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.

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
)

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.

Value

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

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.

See also

Examples

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(snp_gc(pcadapt))