optimal_clustering)[optimal_clustering == i]
#' xlsx::write.xlsx(genesi, filePath, sheetName = paste("Cluster_",i,sep=""),append = TRUE,row.names = FALSE)
#' }
#'
#' grouping = cbind(paste("Cluster_",1:nClust,sep=""), 1:nClust)
#' colnames(grouping) = c("cluster", "group")
#' xlsx::write.xlsx(grouping, filePath, sheetName = "grouping",append = TRUE, row.names = FALSE)
#'
#' }
angy89/TinderMIX documentation built on Nov. 26, 2020, 9:26 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' #'
#' #' This function perform enrichment for each gene label and compute a pathway wordcloud for every label
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gProfileR gprofiler
#' #' @importFrom wordcloud wordcloud
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param Mat matrix of gene labels
#' #' @param max.words max number of words in wordcluds
#' #' @param scale a vector of length 2 indicating the range of the size of the words.
#' #' @param random.order plot words in random order. If false, they will be plotted in decreasing frequency
#' #' @param min.freq words with frequency below min.freq will not be plotted
#' #' @param toplot boolean specifying if plotting wordcloud
#' #' @return a list with the enriched pathways for each cluster of genes
#' #'
#'
#'
#' create_tic_tac_toe_wordcloud = function(Enriched_list = Enriched_list,max.words = 200,scale = c(0.8,2.5),random.order=FALSE,min.freq = 0,toplot=TRUE){
#' # library(wordcloud)
#' # library(clusterProfiler)
#'
#' # Enriched_list = list()
#' # for(i in c(1,4,7,2,5,8,3,6,9)){
#' # gi = Mat[,i]
#' # all_gi = names(gi[gi!=0])
#' # # gi_pos = names(gi[gi>0])
#' # # gi_neg = names(gi[gi<0])
#' #
#' # EP_all = compute_pathways(geneList = all_gi,corrType = corrType,type_enrich=type_enrich, org_enrich = org_enrich,pth = pth,sig = sig,mis = mis,only_annotated=only_annotated )
#' # # toRem = which(EP_all$Description %in% "Reactome")
#' # # if(length(toRem)>0) EP_all = EP_all[-toRem,]
#' #
#' # Enriched_list[[colnames(Mat)[i]]] = EP_all
#' #
#' # }
#'
#' if(toplot) par(mfrow = c(3,3), oma = c(0,0,0,0) + 0.1, mar = c(0,0,0,0) + 0.1)
#' WCDFList = list()
#' WCDF = c()
#' EL = c()
#'
#' for(i in 1:length(Enriched_list)){
#' EP_all = Enriched_list[[i]]
#' if(nrow(EP_all)==0){
#' if(toplot)
#' plot(1, type = "n", axes=FALSE, xlab="", ylab="")
#' }else{
#'
#' if(nrow(EP_all)==1 & (sum(EP_all[1,] == rep("",5))==5)){
#' if(toplot)
#' plot(1, type = "n", axes=FALSE, xlab="", ylab="")
#' }else{
#' #d = data.frame(word = substring(text = EP_all$Description,first = 1,last = 20), freq = log(EP_all$pValueAdj) * -2)
#' d = data.frame(word = EP_all$Description, freq = log(EP_all$pValueAdj) * -2)
#' WCDFList[[i]] = d
#' WCDF = rbind(WCDF, cbind(d, names(Enriched_list)[i]))
#'
#' EL = rbind(EL, data.frame(word = EP_all$Description, pValueAdj = EP_all$pValueAdj, label = names(Enriched_list)[i]))
#'
#' if(toplot){
#' # set.seed(1234)
#' wordcloud::wordcloud(words = d$word, freq = d$freq, min.freq = min.freq,
#' max.words=max.words, random.order=random.order,
#' colors=brewer.pal(8, "Dark2"), scale = scale)
#'
#' }
#' }
#' }
#' }
#' colnames(WCDF)[3] = "label"
#' return(list(Enriched_list=Enriched_list,WCDFList=WCDFList,WCDF=WCDF,EL=EL))
#' }
#'
#' #'
#' #' This function create prototypes for a list of pathways
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gProfileR gprofiler
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param enrichedPath dataframe of enriched pathways coming from the compute_pathways function
#' #' @param annIDs vector with the pathways IDs for which the prototype will be computed
#' #' @param contour_res a list with the contours object returned in output by the create_contour function
#' #' @param nPerm number of permutation to run to compute the pvalue associated to pathway correaltion
#' #' @return a list with the prototypes of the pathways, their genes correlation and a pvalue
#' #'
#' #'
#'
#' create_pathway_prototypes = function(enrichedPath = enrichedPath, annIDs, contour_res = contour_res, nPerm = 100){
#' # diss.cor <-1- abs(stats::cor(contour_res$GenesMap,method="pearson"))
#' rownames(enrichedPath) = enrichedPath[,"annID"]
#'
#' if((sum(annIDs %in% rownames(enrichedPath)))< length(annIDs)){
#' print("ERROR: pathways IDs not present in the dataset")
#' return(NULL)
#' }
#'
#' RPGenes = contour_res$RPGenes
#' names(RPGenes) = toupper(names(RPGenes))
#' GenesMap = contour_res$GenesMap
#' colnames(GenesMap) = toupper(colnames(GenesMap))
#'
#' Pathways_prot = list()
#'
#' for(i in 1:length(annIDs)){
#'
#' print(paste(i,"/", length(annIDs)))
#'
#' genes = unlist(strsplit(x = enrichedPath[annIDs[i],2],split = ","))
#' pt = create_prot(RPGenes = RPGenes, genes=genes)
#'
#' if(length(genes)>1){
#' cor_pt = mean(abs(stats::cor(GenesMap[,genes],method="pearson")))
#' randomCor = c()
#' pb = txtProgressBar(min = 1, max = nPerm, style = 3)
#' for(permIdx in 1:nPerm){
#' randomCor = c(randomCor, mean(abs(stats::cor(GenesMap[,sample(x = 1:ncol(GenesMap),size = length(genes))],method="pearson"))))
#' setTxtProgressBar(pb,permIdx)
#' }
#' close(pb)
#' pval_pt = 1 - sum(randomCor < cor_pt) / length(randomCor)
#' protInfo = list(prototype = pt, correlation = cor_pt, pvalue = pval_pt)
#' }else{
#' protInfo = list(prototype = pt, correlation = NA, pvalue = NA)
#'
#' }
#'
#' Pathways_prot[[enrichedPath[annIDs[i],"Description"]]] = protInfo
#' }
#'
#' return(Pathways_prot)
#' }
#'
#'
#' create_prot = function(RPGenes,genes, nDosesInt = 50, nTimesInt = 50){
#' mx = rep(0, nDosesInt)
#' my = rep(0, nTimesInt)
#' mz = matrix(0,nDosesInt,nTimesInt)
#' for(j in 1:length(genes)){
#' mz = mz + RPGenes[[genes[j]]][[3]]
#' }
#' mx = RPGenes[[genes[1]]][[1]]
#' my = RPGenes[[genes[1]]][[2]]
#' mz = mz/length(genes)
#' L = list(mx, my, mz)
#' return(L)
#' }
#'
#'
#' #'
#' #' This function perform enchment of the genes in each cluster
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gProfileR gprofiler
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param optimal_clustering vector of final clustering
#' #' @param corrType string specifing the algorithm used for determining the significance threshold, one of gSCS, fdr, bonferroni. Default: fdr
#' #' @param type_enrich string specifying the enrichment type. Default = KEGG
#' #' @param org_enrich string specifying the organism. Default = rnorvegicus
#' #' @param pth numeric value specifyint the pvalue threshold. Default = 0.05
#' #' @param sig whether all or only statistically significant results should be returned
#' #' @param mis minimum size of functional category, smaller categories are excluded
#' #' @param only_annotated statistical domain size, one of "annotated", "known"
#' #' @return a list with the enriched pathways for each cluster of genes
#' #'
#' #'
#'
#' compute_enrichment_for_clusters = function(optimal_clustering,corrType = "fdr",type_enrich="KEGG", org_enrich = "rnorvegicus",pth = 0.05,sig = FALSE,mis = 0,only_annotated=FALSE){
#'
#' nClust = length(unique(optimal_clustering))
#'
#' GList = list()
#' for(i in 1:nClust){
#' genelist = names(optimal_clustering)[optimal_clustering == i]
#' GList[[i]] = cbind(genelist, 1:length(genelist))
#'
#' }
#'
#' GList = convert_genes(organism = org_enrich, GList=GList, annType = "SYMBOL")
#'
#'
#' if(corrType == "none"){
#' print("Nominal PValue")
#' EnrichDatList = lapply(GList,enrich,type_enrich,org_enrich,pth,"bonferroni", sig = sig, mis = mis, only_annotated=only_annotated)
#' for(i in 1:length(EnrichDatList)){
#' ERi = EnrichDatList[[i]]
#' ERi$pValueAdj = ERi$pValueAdj / length(ERi$pValueAdj)
#' ERi$pValue = ERi$pValueAdj / length(ERi$pValue)
#' EnrichDatList[[i]] = ERi
#' }
#' }else{
#' EnrichDatList = lapply(GList,enrich,type_enrich,org_enrich,pth,corrType, sig = sig, mis = mis, only_annotated=only_annotated)
#' }
#'
#' return(EnrichDatList)
#' }
#'
#' #'
#' #' This function perform enchment of a set of genes
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gProfileR gprofiler
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param geneList vector of gene identifiers
#' #' @param corrType string specifing the algorithm used for determining the significance threshold, one of gSCS, fdr, bonferroni. Default: fdr
#' #' @param type_enrich string specifying the enrichment type. Default = KEGG
#' #' @param org_enrich string specifying the organism. Default = rnorvegicus
#' #' @param pth numeric value specifyint the pvalue threshold. Default = 0.05
#' #' @param sig whether all or only statistically significant results should be returned
#' #' @param mis minimum size of functional category, smaller categories are excluded
#' #' @param only_annotated statistical domain size, one of "annotated", "known"
#' #' @param annType gene annotation type. default = SYMBOL
#' #' @return a list with the enriched pathways for each cluster of genes
#' #'
#' #'
#' compute_pathways = function(geneList = rownames(res$Mat),corrType = "fdr",type_enrich="KEGG", annType = "SYMBOL",org_enrich = "hsapiens",pth = 0.05,sig = FALSE,mis = 0,only_annotated=FALSE ){
#' GList = list(matrix(geneList, ncol = 1))
#'
#' tryCatch(
#' EnrichDatList = {
#' GList = convert_genes(organism = org_enrich, GList=GList, annType = annType)
#'
#' if(corrType == "none"){
#' print("Nominal PValue")
#' EnrichDatList = lapply(GList,enrich,type_enrich,org_enrich,pth,"bonferroni", sig = sig, mis = mis, only_annotated=only_annotated)
#' for(i in 1:length(EnrichDatList)){
#' ERi = EnrichDatList[[i]]
#' ERi$pValueAdj = ERi$pValueAdj / length(ERi$pValueAdj)
#' ERi$pValue = ERi$pValueAdj / length(ERi$pValue)
#' EnrichDatList[[i]] = ERi
#' }
#' }else{
#' EnrichDatList = lapply(GList,enrich,type_enrich,org_enrich,pth,corrType, sig = sig, mis = mis, only_annotated=only_annotated)
#' }
#' EnrichDatList = EnrichDatList[[1]]
#' },
#' error = function(e){
#' EnrichDatList = matrix(data = "",nrow = 1,ncol = 5)
#' colnames(EnrichDatList) = c("annID","gID" ,"pValue","pValueAdj","Description")
#' }
#' )
#'
#' return(EnrichDatList)
#'
#' }
#'
#' #'
#' #' This function perform enchment of the genes in each cluster
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gProfileR gprofiler
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param x a dataframe with the gene names on the first column
#' #' @param adjust_method string specifing the algorithm used for determining the significance threshold, one of gSCS, fdr, bonferroni. Default: fdr
#' #' @param type string specifying the enrichment type. Default = KEGG
#' #' @param org string specifying the organism. Default = rnorvegicus
#' #' @param pval numeric value specifyint the pvalue threshold. Default = 0.05
#' #' @param sig whether all or only statistically significant results should be returned
#' #' @param mis minimum size of functional category, smaller categories are excluded
#' #' @param only_annotated statistical domain size, one of "annotated", "known"
#' #' @return a list with the enriched pathways for each cluster of genes
#' #'
#'
#' enrich = function(x, type, org, pval, adjust_method,sig = FALSE, mis = 0, only_annotated = TRUE){
#' if(only_annotated){
#' domain_size = "annotated"
#' }else{
#' domain_size = "known"
#' }
#'
#' print("before gprofiler")
#' out = gProfileR::gprofiler(query = as.character(x[,1]),src_filter=type,organism=org,domain_size = domain_size,
#' max_p_value = pval, correction_method = adjust_method,significant = sig,min_isect_size = mis)
#'
#' #out = gProfileR::gprofiler(query = x[,1],src_filter=type,organism=org,max_p_value = pval,domain_size = "known")
#' print(out)
#'
#' ##print("after gprofiler")
#' out=out[,c("term.id","intersection","p.value","p.value","term.name")]
#' colnames(out) = c( "annID","gID","pValue","pValueAdj","Description")
#'
#' print(out)
#'
#' if(nrow(out)>0)
#' out$annID= gsub("KEGG:|REAC:","",out$annID)
#'
#' return(out)
#' }
#'
#'
#' #'
#' #' This function convert genes identifiers
#' #'
#' #' @importFrom AnnotationDbi select
#' #' @importFrom gtools invalid
#' #' @import org.Hs.eg.db
#' #' @import org.Mm.eg.db
#' #' @import org.Rn.eg.db
#' #'
#' #' @param organism a string specifying the organism under analysis
#' #' @param GList a list of genes identifier
#' #' @param annType string specifying the wanted gene identifier
#' #' @return a list with the converted genes identifiers
#' #'
#' convert_genes = function(organism = "hsapiens", GList, annType = "SYMBOL"){
#'
#' orgLibs <- list("hsapiens"=org.Hs.eg.db, "mmusculus"=org.Mm.eg.db, "rnorvegicus" = org.Rn.eg.db)
#' orgDB <- orgLibs[[organism]]
#'
#' if(annType == "SYMBOL"){
#' tmp = GList[[1]]
#' tmp <- AnnotationDbi::select(orgDB, keys=as.character(tmp[,1]), columns="SYMBOL", keytype=annType)
#' return(GList)
#' }
#'
#' for(i in 1:length(GList)){
#' M=GList[[i]]
#' genes = M[,1]
#'
#' selectAnnDF <- AnnotationDbi::select(orgDB, keys=genes, columns="SYMBOL", keytype=annType)
#'
#' toRem = which(is.na(selectAnnDF[,2]))
#' if(length(toRem)>0){
#' selectAnnDF = selectAnnDF[-toRem,]
#' }
#'
#' #remove eventual duplicates
#' selectAnnDF = selectAnnDF[!duplicated(selectAnnDF$SYMBOL),]
#'
#' M = M[M[,1] %in% selectAnnDF[,1],]
#'
#' M = as.matrix(M)
#'
#' # macke sure they match by the key type
#' matches = match(selectAnnDF[,1],M[,1])
#'
#' #all(M$ID[matches] == selectAnnDF[,1])
#' M[matches,1] = selectAnnDF[,2]
#'
#' #update gene symbols
#' GList[[i]] = M
#'
#' }
#'
#' return(GList)
#' }
#'
#' #' This function create an excel file with the same format of the input need by the FunMappOne tool
#' #' @import xlsx
#' #'
#' #' @param optimal_clustering is a numeric vector with the clustering result for every gene
#' #' @param filePath is a string specifying the path of the xlsx file
#' #'
#'
#' write_xlsx_for_funmappone = function(optimal_clustering,filePath = "../contour_clustering/gene_clustering.xlsx"){
#' genes1 = names(optimal_clustering)[optimal_clustering == 1]
#' xlsx::write.xlsx(genes1, filePath, sheetName = "Cluster_1",row.names = FALSE)
#'
#' nClust = length(unique(optimal_clustering))
#'
#' for(i in 2:nClust){
#' genesi = names(optimal_clustering)[optimal_clustering == i]
#' xlsx::write.xlsx(genesi, filePath, sheetName = paste("Cluster_",i,sep=""),append = TRUE,row.names = FALSE)
#' }
#'
#' grouping = cbind(paste("Cluster_",1:nClust,sep=""), 1:nClust)
#' colnames(grouping) = c("cluster", "group")
#' xlsx::write.xlsx(grouping, filePath, sheetName = "grouping",append = TRUE, row.names = FALSE)
#'
#' }
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.