This function computes the IBS matrix.
Usage
snp_ibs(
X,
ind.row = bigstatsr::rows_along(X),
ind.col = bigstatsr::cols_along(X),
type = c("proportion", "adjusted_counts", "raw_counts"),
block.size = bigstatsr::block_size(nrow(X))
)Arguments
- X
a bigstatsr::FBM.code256 matrix (as found in the
genotypesslot of a bigsnpr::bigSNP object).- ind.row
An optional vector of the row indices that are used. If not specified, all rows are used. Don't use negative indices.
- ind.col
An optional vector of the column indices that are used. If not specified, all columns are used. Don't use negative indices.
- type
one of "proportion" (equivalent to "ibs" in PLINK), "adjusted_counts" ("distance" in PLINK), and "raw_counts" (the counts of identical alleles and non-missing alleles, from which the two other quantities are computed)
- block.size
maximum number of columns read at once. Note that, to optimise the speed of matrix operations, we have to store in memory 3 times the columns.
Value
if as.counts = TRUE function returns a list of two bigstatsr::FBM matrices, one of counts of IBS by alleles (i.e. 2*n loci), and one of valid alleles (i.e. 2 * n_loci - 2 * missing_loci). If as.counts = FALSE returns a single matrix of IBS proportions.
Details
Note that monomorphic sites are currently counted. Should we filter them beforehand? What does plink do?
Examples
example_gt <- load_example_gt("gen_tbl")
X <- attr(example_gt$genotypes, "bigsnp")
snp_ibs(X$genotypes)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 1.0000000 0.80 0.700 0.8333333 0.7500000 0.60 0.700
#> [2,] 0.8000000 1.00 0.750 0.8000000 0.5000000 0.50 0.500
#> [3,] 0.7000000 0.75 1.000 0.6000000 0.7000000 0.75 0.625
#> [4,] 0.8333333 0.80 0.600 1.0000000 0.5833333 0.70 0.500
#> [5,] 0.7500000 0.50 0.700 0.5833333 1.0000000 0.60 0.500
#> [6,] 0.6000000 0.50 0.750 0.7000000 0.6000000 1.00 0.750
#> [7,] 0.7000000 0.50 0.625 0.5000000 0.5000000 0.75 1.000
# Compute for individuals 1 to 5
snp_ibs(X$genotypes, ind.row = 1:5, ind.col = 1:5)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1.00 0.7500000 0.7500000 0.800 0.800
#> [2,] 0.75 1.0000000 0.8333333 0.750 0.500
#> [3,] 0.75 0.8333333 1.0000000 0.625 0.625
#> [4,] 0.80 0.7500000 0.6250000 1.000 0.600
#> [5,] 0.80 0.5000000 0.6250000 0.600 1.000
# Adjust block.size
snp_ibs(X$genotypes, block.size = 2)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 1.0000000 0.80 0.700 0.8333333 0.7500000 0.60 0.700
#> [2,] 0.8000000 1.00 0.750 0.8000000 0.5000000 0.50 0.500
#> [3,] 0.7000000 0.75 1.000 0.6000000 0.7000000 0.75 0.625
#> [4,] 0.8333333 0.80 0.600 1.0000000 0.5833333 0.70 0.500
#> [5,] 0.7500000 0.50 0.700 0.5833333 1.0000000 0.60 0.500
#> [6,] 0.6000000 0.50 0.750 0.7000000 0.6000000 1.00 0.750
#> [7,] 0.7000000 0.50 0.625 0.5000000 0.5000000 0.75 1.000
# Change type
snp_ibs(X$genotypes, type = "proportion")
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 1.0000000 0.80 0.700 0.8333333 0.7500000 0.60 0.700
#> [2,] 0.8000000 1.00 0.750 0.8000000 0.5000000 0.50 0.500
#> [3,] 0.7000000 0.75 1.000 0.6000000 0.7000000 0.75 0.625
#> [4,] 0.8333333 0.80 0.600 1.0000000 0.5833333 0.70 0.500
#> [5,] 0.7500000 0.50 0.700 0.5833333 1.0000000 0.60 0.500
#> [6,] 0.6000000 0.50 0.750 0.7000000 0.6000000 1.00 0.750
#> [7,] 0.7000000 0.50 0.625 0.5000000 0.5000000 0.75 1.000
snp_ibs(X$genotypes, type = "adjusted_counts")
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 6.0 4.8 4.20 5.0 4.5 3.6 4.20
#> [2,] 4.8 6.0 4.50 4.8 3.0 3.0 3.00
#> [3,] 4.2 4.5 6.00 3.6 4.2 4.5 3.75
#> [4,] 5.0 4.8 3.60 6.0 3.5 4.2 3.00
#> [5,] 4.5 3.0 4.20 3.5 6.0 3.6 3.00
#> [6,] 3.6 3.0 4.50 4.2 3.6 6.0 4.50
#> [7,] 4.2 3.0 3.75 3.0 3.0 4.5 6.00
snp_ibs(X$genotypes, type = "raw_counts")
#> $ibs
#> A Filebacked Big Matrix of type 'double' with 7 rows and 7 columns.
#>
#> $valid_n
#> A Filebacked Big Matrix of type 'double' with 7 rows and 7 columns.
#>