snp_cor: Correlation matrix

In bigsnpr: Analysis of Massive SNP Arrays

Correlation matrix

Description

Get significant (Pearson) correlations between nearby SNPs of the same chromosome (p-values are computed using a two-sided t-test).

Usage

snp_cor(
  Gna,
  ind.row = rows_along(Gna),
  ind.col = cols_along(Gna),
  size = 500,
  alpha = 1,
  thr_r2 = 0,
  fill.diag = TRUE,
  infos.pos = NULL,
  ncores = 1
)

bed_cor(
  obj.bed,
  ind.row = rows_along(obj.bed),
  ind.col = cols_along(obj.bed),
  size = 500,
  alpha = 1,
  thr_r2 = 0,
  fill.diag = TRUE,
  infos.pos = NULL,
  ncores = 1
)

Arguments

Gna	A FBM.code256 (typically ⁠<bigSNP>$genotypes⁠). You can have missing values in these data.
ind.row	An optional vector of the row indices (individuals) that are used. If not specified, all rows are used. Don't use negative indices.
ind.col	An optional vector of the column indices (SNPs) that are used. If not specified, all columns are used. Don't use negative indices.
size	For one SNP, window size around this SNP to compute correlations. Default is 500. If not providing infos.pos (NULL, the default), this is a window in number of SNPs, otherwise it is a window in kb (genetic distance).
alpha	Type-I error for testing correlations. Default is 1 (no threshold is applied).
thr_r2	Threshold to apply on squared correlations. Default is 0.
fill.diag	Whether to fill the diagonal with 1s (the default) or to keep it as 0s.
infos.pos	Vector of integers specifying the physical position on a chromosome (in base pairs) of each SNP. Typically ⁠<bigSNP>$map$physical.pos⁠.
ncores	Number of cores used. Default doesn't use parallelism. You may use nb_cores.
obj.bed	Object of type bed, which is the mapping of some bed file. Use obj.bed <- bed(bedfile) to get this object.

Value

The (Pearson) correlation matrix. This is a sparse symmetric matrix.

Examples

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

corr <- snp_cor(G, ind.col = 1:1000)
corr[1:10, 1:10]

# Sparsity
length(corr@x) / length(corr)

bigsnpr documentation built on March 31, 2023, 10:37 p.m.