qtl: Tools for analyzing QTL experiments

######################################################################
#
# read.cross.csv.R
#
# copyright (c) 2000-2012, Karl W Broman
# last modified Mar, 2012
# first written Aug, 2000
#
#     This program is free software; you can redistribute it and/or
#     modify it under the terms of the GNU General Public License,
#     version 3, as published by the Free Software Foundation.
# 
#     This program is distributed in the hope that it will be useful,
#     but without any warranty; without even the implied warranty of
#     merchantability or fitness for a particular purpose.  See the GNU
#     General Public License, version 3, for more details.
# 
#     A copy of the GNU General Public License, version 3, is available
#     at http://www.r-project.org/Licenses/GPL-3
#
# Part of the R/qtl package
# Contains: read.cross.csv 
#           [See read.cross.R for the main read.cross function.]
#
######################################################################

######################################################################
#
# read.cross.csv
#
# read data in comma-delimited format
#
######################################################################

read.cross.csv <-
function(dir, file, na.strings=c("-","NA"),
         genotypes=c("A","H","B","D","C"),
         estimate.map=TRUE, rotate=FALSE, ...)
{
  # create file names
  if(missing(file)) file <- "data.csv"

  if(!missing(dir) && dir != "") {
    file <- file.path(dir, file)
  }

  args <- list(...)

  if("" %in% na.strings) {
    na.strings <- na.strings[na.strings != ""]
    warning("Including \"\" in na.strings will cause problems; omitted.")
  }

  # if user wants to use comma for decimal point, we need
  if(length(args) > 0 && "dec" %in% names(args)) {
    dec <- args[["dec"]]
  }
  else dec <- "."

  # read the data file
  if(length(args) < 1 || !("sep" %in% names(args))) {
    # "sep" not in the "..." argument and so take sep=","
    if(length(args) < 1 || !("comment.char" %in% names(args)))
      data <- read.table(file, sep=",", na.strings=na.strings,
                         colClasses="character", fill=TRUE,
                         blank.lines.skip=TRUE, comment.char="", ...)
    else
      data <- read.table(file, sep=",", na.strings=na.strings,
                         colClasses="character", fill=TRUE,
                         blank.lines.skip=TRUE, ...)
  }
  else {
    if(length(args) < 1 || !("comment.char" %in% names(args)))
      data <- read.table(file, na.strings=na.strings,
                         colClasses="character", fill=TRUE,
                         blank.lines.skip=TRUE, comment.char="", ...)
    else
      data <- read.table(file, na.strings=na.strings,
                         colClasses="character", fill=TRUE,
                         blank.lines.skip=TRUE, ...)
  }

  if(rotate)
    data <- as.data.frame(t(data), stringsAsFactors=FALSE)

  empty <- grep("^\\s*$", data[2, ])

  if( ! 1 %in% empty)
    stop("You must include at least one phenotype (e.g., an index). ",
         "There was this value in the first column of the second row '",
         data[2,1],"' where was supposed to be nothing.",sep="")

  # determine number of phenotypes based on initial blanks in row 2
  if(length(empty)==ncol(data))
    stop("Second row has all blank cells; you need to include chromosome IDs for the markers.")
  n.phe <- min((1:ncol(data))[-empty])-1
  empty <- rep(FALSE, n.phe)
  empty[grep("^\\s*$", data[3,1:n.phe])] <- TRUE

  # Is map included?  yes if first n.phe columns in row 3 are all blank
  if(all(empty)) {
    map.included <- TRUE
    map <- asnumericwithdec(unlist(data[3,-(1:n.phe)]), dec=dec)
    if(any(is.na(map))) {
      temp <- unique(unlist(data[3,-(1:n.phe)])[is.na(map)])
      stop("There are missing marker positions.\n",
           "   In particular, we see these value(s): ",
           paste("\"",paste(temp,collapse="\",\"",sep=""),"\"",collapse=" ",sep=""),
           " at position(s): ",
           paste(which(is.na(map)),colapse=",",sep=""),sep="")
    }
    nondatrow <- 3
  }
  else {
    map.included <- FALSE
    map <- rep(0,ncol(data)-n.phe)
    nondatrow <- 2 # last non-data row
  }
  pheno <- as.data.frame(data[-(1:nondatrow),1:n.phe,drop=FALSE], stringsAsFactors=TRUE)
  colnames(pheno) <- data[1,1:n.phe]

  # replace empty cells with NA
  data <- sapply(data,function(a) { a[!is.na(a) & a==""] <- NA; a })

  # pull apart phenotypes, genotypes and map
  mnames <- data[1,-(1:n.phe)]
  if(any(is.na(mnames)))
        stop("There are missing marker names. Check column(s) ",paste(which(is.na(mnames))+1+n.phe,collapse=","),sep="")
  chr <- data[2,-(1:n.phe)]
  if(any(is.na(chr)))
        stop("There are missing chromosome IDs. Check column(s) ",paste(which(is.na(chr))+1+n.phe,collapse=","),sep="")

  if(length(genotypes) > 0) {
    # look for strange entries in the genotype data
    temp <- unique(as.character(data[-(1:nondatrow),-(1:n.phe),drop=FALSE]))

    temp <- temp[!is.na(temp)]
    wh <- !(temp %in% genotypes)
    if(any(wh)) {
      warn <- "The following unexpected genotype codes were treated as missing.\n    "
      ge <- paste("|", paste(temp[wh],collapse="|"),"|",sep="")
      warn <- paste(warn,ge,"\n",sep="")
      warning(warn)
    }

    # convert genotype data
    allgeno <- matrix(match(data[-(1:nondatrow),-(1:n.phe)],genotypes),
                      ncol=ncol(data)-n.phe)
  }
  else
    allgeno <- matrix(as.numeric(data[-(1:nondatrow),-(1:n.phe)]),
                      ncol=ncol(data)-n.phe)

  # Fix up phenotypes
  sw2numeric <-
    function(x, dec) {
      wh1 <- is.na(x)
      n <- sum(!is.na(x))
      y <- suppressWarnings(asnumericwithdec(as.character(x), dec))
      wh2 <- is.na(y)
      m <- sum(!is.na(y))
      if(n==m || (n-m) < 2 || (n-m) < n*0.05) {
        if(sum(!wh1 & wh2) > 0) {
          u <- unique(as.character(x[!wh1 & wh2]))
          if(length(u) > 1) {
            themessage <- paste("The phenotype values", paste("\"", u, "\"", sep="", collapse=" "))
                themessage <- paste(themessage, " were", sep="")
              }
              else {
                themessage <- paste("The phenotype value \"", u, "\" ", sep="")
                themessage <- paste(themessage, " was", sep="")
              }
              themessage <- paste(themessage, "interpreted as missing.")
              warning(themessage)

        }
        return(y)
      }
      else return(x)
    }
  pheno <- data.frame(lapply(pheno, sw2numeric, dec=dec), stringsAsFactors=TRUE)

  # re-order the markers by chr and position
  # try to figure out the chr labels
  if(all(chr %in% c(1:999,"X","x"))) { # 1...19 + X
    tempchr <- chr
    tempchr[chr=="X" | chr=="x"] <- 1000
    tempchr <- as.numeric(tempchr)
    if(map.included) neworder <- order(tempchr, map)
    else neworder <- order(tempchr)

    chr <- chr[neworder]
    map <- map[neworder]
    allgeno <- allgeno[,neworder,drop=FALSE]
    mnames <- mnames[neworder]
  }
  
  # fix up dummy map
  if(!map.included) {
    map <- split(rep(0,length(chr)),chr)[unique(chr)]
    map <- unlist(lapply(map,function(a) seq(0,length=length(a),by=5)))
    names(map) <- NULL
  }

  # fix up map information
  # number of chromosomes
  uchr <- unique(chr)
  n.chr <- length(uchr)
  geno <- vector("list",n.chr)
  names(geno) <- uchr
  min.mar <- 1
  allautogeno <- NULL  
  for(i in 1:n.chr) { # loop over chromosomes
    # create map
    temp.map <- map[chr==uchr[i]]
    names(temp.map) <- mnames[chr==uchr[i]]

    # pull out appropriate portion of genotype data
    data <- allgeno[,min.mar:(length(temp.map)+min.mar-1),drop=FALSE]
    min.mar <- min.mar + length(temp.map)
    colnames(data) <- names(temp.map)

    geno[[i]] <- list(data=data,map=temp.map)
    if(uchr[i] == "X" || uchr[i] == "x")
      class(geno[[i]]) <- "X"
    else {
      class(geno[[i]]) <- "A"
      if(is.null(allautogeno)) allautogeno <- data 
      else allautogeno <- cbind(allautogeno,data) 
    }
  }

  if(is.null(allautogeno)) allautogeno <- allgeno 

  # check that data dimensions match
  n.mar1 <- sapply(geno,function(a) ncol(a$data))
  n.mar2 <- sapply(geno,function(a) length(a$map))
  n.phe <- ncol(pheno)
  n.ind1 <- nrow(pheno)
  n.ind2 <- sapply(geno,function(a) nrow(a$data))
  if(any(n.ind1 != n.ind2)) {
    cat(n.ind1,n.ind2,"\n")
    stop("Number of individuals in genotypes and phenotypes do not match.")
  }
  if(any(n.mar1 != n.mar2)) {
    cat(n.mar1,n.mar2,"\n")
    stop("Numbers of markers in genotypes and marker names files do not match.")
  }

  # print some information about the amount of data read
  cat(" --Read the following data:\n")
  cat("\t",n.ind1," individuals\n")
  cat("\t",sum(n.mar1)," markers\n")
  cat("\t",n.phe," phenotypes\n")

  # determine map type: f2 or bc or 4way?
  if(all(is.na(allgeno))) warning("There is no genotype data!\n")
  if(all(is.na(allautogeno)) || max(allautogeno,na.rm=TRUE)<=2) type <- "bc"
  else if(max(allautogeno,na.rm=TRUE)<=5) type <- "f2"
  else type <- "4way"
  cross <- list(geno=geno,pheno=pheno)
  class(cross) <- c(type,"cross")

  # check that nothing is strange in the genotype data
  cross.type <- class(cross)[1]
  if(cross.type=="f2") max.gen <- 5
  else if(cross.type=="bc") max.gen <- 2
  else max.gen <- 14

  # check that markers are in proper order
  #     if not, fix up the order
  for(i in 1:n.chr) {
    if(any(diff(cross$geno[[i]]$map)<0)) {
      o <- order(cross$geno[[i]]$map)
      cross$geno[[i]]$map <- cross$geno[[i]]$map[o]
      cross$geno[[i]]$data <- cross$geno[[i]]$data[,o,drop=FALSE]
    }
  }

  # if 4-way cross, make the maps matrices
  if(type=="4way") {
    for(i in 1:n.chr) 
      cross$geno[[i]]$map <- rbind(cross$geno[[i]]$map, cross$geno[[i]]$map)
  }

  # estimate genetic map
  if(estimate.map && !map.included) estmap <- TRUE
  else estmap <- FALSE

  # return cross + indicator of whether to run est.map
  list(cross,estmap)
}

# end of read.cross.csv.R