snp_autoSVD: Truncated SVD while limiting LD

In privefl/mypack: Analysis of Massive SNP Arrays

Truncated SVD while limiting LD

Description

Fast truncated SVD with initial pruning and that iteratively removes long-range LD regions. Some variants are removing due to the initial clumping, then more and more variants are removed at each iteration. You can access the indices of the remaining variants with ⁠attr(*, "subset")⁠. If some of the variants removed are contiguous, the regions are reported in ⁠attr(*, "lrldr")⁠.

Usage

snp_autoSVD(
  G,
  infos.chr,
  infos.pos = NULL,
  ind.row = rows_along(G),
  ind.col = cols_along(G),
  fun.scaling = snp_scaleBinom(),
  thr.r2 = 0.2,
  size = 100/thr.r2,
  k = 10,
  roll.size = 50,
  int.min.size = 20,
  alpha.tukey = 0.05,
  min.mac = 10,
  max.iter = 5,
  is.size.in.bp = NULL,
  ncores = 1,
  verbose = TRUE
)

bed_autoSVD(
  obj.bed,
  ind.row = rows_along(obj.bed),
  ind.col = cols_along(obj.bed),
  fun.scaling = bed_scaleBinom,
  thr.r2 = 0.2,
  size = 100/thr.r2,
  k = 10,
  roll.size = 50,
  int.min.size = 20,
  alpha.tukey = 0.05,
  min.mac = 10,
  max.iter = 5,
  ncores = 1,
  verbose = TRUE
)

Arguments

G	A FBM.code256 (typically ⁠<bigSNP>$genotypes⁠). You shouldn't have missing values. Also, remember to do quality control, e.g. some algorithms in this package won't work if you use SNPs with 0 MAF.
infos.chr	Vector of integers specifying each SNP's chromosome. Typically ⁠<bigSNP>$map$chromosome⁠.
infos.pos	Vector of integers specifying the physical position on a chromosome (in base pairs) of each SNP. Typically ⁠<bigSNP>$map$physical.pos⁠.
ind.row	An optional vector of the row indices (individuals) that are used. If not specified, all rows are used. Don't use negative indices.
ind.col	An optional vector of the column indices (SNPs) that are used. If not specified, all columns are used. Don't use negative indices.
fun.scaling	A function with parameters X (or obj.bed), ind.row and ind.col, and that returns a data.frame with ⁠$center⁠ and ⁠$scale⁠ for the columns corresponding to ind.col, to scale each of their elements such as followed: \frac{X_{i,j} - center_j}{scale_j}. Default uses binomial scaling. You can also provide your own center and scale by using as_scaling_fun().
thr.r2	Threshold over the squared correlation between two SNPs. Default is 0.2. Use NA if you want to skip the clumping step.
size	For one SNP, window size around this SNP to compute correlations. Default is 100 / thr.r2 for clumping (0.2 -> 500; 0.1 -> 1000; 0.5 -> 200). If not providing infos.pos (NULL, the default), this is a window in number of SNPs, otherwise it is a window in kb (genetic distance). I recommend that you provide the positions if available.
k	Number of singular vectors/values to compute. Default is 10. This algorithm should be used to compute a few singular vectors/values.
roll.size	Radius of rolling windows to smooth log-p-values. Default is 50.
int.min.size	Minimum number of consecutive outlier SNPs in order to be reported as long-range LD region. Default is 20.
alpha.tukey	Default is 0.1. The type-I error rate in outlier detection (that is further corrected for multiple testing).
min.mac	Minimum minor allele count (MAC) for variants to be included. Default is 10.
max.iter	Maximum number of iterations of outlier detection. Default is 5.
is.size.in.bp	Deprecated.
ncores	Number of cores used. Default doesn't use parallelism. You may use nb_cores().
verbose	Output some information on the iterations? Default is TRUE.
obj.bed	Object of type bed, which is the mapping of some bed file. Use obj.bed <- bed(bedfile) to get this object.

Details

If you don't have any information about SNPs, you can try using

• infos.chr = rep(1, ncol(G)),

• size = ncol(G) (if SNPs are not sorted),

• roll.size = 0 (if SNPs are not sorted).

Value

A named list (an S3 class "big_SVD") of

• d, the singular values,

• u, the left singular vectors,

• v, the right singular vectors,

• niter, the number of the iteration of the algorithm,

• nops, number of Matrix-Vector multiplications used,

• center, the centering vector,

• scale, the scaling vector.

Note that to obtain the Principal Components, you must use predict on the result. See examples.

Examples

ex <- snp_attachExtdata()
G <- ex$genotypes

obj.svd <- snp_autoSVD(G,
                       infos.chr = ex$map$chromosome,
                       infos.pos = ex$map$physical.position)

str(obj.svd)

privefl/mypack documentation built on April 20, 2024, 1:51 a.m.