genDataPreprocess {Haplin}

Pre-processing of the genetic data

Description

This function prepares the data to be used in Haplin analysis

Usage

genDataPreprocess(
  data.in = stop("You have to give the object to preprocess!"),
  map.file,
  map.header = FALSE,
  design = "triad",
  file.out = "data_preprocessed",
  dir.out = ".",
  ncpu = 1,
  overwrite = NULL
)

Arguments

data.in

Input data, as loaded by genDataRead or genDataLoad.

map.file

Filename (with path if the file is not in current directory) of the .map file holding the SNP names, if available.

map.header

Logical: does the map.file contain a header in the first row? Default: FALSE.

design

The design used in the study - choose from:

• triad - (default), data includes genotypes of mother, father and child;

• cc - classical case-control;

• cc.triad - hybrid design: triads with cases and controls

.

file.out

The core name of the files that will contain the preprocessed data (character string); ready to load next time with genDataLoad function; default: "data_preprocessed".

dir.out

The directory that will contain the saved data; defaults to current working directory.

ncpu

The number of CPU cores to use - this speeds up the process for large datasets significantly. Default is 1 core, maximum is 1 less than the total number of cores available on a current machine (even if the number given by the user is more than that).

overwrite

Whether to overwrite the output files: if NULL (default), will prompt the user to give answer; set to TRUE, will automatically overwrite any existing files; and set to FALSE, will stop if the output files exist.

Value

A list object with three elements:

• cov.data - a data.frame with covariate data (if available in the input file)

• gen.data - a list with chunks of the genetic data; the data is divided column-wise, using 10,000 columns per chunk; each element of this list is a ff matrix

• aux - a list with meta-data and important parameters:

  • variables - tabulated information of the covariate data;

  • variables.nas - how many NA values per each column of covariate data;

  • alleles - all the possible alleles in each marker;

  • alleles.nas - how many NA values in each marker;

  • nrows.with.missing - how many rows contain any missing allele information;

  • which.rows.with.missing - vector of indices of rows with missing data (if any)

  .

Details

The .map file should contain at least two columns, where the second one contains SNP names. Any additional columns should be separated by a whitespace character, but will be ignored. The file should contain a header.

Examples

  # The argument 'overwrite' is set to TRUE!
  # First, read the data:
  examples.dir <- system.file( "extdata", package = "Haplin" )
  example.file <- file.path( examples.dir, "exmpl_data.ped" )
  ped.data.read <- genDataRead( example.file, file.out = "exmpl_ped_data", 
   dir.out = tempdir( check = TRUE ), format = "ped", overwrite = TRUE )
  ped.data.read
  # Take only part of the data (if needed)
  ped.data.part <- genDataGetPart( ped.data.read, design = "triad", markers = 10:12,
   dir.out = tempdir( check = TRUE ), file.out = "exmpl_ped_data_part", overwrite = TRUE )
  # Preprocess as "triad" data:
  ped.data.preproc <- genDataPreprocess( ped.data.part, design = "triad",
   dir.out = tempdir( check = TRUE ), file.out = "exmpl_data_preproc", overwrite = TRUE )
  ped.data.preproc