Correlation matrix — snp

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

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 bigstatsr::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]
#> 10 x 10 sparse Matrix of class "dsCMatrix"
#>                                                                      
#>  [1,]  1.000000000 -0.059420393  0.005664354 -0.024489153  0.07467733
#>  [2,] -0.059420393  1.000000000  0.011193114  0.069041892  0.01201638
#>  [3,]  0.005664354  0.011193114  1.000000000 -0.102606763  0.03146612
#>  [4,] -0.024489153  0.069041892 -0.102606763  1.000000000  0.06581927
#>  [5,]  0.074677326  0.012016376  0.031466118  0.065819274  1.00000000
#>  [6,] -0.016720892  0.006950400  0.075573785 -0.009996247  0.01995194
#>  [7,]  0.003680272 -0.073816948  0.015793311  0.067027181  0.03088233
#>  [8,] -0.017764606 -0.033245425 -0.087165386  0.052163271 -0.04179906
#>  [9,]  0.111349765  0.008661176 -0.060211948  0.062986807 -0.03244854
#> [10,] -0.051804449 -0.065532091  0.017048961 -0.093032007 -0.01146874
#>                                                                      
#>  [1,] -0.016720892  0.003680272 -0.01776461  0.111349765 -0.051804449
#>  [2,]  0.006950400 -0.073816948 -0.03324542  0.008661176 -0.065532091
#>  [3,]  0.075573785  0.015793311 -0.08716539 -0.060211948  0.017048961
#>  [4,] -0.009996247  0.067027181  0.05216327  0.062986807 -0.093032007
#>  [5,]  0.019951940  0.030882334 -0.04179906 -0.032448541 -0.011468736
#>  [6,]  1.000000000  0.053884169 -0.04024761 -0.045578013  0.052820407
#>  [7,]  0.053884169  1.000000000 -0.06369954 -0.012107321  0.006552636
#>  [8,] -0.040247606 -0.063699540  1.00000000 -0.022351147 -0.058557214
#>  [9,] -0.045578013 -0.012107321 -0.02235115  1.000000000 -0.041503959
#> [10,]  0.052820407  0.006552636 -0.05855721 -0.041503959  1.000000000

# Sparsity
length(corr@x) / length(corr)
#> [1] 0.375676