snp_pcadapt: Outlier detection

Description

Method to detect genetic markers involved in biological adaptation. This provides a statistical tool for outlier detection based on Principal Component Analysis. This corresponds to the statistic based on mahalanobis distance, as implemented in package pcadapt.

Usage

snp_pcadapt(
  G,
  U.row,
  ind.row = rows_along(G),
  ind.col = cols_along(G),
  ncores = 1
)
bed_pcadapt(
  obj.bed,
  U.row,
  ind.row = rows_along(obj.bed),
  ind.col = cols_along(obj.bed),
  ncores = 1
)

Value

An object of classes mhtest and data.frame returning one score by SNP. See methods(class = "mhtest").

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.
U.row: Left singular vectors (not scores, $U^T U = I$) corresponding to ind.row.
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.
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.

References

Luu, K., Bazin, E., & Blum, M. G. (2017). pcadapt: an R package to perform genome scans for selection based on principal component analysis. Molecular ecology resources, 17(1), 67-77.

Examples

Run this code

test <- snp_attachExtdata()
G <- test$genotypes
obj.svd <- big_SVD(G, fun.scaling = snp_scaleBinom(), k = 10)
plot(obj.svd) # there seems to be 3 "significant" components
pcadapt <- snp_pcadapt(G, obj.svd$u[, 1:3])
snp_qq(pcadapt)