Fast Principal Component Analysis for Outlier Detection

Methods to detect genetic markers involved in biological adaptation. 'pcadapt' provides statistical tools for outlier detection based on Principal Component Analysis. Implements the method described in (Luu, 2016) and later revised in (Privé, 2020) .

pcadapt

R package {pcadapt} was developed to detect genetic markers involved in biological adaptation. This package uses statistical tools for outlier detection based on Principal Component Analysis (PCA).

A tutorial for {pcadapt} is available on pcadapt's website.

News: Our paper describing version 4 of pcadapt (which has been available for quite some time now) is out. Please cite the newest paper (see Refs below).

Installation

# From CRAN
install.packages("pcadapt")

# From GitHub
remotes::install_github("bcm-uga/pcadapt")

References

• Privé, F., Luu, K., Vilhjálmsson, B. J., & Blum, M. G.B. (2020). Performing highly efficient genome scans for local adaptation with R package pcadapt version 4. Molecular Biology and Evolution 37.7 (2020): 2153–2154.

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

• Duforet-Frebourg, N., Luu, K., Laval, G., Bazin, E., & Blum, M. G.B. (2015). Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data. Molecular biology and evolution, 33(4), 1082–1093.