R/makeNetwork.r

In Bohdan-Khomtchouk/geneXtendeR: Optimized Functional Annotation Of ChIP-seq Data

#' Makes gene-GO networks
#'
#' Creates dynamic and interactive networks of genes linked to their respective gene ontology terms for each category (biological process (BP), cellular compartment (CC), molecular function (MF)) of genes-under-peaks that are unique between two different upstream extension levels.
#'
#'
#' @param organism Object name assigned from readGFF() command.
#' @param start Lower bound of upstream extension.
#' @param end Upper bound of upstream extension.
#' @param GOcategory Either BP, CC, or MF.
#' @param GOspecies Either org.Ag.eg.db (mosquito), org.Bt.eg.db (bovine), org.Ce.eg.db (worm), org.Cf.eg.db (canine), org.Dm.eg.db (fly), org.Dr.eg.db (zebrafish), org.Gg.eg.db (chicken), org.Hs.eg.db (human), org.Mm.eg.db (mouse), org.Mmu.eg.db (rhesus), org.Pt.eg.db (chimpanzee), org.Rn.eg.db (rat), org.Sc.sgd.db (yeast), org.Ss.eg.db (pig), or org.Xl.eg.db (frog).  
#'
#' @return An interactive network of gene names linked to their respective gene ontology terms for either a BP, CC, or MF category.
#'
#' @examples
#' library(rtracklayer)
#' rat <- readGFF("ftp://ftp.ensembl.org/pub/release-84/gtf/rattus_norvegicus/Rattus_norvegicus.Rnor_6.0.84.gtf.gz")
#' fpath <- system.file("extdata", "somepeaksfile.txt", package="geneXtendeR")
#' peaksInput(fpath)
#' library(networkD3)
#' library(dplyr)
#' library(org.Rn.eg.db)
#' makeNetwork(rat, 0, 500, BP, org.Rn.eg.db)
#'
#' @useDynLib geneXtendeR, .registration = TRUE
#' 
#' 
#' @importFrom dplyr left_join select %>% rename id
#' @importFrom networkD3 forceNetwork JS
#'
#' @export
makeNetwork <- function(organism, start, end, GOcategory, GOspecies) {
  if(!file.exists("peaks.txt")){
    message("Please run peaksInput() function first!  See ?peaksInput for more information")
  } else {
    
    run2 <- function(f1, f2, peakslist) {
      .C("extractpeaks", f1, f2, peakslist)[[3]]
    }
    
    sapply(c(start, end), .geneXtender, organism, FALSE)
    twogxFiles <- sprintf("geneXtender_gtf_%s.bed", c(start, end))
    linelen <- ""  
    n <- 500000
    peaksArray <- rep(linelen, n)
    peaksArray2 <- rep(linelen, n)
    cmdtmp1 <- run2(f1 = "peaks.txt", f2 = twogxFiles[[1]], as.character(peaksArray))
    cmdtmp2 <- run2(f1 = "peaks.txt", f2 = twogxFiles[[2]], as.character(peaksArray2))
    cmd1 <- cmdtmp1[cmdtmp1 != linelen]
    cmd2 <- cmdtmp2[cmdtmp2 != linelen]
    m = regexec("^(?:[^\t]+\t){3}", cmd1)
    first3.cmd1 = unlist(regmatches(cmd1, m))
    m = regexec("^(?:[^\t]+\t){3}", cmd2)
    first3.cmd2 = unlist(regmatches(cmd2, m))
    finalList = cmd2[!(first3.cmd2 %in% first3.cmd1)]
    DT <- data.table::as.data.table(do.call("rbind", strsplit(finalList, split = "\t")))
    gene_names <- DT[[8]]
    
    if (deparse(substitute(GOcategory)) == 'BP') {
      gene_names_annotated <- AnnotationDbi::select(GOspecies, gene_names, "GO", "SYMBOL")
      gene_names_annotated_DT <- as.data.table(gene_names_annotated)
      Gene <- gene_names_annotated_DT[gene_names_annotated_DT$ONTOLOGY == 'BP']
      gene <- as.data.frame(Gene)
      terms <- AnnotationDbi::select(GO.db, as.character(gene[,2]), "TERM", "GOID")
      symbol_GOID_terms <- cbind(gene$SYMBOL, terms)
      uniq_symbol_GOID_terms <- unique(symbol_GOID_terms)
    } else if (deparse(substitute(GOcategory)) == 'CC') {
      gene_names_annotated <- AnnotationDbi::select(GOspecies, gene_names, "GO", "SYMBOL")
      gene_names_annotated_DT <- as.data.table(gene_names_annotated)
      Gene <- gene_names_annotated_DT[gene_names_annotated_DT$ONTOLOGY == 'CC']
      gene <- as.data.frame(Gene)
      terms <- AnnotationDbi::select(GO.db, as.character(gene[,2]), "TERM", "GOID")
      symbol_GOID_terms <- cbind(gene$SYMBOL, terms)
      uniq_symbol_GOID_terms <- unique(symbol_GOID_terms)
    } else if (deparse(substitute(GOcategory)) == 'MF') {
      gene_names_annotated <- AnnotationDbi::select(GOspecies, gene_names, "GO", "SYMBOL")
      gene_names_annotated_DT <- as.data.table(gene_names_annotated)
      Gene <- gene_names_annotated_DT[gene_names_annotated_DT$ONTOLOGY == 'MF']
      gene <- as.data.frame(Gene)
      terms <- AnnotationDbi::select(GO.db, as.character(gene[,2]), "TERM", "GOID")
      symbol_GOID_terms <- cbind(gene$SYMBOL, terms)
      uniq_symbol_GOID_terms <- unique(symbol_GOID_terms)
      
    } else {
      stop("Not a valid GO category.  Must be either BP, CC, or MF.")
    }
    
  
  
  src <- as.character(uniq_symbol_GOID_terms$`gene$SYMBOL`)
  target <- as.character(uniq_symbol_GOID_terms$TERM)
  networkData <- data.frame(src, target, stringsAsFactors = FALSE)
  nodes <- data.frame(name = unique(c(src, target)), stringsAsFactors = FALSE)
  nodes$id <- 0:(nrow(nodes) - 1)
  
  edges <- networkData %>%
    left_join(nodes, by = c("src" = "name")) %>%
    dplyr::select(-src) %>%
    rename(source = id) %>%
    left_join(nodes, by = c("target" = "name")) %>%
    dplyr::select(-target) %>%
    rename(target = id)
  
  edges$width <- 1
  nodes$group <- ifelse(nodes$name %in% src, "lions", "tigers")
  
  ColourScale <- 'd3.scaleOrdinal()
            .domain(["lions", "tigers"])
           .range(["#FF6900", "#694489"]);'
  
  forceNetwork(Links = edges, Nodes = nodes, 
               Source = "source",
               Target = "target",
               NodeID ="name",
               Group = "group",
               Value = "width",
               opacity = 0.9,
               zoom = TRUE,
               charge = -10,
               colourScale = JS(ColourScale))
  }
}