# SUMMARY

# -------

# This file contains several functions for reading the QTL experiment

# data from text files. Here is an overview of the functions defined

# in this file:

#

#   read.pheno(file)

#   read.map(file)

#   read.geno.dosage(file,n)

#

# FUNCTION DEFINITIONS

# ----------------------------------------------------------------------

# Loads the phenotype data stored in a CSV file.

read.pheno <- function (file) {

  # Read in the phenotype data from the CSV file. 

  pheno <- read.csv(file,quote = "",header = TRUE,check.names = FALSE,

                    stringsAsFactors = FALSE,comment.char = "#")

  # Convert some of the columns to factors.

  pheno <- transform(pheno,

                     id            = as.character(id),

                     round         = factor(round,paste0("SW",1:25)),

                     FCbox         = factor(FCbox),

                     PPIbox        = factor(PPIbox),

                     methcage      = factor(methcage),

                     methcycle     = factor(methcycle),

                     discard       = factor(discard),

                     mixup         = factor(mixup),

                     earpunch      = factor(earpunch),

                     abnormalbone  = factor(abnormalbone),

                     experimenters = factor(experimenters))

  # Convert some of the columns to double precision.

  pheno <- transform(pheno,

                     fastglucose       = as.double(fastglucose),

                     D1totaldist0to15  = as.double(D1totaldist0to15),

                     D1totaldist15to30 = as.double(D1totaldist15to30),

                     D1totaldist0to30  = as.double(D1totaldist0to30),

                     D2totaldist0to15  = as.double(D2totaldist0to15),

                     D2totaldist15to30 = as.double(D2totaldist15to30),

                     D2totaldist0to30  = as.double(D2totaldist0to30),

                     D3totaldist0to15  = as.double(D3totaldist0to15),

                     D3totaldist15to30 = as.double(D3totaldist15to30),

                     D3totaldist0to30  = as.double(D3totaldist0to30),

                     D1TOTDIST5  = as.double(D1TOTDIST5),

                     D1TOTDIST10 = as.double(D1TOTDIST10),

                     D1TOTDIST15 = as.double(D1TOTDIST15),

                     D1TOTDIST20 = as.double(D1TOTDIST20),

                     D1TOTDIST25 = as.double(D1TOTDIST25),

                     D1TOTDIST30 = as.double(D1TOTDIST30),

                     D2TOTDIST5  = as.double(D2TOTDIST5),

                     D2TOTDIST10 = as.double(D2TOTDIST10),

                     D2TOTDIST15 = as.double(D2TOTDIST15),

                     D2TOTDIST20 = as.double(D2TOTDIST20),

                     D2TOTDIST25 = as.double(D2TOTDIST25),

                     D2TOTDIST30 = as.double(D2TOTDIST30),

                     D3TOTDIST5  = as.double(D3TOTDIST5),

                     D3TOTDIST10 = as.double(D3TOTDIST10),

                     D3TOTDIST15 = as.double(D3TOTDIST15),

                     D3TOTDIST20 = as.double(D3TOTDIST20),

                     D3TOTDIST25 = as.double(D3TOTDIST25),

                     D3TOTDIST30 = as.double(D3TOTDIST30))

  # Return the phenotype data table.

  return(pheno)

}

# ----------------------------------------------------------------------

# Returns a data frame containing the marker data stored in a text

# file. Here I convert the chromosomes and alleles to factors manually

# to make sure that the chromosomes and bases are ordered properly in

# the factors.

read.map <- function (file) {

  bases <- c("A","T","G","C")

  map   <- read.table(file,sep = " ",header = TRUE,comment.char = "#",

                 stringsAsFactors = FALSE)

  return(transform(map,

                   chr = factor(chr,1:19),

                   ref = factor(ref,bases),

                   alt = factor(alt,bases)))

}

# ----------------------------------------------------------------------

# Returns a list object containing (1) a vector "discard" specifying

# samples that are from potentially mislabeled flow samples, (2) an n

# x p matrix of genotype "dosages" (expected allele counts), where n

# is the number of samples and p is the number of SNPs.

read.geno.dosage <- function (file, n) {

  out <- fread(file,sep = " ",header = TRUE,showProgress = FALSE,

               colClasses = c("character","character",rep("double",n)))

  class(out)    <- "data.frame"

  rownames(out) <- out$id

  return(list(discard = factor(out$discard),

              geno    = as.matrix(out[-(1:2)])))

}