Principle Component Analysis (PCA)
- Star Yanxin Gao
Created by Star Yanxin Gao, last modified by Xuecai Zhang on Jul 03, 2018
PLINK 1.9 provides two dimension reduction routines: --pca, for principal components analysis (PCA) based on the variance-standardized relationship matrix, and --mds-plot, for multidimensional scaling (MDS) based on raw Hamming distances. Top principal components are generally used as covariates in association analysis regressions to help correct for population stratification, while MDS coordinates help with visualizing genetic distances.
By default, --pca extracts the top 20 principal components of the variance-standardized relationship matrix; you can change the number by passing a numeric parameter. Eigenvectors are written to plink.eigenvec, and top eigenvalues are written to plink.eigenval. The 'header' modifier adds a header line to the .eigenvec file(s), and the 'tabs' modifier makes the .eigenvec file(s) tab- instead of space-delimited. You can request variant weights with the 'var-wts' modifier, and dump the matrix by using --pca in combination with --make-rel/--make-grm-gz/--make-grm-bin.