R: Computes a Very Large Genomic Relationship Matrix

getG_symDMatrix {BGData}

R Documentation

Computes a Very Large Genomic Relationship Matrix

Description

Computes a positive semi-definite symmetric genomic relation matrix G=XX' offering options for centering and scaling the columns of X beforehand.

Usage

getG_symDMatrix(X, center = TRUE, scale = TRUE, impute = TRUE, scaleG = TRUE,
  minVar = 1e-05, blockSize = 5000L,
  folderOut = paste0("symDMatrix_", randomString()), vmode = "double",
  i = seq_len(nrow(X)), j = seq_len(ncol(X)), chunkSize = 5000L,
  nCores = getOption("mc.cores", 2L), verbose = FALSE)

Arguments

`X`	A matrix-like object, typically the genotypes of a `BGData` object.
`center`	Either a logical value or a numeric vector of length equal to the number of columns of `X`. If `FALSE`, no centering is done. Defaults to `TRUE`.
`scale`	Either a logical value or a numeric vector of length equal to the number of columns of `X`. If `FALSE`, no scaling is done. Defaults to `TRUE`.
`impute`	Indicates whether missing values should be imputed. Defaults to `TRUE`.
`scaleG`	TRUE/FALSE whether xx' must be scaled.
`minVar`	Columns with variance lower than this value will not be used in the computation (only if `scale` is not `FALSE`).
`blockSize`	The number of rows and columns of each block. If `NULL`, a single block of the same length as `i` will be created. Defaults to 5000.
`folderOut`	The path to the folder where to save the `symDMatrix` object. Defaults to a random string prefixed with "symDMatrix_".
`vmode`	vmode of `ff` objects.
`i`	Indicates which rows of `X` should be used. Can be integer, boolean, or character. By default, all rows are used.
`j`	Indicates which columns of `X` should be used. Can be integer, boolean, or character. By default, all columns are used.
`chunkSize`	The number of columns of `X` that are brought into physical memory for processing per core. If `NULL`, all columns of `X` are used. Defaults to 5000.
`nCores`	The number of cores (passed to `mclapply`). Defaults to the number of cores as detected by `detectCores`.
`verbose`	Whether progress updates will be posted. Defaults to `FALSE`.

Details

Even very large genomic relationship matrices are supported by partitioning X into blocks and calling getG on these blocks. This function performs the block computations sequentially, which may be slow. In an HPC environment, performance can be improved by manually distributing these operations to different nodes.

Value