R/panev.network.R
In vpalombo/PANEV: PaNeV: an R package for a pathway-based network visualization
Defines functions
panev.network
Documented in
panev.network
#' @title Pathways Network Visualization (PANEV)
#' @description The function performs a pathway analysis taking into account both upstream and downstream dependent network of functional related genes, from 2 to \emph{n} degrees of interaction. This function generates \emph{n} '\emph{.txt}' files, one for each level of interaction, containing the candidate genes and the related pathways highlighted. Along with the tabular format results, the function gets also the diagram visualization of PANEV results, saved in an interactive '\emph{.html}' file.
#' @usage panev.network(in.file = NULL, out.file = "PANEV_gene", species = NULL,
#' FL = NULL, levels = 2)
#' @param in.file Name of input file (with extension) containing the gene list of interest. The file \bold{must} contain three columns labelled as \emph{'ensembl_gene_id'}, \emph{'entrezgene'} and \emph{'external_gene_name'}, respectively. The file \bold{must} rely in the working directory. The handy function \code{\link[PANEV]{panev.dataPreparation}} could be used to create a properly formatted input file from a single gene list.
#' @param out.file Name of the folder where the results will be stored and of the output diagram file (default = 'PANEV_gene').
#' @param species The code of your species of interest. The correct code can get among the list of those available in KEGG with the handy function \code{\link[PANEV]{panev.speciesCode}}.
#' @param FL A list of pathways of first level to investigate. The list of all available pathways can get with the \code{\link[PANEV]{panev.pathList}} function.
#' @param levels The number of levels of interactions (from \emph{1} to \emph{n}) investingated (default = 2).
#' @details For each level required for the investigation, a single \emph{.txt} file will be generated, containing the genes and the related pathways for a specific level of interaction. The function get also the diagram visualization of results. This function is based on the main \pkg{KEGGREST} package (\url{https://bioconductor.org/packages/release/bioc/html/KEGGREST.html}).
#' @return
#' \itemize{
#' \item{The script generates from \emph{1} to \emph{n} '\emph{txt}' files, (with \emph{n} equal to the levels of interaction required for the investigation).
#' Each file is named \emph{(n)}Lgenes, based on \emph{n} level analysed
#' and contains the genes falling inside the pathways of the specific degree of interaction investigated.
#' Each file contains five columns with \emph{ensembl gene}, \emph{entrez gene}, \emph{gene name}, \emph{path description} and \emph{path id}.
#' The files are stored in a folder named as \emph{PANEV_RESULTS_<out.name>}, created in the work directory.}
#' \item{A \emph{<out.file>.html} file with the diagram visualization of PANEV results. The file is stored in the same folder of \emph{.txt} files}
#' }
#' @author Valentino Palombo (\email{valentino.palombo@gmail.com})
#' @references Tenenbaum D (2017). KEGGREST: Client-side REST access to KEGG. R package version 1.16.1.
#' @references Thieurmel B (2016). visNetwork: Network Visualization using 'vis.js' Library. R package version 2.0.3. https://CRAN.R-project.org/package=visNetwork
#' @examples ##### EXAMPLES CODE #####
#' #Copy the example files in the current working directory
#' panev.example()
#'
#' #Parameters
#' in.file="data.txt"
#' out.file="example"
#' FL = c("path:map00061", "path:map00062", "path:map00071", "path:map00072")
#' levels = 2
#'
#' #Look for the specie code matching the search string
#' list <- panev.speciesCode(string = "bos")
#' species=as.character(list[1,2]) # bta
#'
#' # Run the PANEV function
#' panev.network(in.file = in.file,
#' out.file = out.file,
#' species = species,
#' FL = FL,
#' levels = levels)
#'
#Script: panev.network.R
#License: GPLv3 or later
#Modification date: 2019-10-15
#Written by: Valentino Palombo
#Contact: valentino.palombo@gmail.com
#Description: Run PANEV and get diagram visualization.
panev.network <- function(
in.file=NULL,
out.file="PANEV_gene",
species=NULL,
FL = NULL,
levels = 2,
physics = T
)
{
#import genelist
in.file.name <- paste(in.file)
if (file.exists(in.file.name)){
genelist <<- read.table(in.file.name, header=T, stringsAsFactors = F)
cat("Input file imported! \n")
cat("\n")
#check the genelist arguments
panev.checkGenelist(genelist = genelist, type="gene")
#delete gene(s) with no entrez gene id
genelist <- genelist[!is.na(genelist$entrezgene),]
#add esembl to no symbol genes
genelist <<- panev.addExternalGeneName(genelist = genelist)
}else{
cat("Input file not found. Please check! \n")
stop("Exit",call. = F)
}
#check the path arguments
panev.checkPath(species, FL)
cat("\n")
cat("\nPrerequisite check passed!\n\nPANEV is running ... \n")
cat(" Please wait... It could take a while depending on the number of pathways and levels required! \n")
#set the levels to investigate
level <- 1:levels
#download gene/path id table
panev.downloadGenePathTable(species = species)
#add gene/path relations to genelist
genelist$KEGGgenes <- paste(paste(species),":",genelist$entrezgene, sep="")
genelist <- merge(x = genelist, y = KEGGgenes, by.x = "KEGGgenes", by.y = "V1", all=F)
#download pathways description
panev.downloadPathDescription(species = species)
#create a folder for storing results
RES <- paste("PANEV_RESULTS_",out.file,sep="")
if (file.exists(RES)) {
RES <- paste(RES,"/",sep="")
wd <- getwd()
}else{
dir.create(RES)
RES <- paste(RES,"/",sep="")
wd <- getwd()
}
#prepare FL to query on KEGG
query <- as.data.frame(gsub("map", species, FL), stringsAsFactors = F)
colnames(query) <- "path_ID"
#prepare the storage object
nextlevel <- NULL
glFL <- as.data.frame(dplyr::filter(genelist, genelist$rn %in% query$path_ID), stringsAsFactors = F)
FLgenes <- as.data.frame(glFL, stringsAsFactors = F)
FLgenes$external_gene_name <- as.character(FLgenes$external_gene_name)
FLgenes$ensembl_gene_id <- as.character(FLgenes$ensembl_gene_id)
FLgenes <- data.frame(cbind(ensembl_gene_id = paste(FLgenes$ensembl_gene_id), entrezgene = paste(FLgenes$entrezgene), external_gene_name = paste(FLgenes$external_gene_name), rn = paste(FLgenes$rn)), stringsAsFactors = F)
FLgenes <- merge(FLgenes, KEGGpath, by.x = "rn", by.y = "rn")
FLgenes <- data.frame(cbind(paste(FLgenes$ensembl_gene_id), paste(FLgenes$entrezgene), paste(FLgenes$external_gene_name), paste(FLgenes$V1), paste(FLgenes$rn)), stringsAsFactors = F)
colnames(FLgenes) <- c("ensemblgene","entrezgene", "gene_name", "path_description","path_ID")
FLgenes[which(FLgenes$gene_name == ""), "gene_name"] <- FLgenes[which(FLgenes$gene_name == ""),"ensemblgene"]
FLgenes[which(FLgenes$gene_name == "NA"), "gene_name"] <- FLgenes[which(FLgenes$gene_name == "NA"),"ensemblgene"]
FLgenes[which(is.na(FLgenes$gene_name)), "gene_name"] <- FLgenes[which(is.na(FLgenes$gene_name)), "ensemblgene"]
exp <- paste(RES,"/1Lgenes.txt",sep="")
write.table(FLgenes, exp, sep="\t", row.names = F)
if (length(level)>1){
#path(s) excluded for further levels
exclude <- query
for (j in 2:length(level)){
for (k in 1:length(query[,1])){
out_nodes <- suppressMessages(data.frame(do.call(rbind, (XML::getNodeSet((XML::xmlParse(KEGGREST::keggGet(query[k,1], "kgml"))), "//entry[@type='map']", fun=XML::xmlToList)))))
lks <- as.data.frame(t(as.data.frame(out_nodes$.attrs)), stringsAsFactors = F)
lks <- unique(as.data.frame(paste(lks$name), stringsAsFactors = F))
colnames(lks) <- "path_ID"
lks <- as.data.frame(subset(lks, !(lks$path_ID %in% query$path_ID)), stringsAsFactors = F)
nextlevel <- rbind(nextlevel, lks)
}
nextlevel <- unique(nextlevel)
#grab only path with species specified
nextlevel <- nextlevel[grep(species, nextlevel$path_ID, ignore.case = TRUE), ]
#grab path different from previous level
nextlevel <- nextlevel[(!(nextlevel %in% exclude$path_ID))]
#path excluded in the next levels analysis
exclude <- rbind(exclude, data.frame(path_ID = nextlevel))
glNDI <- as.data.frame(dplyr::filter(genelist, genelist$rn %in% nextlevel), stringsAsFactors = F)
glNDI <- merge(glNDI, KEGGpath, by.x = "rn", by.y = "rn")
glNDI <- as.data.frame(cbind(paste(glNDI$ensembl_gene_id), glNDI$entrezgene, paste(glNDI$external_gene_name), glNDI$V1, glNDI$rn))
colnames(glNDI) <- c("ensemblgene","entrezgene", "gene_name", "path_description","path_ID")
glNDI[which(glNDI$gene_name == ""), "gene_name"] <- glNDI[which(glNDI$gene_name == ""),"ensemblgene"]
glNDI[which(glNDI$gene_name == "NA"), "gene_name"] <- glNDI[which(glNDI$gene_name == "NA"),"ensemblgene"]
glNDI[which(is.na(glNDI$gene_name)), "gene_name"] <- glNDI[which(is.na(glNDI$gene_name)), "ensemblgene"]
names <- paste(j,"Lgenes.txt", sep="")
exp <- paste(RES, names, sep="")
write.table(glNDI, exp, sep="\t", row.names = F)
query <- data.frame(path_ID = nextlevel, stringsAsFactors = F)
}
}
cat("\n")
cat("PANEV analysis completed and relative '.txt' files exported! \n")
#set wd where PANEV outputs were stored
setwd(RES)
#upload the output file(s) generated before
panev.uploadPANEVscriptoutput(levels=levels)
cat("\n")
cat("Preparing PANEV diagram visualization! \n")
cat(" Please wait... It could take a while depending on the number of pathways and levels required! \n")
#find the interactions
#create the species-specific pathway(s) list for querying the KEGG database
if (length(level)==1){
query <- as.data.frame(gsub("map", species, FL), stringsAsFactors = F)
colnames(query)="path_ID"
interactions <- NULL
for (k in 1:length(query[,1])){
out_nodes <- suppressMessages(data.frame(do.call(rbind, (XML::getNodeSet((XML::xmlParse(KEGGREST::keggGet(query[k,1], "kgml"))), "//entry[@type='map']", fun=XML::xmlToList)))))
lks <- as.data.frame(t(as.data.frame(out_nodes$.attrs)), stringsAsFactors = F)
lks <- unique(as.data.frame(paste(lks$name), stringsAsFactors = F))
colnames(lks) <- "path_ID"
lks <- as.data.frame(subset(lks, (lks$path_ID %in% query$path_ID)), stringsAsFactors = F)
lks$to <- lks$path_ID
lks$from <- query[k,1]
lks <- lks[grep(species, lks$to), ]
interactions <- rbind(interactions, lks)
}
interactions$path_ID <-NULL
interactions <- unique(interactions)
interactions <- interactions[interactions$from != interactions$to,]
dm <- NULL
dm <- rbind(dm, get(paste("the","1Lgenes",sep="")))
dm <- as.data.frame(cbind(dm$gene_name, dm$path_ID), row.names = F, stringsAsFactors = F)
colnames(dm) <- c("from", "to")
int <- as.data.frame(cbind(interactions$from, interactions$to), row.names = F)
colnames(int) <- c("from", "to")
dm <- as.data.frame(rbind(dm, int))
row.names(dm) <- NULL
#extract the interactions uncovered to generate the diagram label
label <- data.frame(name = unique(c(paste(dm$from), paste(dm$to))))
label$num <- c(1:length(label$name))
#create the attributes of diagram objects
label$type <- 1
add <- get(paste("the","1Lgenes",sep=""))
label$type <- ifelse(label$name %in% add$gene_name, paste("1Lgenes", sep=""), label$type)
label$type <- ifelse(label$name %in% add$path_ID, paste("1Lpath", sep=""), label$type)
label$shape <- 1
label$shape <- ifelse(label$name %in% add$gene_name, "circle", label$shape)
label$shape <- ifelse(label$shape == "1", "diamond", label$shape)
label$color <- "black"
label$type <- ifelse(label$type == "1", "path without results", label$type)
label$type <- ifelse(label$shape == "circle", "gene", label$type)
label <- label[order(label$type, decreasing = F),]
label <- label[order(label$shape, decreasing = F),]
#create the diagram legend
legenda <- data.frame(cbind(num = label$num, name = paste(label$name)), stringsAsFactors = F)
legenda <- merge(legenda, KEGGpath, by.x = "name", by.y = "rn", all=T)
legenda <- legenda[order(as.numeric(legenda$num)),]
legenda <- subset(legenda, (!is.na(legenda$num)))
legenda$name <- ifelse(is.na(legenda$V1), legenda$name, legenda$V1)
#create diagram nodes
ndf <- data.frame(
id = label$num,
label = paste(legenda$name),
shape = label$shape,
group = label$type)
#extract the interactions uncovered
for (i in 1:length(label$num)){
dm$from[paste(dm$from) == paste(label$name[i])] <- paste(label$num[i])
dm$to[paste(dm$to) == paste(label$name[i])] <- paste(label$num[i])
}
#create the diagram edges
edf <- data.frame(
from = dm$from,
to = dm$to)
#legend color
qual_col_pals = RColorBrewer::brewer.pal.info[RColorBrewer::brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(RColorBrewer::brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
#create the diagram and export a html file
graph <- visNetwork::visNetwork(ndf, edf, main = paste("PANEV visualization", out.file, sep=" "))
graph <- visNetwork::visGroups(graph, groupname = "gene", color = paste(col_vector[1]), shape="dot")
graph <- visNetwork::visGroups(graph, groupname = "path without results", color = paste(col_vector[3]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "1Lpath", color = paste(col_vector[2]), shape="diamond")
graph <- visNetwork::visOptions(graph, highlightNearest = TRUE, nodesIdSelection = TRUE)
graph <- visNetwork::visLegend(graph, useGroups = T)
if (physics == F){
graph <- visIgraphLayout(graph)
}
visNetwork::visSave(graph, file = paste(out.file,".html", sep=""))
}else{
query <- as.data.frame(gsub("map", species, FL), stringsAsFactors = F)
colnames(query)="path_ID"
#create store objects
interactions <- NULL
nextlevel <- NULL
exclude <- NULL
for (j in 2:length(level)){
exclude <- rbind(exclude, query)
colnames(exclude) = "path_ID"
exclude <- unique(exclude)
for (k in 1:length(query[,1])){
out_nodes <- suppressMessages(data.frame(do.call(rbind, (XML::getNodeSet((XML::xmlParse(KEGGREST::keggGet(query[k,1], "kgml"))), "//entry[@type='map']", fun=XML::xmlToList)))))
lks <- as.data.frame(t(as.data.frame(out_nodes$.attrs)), stringsAsFactors = F)
lks <- unique(as.data.frame(paste(lks$name), stringsAsFactors = F))
colnames(lks) <- "path_ID"
#lks <- as.data.frame(subset(lks, !(lks$path_ID %in% query$path_ID)), stringsAsFactors = F)
nextlevel <- rbind(nextlevel, lks)
lks$to <- lks$path_ID
if (length(lks$to)>0) {
lks$from <- query[k,1]
}else{
lks$from <- lks$path_ID
}
lks <- lks[grep(species, lks$to), ]
lks <- lks[lks$from != lks$to,]
interactions <- rbind(interactions, lks)
}
nextlevel <- unique(nextlevel)
#grab only path with species specified
nextlevel <- nextlevel[grep(species, nextlevel$path_ID, ignore.case = TRUE), ]
#grab path different from previous level
nextlevel <- nextlevel[(!(nextlevel %in% exclude$path_ID))]
#path excluded in the next levels analysis
exclude <- rbind(exclude, data.frame(path_ID = nextlevel))
#create new query
query <- data.frame(path_ID=nextlevel)
}
}
interactions$path_ID <-NULL
interactions <- unique(interactions)
interactions <- interactions[interactions$from != interactions$to,]
dm <- NULL
for (i in 1:length(1:levels)){
dm <- rbind(dm, get(paste("the",i,"Lgenes",sep="")))
}
dm <- as.data.frame(cbind(dm$gene_name, dm$path_ID), stringsAsFactors = F)
colnames(dm) <- c("from", "to")
int <- as.data.frame(cbind(interactions$from, interactions$to), row.names = F)
colnames(int) <- c("from", "to")
dm <- as.data.frame(rbind(dm, int))
#extract the interactions uncovered to generate the diagram label
label <- data.frame(name = unique(c(paste(dm$from), paste(dm$to))))
label$num <- c(1:length(label$name))
#create the attributes of diagram objects
label$type <- 1
for (k in length(1:levels):1){
add <- get(paste("the",k,"Lgenes",sep=""))
label$type <- ifelse(label$name %in% add$gene_name, paste(k, "Lgenes", sep=""), label$type)
label$type <- ifelse(label$name %in% add$path_ID, paste(k, "Lpath", sep=""), label$type)
}
label$shape <- 1
for (k in 1:length(1:levels)){
add <- get(paste("the",k,"Lgenes",sep=""))
label$shape <- ifelse(label$name %in% add$gene_name, "circle", label$shape)
}
label$shape <- ifelse(label$shape == "1", "diamond", label$shape)
label$color <- "black"
label$type <- ifelse(label$type == "1", "path without results", label$type)
label$type <- ifelse(label$shape == "circle", "gene", label$type)
label <- label[order(label$type, decreasing = F),]
label <- label[order(label$shape, decreasing = F),]
label <- label[order(label$num, decreasing = F),]
#create the diagram legend
legenda <- data.frame(cbind(num = label$num, name = paste(label$name)), stringsAsFactors = F)
legenda <- merge(legenda, KEGGpath, by.x = "name", by.y = "rn", all=T)
legenda <- legenda[order(as.numeric(legenda$num)),]
legenda <- subset(legenda, (!is.na(legenda$num)))
legenda$name <- ifelse(is.na(legenda$V1), legenda$name, legenda$V1)
#create diagram nodes
ndf <- data.frame(
id = legenda$num,
label = paste(legenda$name),
shape = label$shape,
group = label$type)
#extract the interactions uncovered
for (i in 1:length(label$num)){
dm$from[paste(dm$from) == paste(label$name[i])] <- paste(label$num[i])
dm$to[paste(dm$to) == paste(label$name[i])] <- paste(label$num[i])
}
#create the diagram edges
edf <- data.frame(
from = dm$from,
to = dm$to)
#legend color
qual_col_pals = RColorBrewer::brewer.pal.info[RColorBrewer::brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(RColorBrewer::brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
#create the diagram and export a html file
graph <- visNetwork::visNetwork(ndf, edf, main = paste("PANEV visualization", out.file, sep=" "))
graph <- visNetwork::visGroups(graph, groupname = "gene", color = paste(col_vector[1]), shape="dot")
graph <- visNetwork::visGroups(graph, groupname = "path without results", color = paste(col_vector[3]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "1Lpath", color = paste(col_vector[2]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "2Lpath", color = paste(col_vector[4]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "3Lpath", color = paste(col_vector[5]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "4Lpath", color = paste(col_vector[6]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "5Lpath", color = paste(col_vector[7]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "6Lpath", color = paste(col_vector[8]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "7Lpath", color = paste(col_vector[9]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "8Lpath", color = paste(col_vector[10]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "9Lpath", color = paste(col_vector[11]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "10Lpath", color = paste(col_vector[12]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "11Lpath", color = paste(col_vector[13]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "12Lpath", color = paste(col_vector[14]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "13Lpath", color = paste(col_vector[15]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "14Lpath", color = paste(col_vector[16]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "15Lpath", color = paste(col_vector[17]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "16Lpath", color = paste(col_vector[18]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "17Lpath", color = paste(col_vector[19]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "18Lpath", color = paste(col_vector[20]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "19Lpath", color = paste(col_vector[21]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "20Lpath", color = paste(col_vector[22]), shape="diamond")
graph <- visNetwork::visOptions(graph, highlightNearest = TRUE, nodesIdSelection = TRUE)
graph <- visNetwork::visLegend(graph, useGroups = T)
if (physics == F){
graph <- visIgraphLayout(graph)
}
visNetwork::visSave(graph, file = paste(out.file,".html", sep=""))
#clear the workspace
panev.cleanEnvir(KEGGpath)
panev.cleanEnvir(KEGGgenes)
panev.cleanEnvir(store)
setwd(wd)
cat("\n")
cat("Well done! Diagram visualization was created and exported. \n")
}
vpalombo/PANEV documentation built on June 13, 2022, 1:11 p.m.
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Defines functions panev.network
Documented in panev.network
#' @title Pathways Network Visualization (PANEV)
#' @description The function performs a pathway analysis taking into account both upstream and downstream dependent network of functional related genes, from 2 to \emph{n} degrees of interaction. This function generates \emph{n} '\emph{.txt}' files, one for each level of interaction, containing the candidate genes and the related pathways highlighted. Along with the tabular format results, the function gets also the diagram visualization of PANEV results, saved in an interactive '\emph{.html}' file.
#' @usage panev.network(in.file = NULL, out.file = "PANEV_gene", species = NULL,
#' FL = NULL, levels = 2)
#' @param in.file Name of input file (with extension) containing the gene list of interest. The file \bold{must} contain three columns labelled as \emph{'ensembl_gene_id'}, \emph{'entrezgene'} and \emph{'external_gene_name'}, respectively. The file \bold{must} rely in the working directory. The handy function \code{\link[PANEV]{panev.dataPreparation}} could be used to create a properly formatted input file from a single gene list.
#' @param out.file Name of the folder where the results will be stored and of the output diagram file (default = 'PANEV_gene').
#' @param species The code of your species of interest. The correct code can get among the list of those available in KEGG with the handy function \code{\link[PANEV]{panev.speciesCode}}.
#' @param FL A list of pathways of first level to investigate. The list of all available pathways can get with the \code{\link[PANEV]{panev.pathList}} function.
#' @param levels The number of levels of interactions (from \emph{1} to \emph{n}) investingated (default = 2).
#' @details For each level required for the investigation, a single \emph{.txt} file will be generated, containing the genes and the related pathways for a specific level of interaction. The function get also the diagram visualization of results. This function is based on the main \pkg{KEGGREST} package (\url{https://bioconductor.org/packages/release/bioc/html/KEGGREST.html}).
#' @return
#' \itemize{
#' \item{The script generates from \emph{1} to \emph{n} '\emph{txt}' files, (with \emph{n} equal to the levels of interaction required for the investigation).
#' Each file is named \emph{(n)}Lgenes, based on \emph{n} level analysed
#' and contains the genes falling inside the pathways of the specific degree of interaction investigated.
#' Each file contains five columns with \emph{ensembl gene}, \emph{entrez gene}, \emph{gene name}, \emph{path description} and \emph{path id}.
#' The files are stored in a folder named as \emph{PANEV_RESULTS_<out.name>}, created in the work directory.}
#' \item{A \emph{<out.file>.html} file with the diagram visualization of PANEV results. The file is stored in the same folder of \emph{.txt} files}
#' }
#' @author Valentino Palombo (\email{valentino.palombo@gmail.com})
#' @references Tenenbaum D (2017). KEGGREST: Client-side REST access to KEGG. R package version 1.16.1.
#' @references Thieurmel B (2016). visNetwork: Network Visualization using 'vis.js' Library. R package version 2.0.3. https://CRAN.R-project.org/package=visNetwork
#' @examples ##### EXAMPLES CODE #####
#' #Copy the example files in the current working directory
#' panev.example()
#'
#' #Parameters
#' in.file="data.txt"
#' out.file="example"
#' FL = c("path:map00061", "path:map00062", "path:map00071", "path:map00072")
#' levels = 2
#'
#' #Look for the specie code matching the search string
#' list <- panev.speciesCode(string = "bos")
#' species=as.character(list[1,2]) # bta
#'
#' # Run the PANEV function
#' panev.network(in.file = in.file,
#' out.file = out.file,
#' species = species,
#' FL = FL,
#' levels = levels)
#'
#Script: panev.network.R
#License: GPLv3 or later
#Modification date: 2019-10-15
#Written by: Valentino Palombo
#Contact: valentino.palombo@gmail.com
#Description: Run PANEV and get diagram visualization.
panev.network <- function(
in.file=NULL,
out.file="PANEV_gene",
species=NULL,
FL = NULL,
levels = 2,
physics = T
)
{
#import genelist
in.file.name <- paste(in.file)
if (file.exists(in.file.name)){
genelist <<- read.table(in.file.name, header=T, stringsAsFactors = F)
cat("Input file imported! \n")
cat("\n")
#check the genelist arguments
panev.checkGenelist(genelist = genelist, type="gene")
#delete gene(s) with no entrez gene id
genelist <- genelist[!is.na(genelist$entrezgene),]
#add esembl to no symbol genes
genelist <<- panev.addExternalGeneName(genelist = genelist)
}else{
cat("Input file not found. Please check! \n")
stop("Exit",call. = F)
}
#check the path arguments
panev.checkPath(species, FL)
cat("\n")
cat("\nPrerequisite check passed!\n\nPANEV is running ... \n")
cat(" Please wait... It could take a while depending on the number of pathways and levels required! \n")
#set the levels to investigate
level <- 1:levels
#download gene/path id table
panev.downloadGenePathTable(species = species)
#add gene/path relations to genelist
genelist$KEGGgenes <- paste(paste(species),":",genelist$entrezgene, sep="")
genelist <- merge(x = genelist, y = KEGGgenes, by.x = "KEGGgenes", by.y = "V1", all=F)
#download pathways description
panev.downloadPathDescription(species = species)
#create a folder for storing results
RES <- paste("PANEV_RESULTS_",out.file,sep="")
if (file.exists(RES)) {
RES <- paste(RES,"/",sep="")
wd <- getwd()
}else{
dir.create(RES)
RES <- paste(RES,"/",sep="")
wd <- getwd()
}
#prepare FL to query on KEGG
query <- as.data.frame(gsub("map", species, FL), stringsAsFactors = F)
colnames(query) <- "path_ID"
#prepare the storage object
nextlevel <- NULL
glFL <- as.data.frame(dplyr::filter(genelist, genelist$rn %in% query$path_ID), stringsAsFactors = F)
FLgenes <- as.data.frame(glFL, stringsAsFactors = F)
FLgenes$external_gene_name <- as.character(FLgenes$external_gene_name)
FLgenes$ensembl_gene_id <- as.character(FLgenes$ensembl_gene_id)
FLgenes <- data.frame(cbind(ensembl_gene_id = paste(FLgenes$ensembl_gene_id), entrezgene = paste(FLgenes$entrezgene), external_gene_name = paste(FLgenes$external_gene_name), rn = paste(FLgenes$rn)), stringsAsFactors = F)
FLgenes <- merge(FLgenes, KEGGpath, by.x = "rn", by.y = "rn")
FLgenes <- data.frame(cbind(paste(FLgenes$ensembl_gene_id), paste(FLgenes$entrezgene), paste(FLgenes$external_gene_name), paste(FLgenes$V1), paste(FLgenes$rn)), stringsAsFactors = F)
colnames(FLgenes) <- c("ensemblgene","entrezgene", "gene_name", "path_description","path_ID")
FLgenes[which(FLgenes$gene_name == ""), "gene_name"] <- FLgenes[which(FLgenes$gene_name == ""),"ensemblgene"]
FLgenes[which(FLgenes$gene_name == "NA"), "gene_name"] <- FLgenes[which(FLgenes$gene_name == "NA"),"ensemblgene"]
FLgenes[which(is.na(FLgenes$gene_name)), "gene_name"] <- FLgenes[which(is.na(FLgenes$gene_name)), "ensemblgene"]
exp <- paste(RES,"/1Lgenes.txt",sep="")
write.table(FLgenes, exp, sep="\t", row.names = F)
if (length(level)>1){
#path(s) excluded for further levels
exclude <- query
for (j in 2:length(level)){
for (k in 1:length(query[,1])){
out_nodes <- suppressMessages(data.frame(do.call(rbind, (XML::getNodeSet((XML::xmlParse(KEGGREST::keggGet(query[k,1], "kgml"))), "//entry[@type='map']", fun=XML::xmlToList)))))
lks <- as.data.frame(t(as.data.frame(out_nodes$.attrs)), stringsAsFactors = F)
lks <- unique(as.data.frame(paste(lks$name), stringsAsFactors = F))
colnames(lks) <- "path_ID"
lks <- as.data.frame(subset(lks, !(lks$path_ID %in% query$path_ID)), stringsAsFactors = F)
nextlevel <- rbind(nextlevel, lks)
}
nextlevel <- unique(nextlevel)
#grab only path with species specified
nextlevel <- nextlevel[grep(species, nextlevel$path_ID, ignore.case = TRUE), ]
#grab path different from previous level
nextlevel <- nextlevel[(!(nextlevel %in% exclude$path_ID))]
#path excluded in the next levels analysis
exclude <- rbind(exclude, data.frame(path_ID = nextlevel))
glNDI <- as.data.frame(dplyr::filter(genelist, genelist$rn %in% nextlevel), stringsAsFactors = F)
glNDI <- merge(glNDI, KEGGpath, by.x = "rn", by.y = "rn")
glNDI <- as.data.frame(cbind(paste(glNDI$ensembl_gene_id), glNDI$entrezgene, paste(glNDI$external_gene_name), glNDI$V1, glNDI$rn))
colnames(glNDI) <- c("ensemblgene","entrezgene", "gene_name", "path_description","path_ID")
glNDI[which(glNDI$gene_name == ""), "gene_name"] <- glNDI[which(glNDI$gene_name == ""),"ensemblgene"]
glNDI[which(glNDI$gene_name == "NA"), "gene_name"] <- glNDI[which(glNDI$gene_name == "NA"),"ensemblgene"]
glNDI[which(is.na(glNDI$gene_name)), "gene_name"] <- glNDI[which(is.na(glNDI$gene_name)), "ensemblgene"]
names <- paste(j,"Lgenes.txt", sep="")
exp <- paste(RES, names, sep="")
write.table(glNDI, exp, sep="\t", row.names = F)
query <- data.frame(path_ID = nextlevel, stringsAsFactors = F)
}
}
cat("\n")
cat("PANEV analysis completed and relative '.txt' files exported! \n")
#set wd where PANEV outputs were stored
setwd(RES)
#upload the output file(s) generated before
panev.uploadPANEVscriptoutput(levels=levels)
cat("\n")
cat("Preparing PANEV diagram visualization! \n")
cat(" Please wait... It could take a while depending on the number of pathways and levels required! \n")
#find the interactions
#create the species-specific pathway(s) list for querying the KEGG database
if (length(level)==1){
query <- as.data.frame(gsub("map", species, FL), stringsAsFactors = F)
colnames(query)="path_ID"
interactions <- NULL
for (k in 1:length(query[,1])){
out_nodes <- suppressMessages(data.frame(do.call(rbind, (XML::getNodeSet((XML::xmlParse(KEGGREST::keggGet(query[k,1], "kgml"))), "//entry[@type='map']", fun=XML::xmlToList)))))
lks <- as.data.frame(t(as.data.frame(out_nodes$.attrs)), stringsAsFactors = F)
lks <- unique(as.data.frame(paste(lks$name), stringsAsFactors = F))
colnames(lks) <- "path_ID"
lks <- as.data.frame(subset(lks, (lks$path_ID %in% query$path_ID)), stringsAsFactors = F)
lks$to <- lks$path_ID
lks$from <- query[k,1]
lks <- lks[grep(species, lks$to), ]
interactions <- rbind(interactions, lks)
}
interactions$path_ID <-NULL
interactions <- unique(interactions)
interactions <- interactions[interactions$from != interactions$to,]
dm <- NULL
dm <- rbind(dm, get(paste("the","1Lgenes",sep="")))
dm <- as.data.frame(cbind(dm$gene_name, dm$path_ID), row.names = F, stringsAsFactors = F)
colnames(dm) <- c("from", "to")
int <- as.data.frame(cbind(interactions$from, interactions$to), row.names = F)
colnames(int) <- c("from", "to")
dm <- as.data.frame(rbind(dm, int))
row.names(dm) <- NULL
#extract the interactions uncovered to generate the diagram label
label <- data.frame(name = unique(c(paste(dm$from), paste(dm$to))))
label$num <- c(1:length(label$name))
#create the attributes of diagram objects
label$type <- 1
add <- get(paste("the","1Lgenes",sep=""))
label$type <- ifelse(label$name %in% add$gene_name, paste("1Lgenes", sep=""), label$type)
label$type <- ifelse(label$name %in% add$path_ID, paste("1Lpath", sep=""), label$type)
label$shape <- 1
label$shape <- ifelse(label$name %in% add$gene_name, "circle", label$shape)
label$shape <- ifelse(label$shape == "1", "diamond", label$shape)
label$color <- "black"
label$type <- ifelse(label$type == "1", "path without results", label$type)
label$type <- ifelse(label$shape == "circle", "gene", label$type)
label <- label[order(label$type, decreasing = F),]
label <- label[order(label$shape, decreasing = F),]
#create the diagram legend
legenda <- data.frame(cbind(num = label$num, name = paste(label$name)), stringsAsFactors = F)
legenda <- merge(legenda, KEGGpath, by.x = "name", by.y = "rn", all=T)
legenda <- legenda[order(as.numeric(legenda$num)),]
legenda <- subset(legenda, (!is.na(legenda$num)))
legenda$name <- ifelse(is.na(legenda$V1), legenda$name, legenda$V1)
#create diagram nodes
ndf <- data.frame(
id = label$num,
label = paste(legenda$name),
shape = label$shape,
group = label$type)
#extract the interactions uncovered
for (i in 1:length(label$num)){
dm$from[paste(dm$from) == paste(label$name[i])] <- paste(label$num[i])
dm$to[paste(dm$to) == paste(label$name[i])] <- paste(label$num[i])
}
#create the diagram edges
edf <- data.frame(
from = dm$from,
to = dm$to)
#legend color
qual_col_pals = RColorBrewer::brewer.pal.info[RColorBrewer::brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(RColorBrewer::brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
#create the diagram and export a html file
graph <- visNetwork::visNetwork(ndf, edf, main = paste("PANEV visualization", out.file, sep=" "))
graph <- visNetwork::visGroups(graph, groupname = "gene", color = paste(col_vector[1]), shape="dot")
graph <- visNetwork::visGroups(graph, groupname = "path without results", color = paste(col_vector[3]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "1Lpath", color = paste(col_vector[2]), shape="diamond")
graph <- visNetwork::visOptions(graph, highlightNearest = TRUE, nodesIdSelection = TRUE)
graph <- visNetwork::visLegend(graph, useGroups = T)
if (physics == F){
graph <- visIgraphLayout(graph)
}
visNetwork::visSave(graph, file = paste(out.file,".html", sep=""))
}else{
query <- as.data.frame(gsub("map", species, FL), stringsAsFactors = F)
colnames(query)="path_ID"
#create store objects
interactions <- NULL
nextlevel <- NULL
exclude <- NULL
for (j in 2:length(level)){
exclude <- rbind(exclude, query)
colnames(exclude) = "path_ID"
exclude <- unique(exclude)
for (k in 1:length(query[,1])){
out_nodes <- suppressMessages(data.frame(do.call(rbind, (XML::getNodeSet((XML::xmlParse(KEGGREST::keggGet(query[k,1], "kgml"))), "//entry[@type='map']", fun=XML::xmlToList)))))
lks <- as.data.frame(t(as.data.frame(out_nodes$.attrs)), stringsAsFactors = F)
lks <- unique(as.data.frame(paste(lks$name), stringsAsFactors = F))
colnames(lks) <- "path_ID"
#lks <- as.data.frame(subset(lks, !(lks$path_ID %in% query$path_ID)), stringsAsFactors = F)
nextlevel <- rbind(nextlevel, lks)
lks$to <- lks$path_ID
if (length(lks$to)>0) {
lks$from <- query[k,1]
}else{
lks$from <- lks$path_ID
}
lks <- lks[grep(species, lks$to), ]
lks <- lks[lks$from != lks$to,]
interactions <- rbind(interactions, lks)
}
nextlevel <- unique(nextlevel)
#grab only path with species specified
nextlevel <- nextlevel[grep(species, nextlevel$path_ID, ignore.case = TRUE), ]
#grab path different from previous level
nextlevel <- nextlevel[(!(nextlevel %in% exclude$path_ID))]
#path excluded in the next levels analysis
exclude <- rbind(exclude, data.frame(path_ID = nextlevel))
#create new query
query <- data.frame(path_ID=nextlevel)
}
}
interactions$path_ID <-NULL
interactions <- unique(interactions)
interactions <- interactions[interactions$from != interactions$to,]
dm <- NULL
for (i in 1:length(1:levels)){
dm <- rbind(dm, get(paste("the",i,"Lgenes",sep="")))
}
dm <- as.data.frame(cbind(dm$gene_name, dm$path_ID), stringsAsFactors = F)
colnames(dm) <- c("from", "to")
int <- as.data.frame(cbind(interactions$from, interactions$to), row.names = F)
colnames(int) <- c("from", "to")
dm <- as.data.frame(rbind(dm, int))
#extract the interactions uncovered to generate the diagram label
label <- data.frame(name = unique(c(paste(dm$from), paste(dm$to))))
label$num <- c(1:length(label$name))
#create the attributes of diagram objects
label$type <- 1
for (k in length(1:levels):1){
add <- get(paste("the",k,"Lgenes",sep=""))
label$type <- ifelse(label$name %in% add$gene_name, paste(k, "Lgenes", sep=""), label$type)
label$type <- ifelse(label$name %in% add$path_ID, paste(k, "Lpath", sep=""), label$type)
}
label$shape <- 1
for (k in 1:length(1:levels)){
add <- get(paste("the",k,"Lgenes",sep=""))
label$shape <- ifelse(label$name %in% add$gene_name, "circle", label$shape)
}
label$shape <- ifelse(label$shape == "1", "diamond", label$shape)
label$color <- "black"
label$type <- ifelse(label$type == "1", "path without results", label$type)
label$type <- ifelse(label$shape == "circle", "gene", label$type)
label <- label[order(label$type, decreasing = F),]
label <- label[order(label$shape, decreasing = F),]
label <- label[order(label$num, decreasing = F),]
#create the diagram legend
legenda <- data.frame(cbind(num = label$num, name = paste(label$name)), stringsAsFactors = F)
legenda <- merge(legenda, KEGGpath, by.x = "name", by.y = "rn", all=T)
legenda <- legenda[order(as.numeric(legenda$num)),]
legenda <- subset(legenda, (!is.na(legenda$num)))
legenda$name <- ifelse(is.na(legenda$V1), legenda$name, legenda$V1)
#create diagram nodes
ndf <- data.frame(
id = legenda$num,
label = paste(legenda$name),
shape = label$shape,
group = label$type)
#extract the interactions uncovered
for (i in 1:length(label$num)){
dm$from[paste(dm$from) == paste(label$name[i])] <- paste(label$num[i])
dm$to[paste(dm$to) == paste(label$name[i])] <- paste(label$num[i])
}
#create the diagram edges
edf <- data.frame(
from = dm$from,
to = dm$to)
#legend color
qual_col_pals = RColorBrewer::brewer.pal.info[RColorBrewer::brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(RColorBrewer::brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
#create the diagram and export a html file
graph <- visNetwork::visNetwork(ndf, edf, main = paste("PANEV visualization", out.file, sep=" "))
graph <- visNetwork::visGroups(graph, groupname = "gene", color = paste(col_vector[1]), shape="dot")
graph <- visNetwork::visGroups(graph, groupname = "path without results", color = paste(col_vector[3]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "1Lpath", color = paste(col_vector[2]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "2Lpath", color = paste(col_vector[4]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "3Lpath", color = paste(col_vector[5]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "4Lpath", color = paste(col_vector[6]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "5Lpath", color = paste(col_vector[7]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "6Lpath", color = paste(col_vector[8]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "7Lpath", color = paste(col_vector[9]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "8Lpath", color = paste(col_vector[10]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "9Lpath", color = paste(col_vector[11]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "10Lpath", color = paste(col_vector[12]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "11Lpath", color = paste(col_vector[13]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "12Lpath", color = paste(col_vector[14]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "13Lpath", color = paste(col_vector[15]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "14Lpath", color = paste(col_vector[16]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "15Lpath", color = paste(col_vector[17]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "16Lpath", color = paste(col_vector[18]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "17Lpath", color = paste(col_vector[19]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "18Lpath", color = paste(col_vector[20]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "19Lpath", color = paste(col_vector[21]), shape="diamond")
graph <- visNetwork::visGroups(graph, groupname = "20Lpath", color = paste(col_vector[22]), shape="diamond")
graph <- visNetwork::visOptions(graph, highlightNearest = TRUE, nodesIdSelection = TRUE)
graph <- visNetwork::visLegend(graph, useGroups = T)
if (physics == F){
graph <- visIgraphLayout(graph)
}
visNetwork::visSave(graph, file = paste(out.file,".html", sep=""))
#clear the workspace
panev.cleanEnvir(KEGGpath)
panev.cleanEnvir(KEGGgenes)
panev.cleanEnvir(store)
setwd(wd)
cat("\n")
cat("Well done! Diagram visualization was created and exported. \n")
}
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