Post by Jian Yang on Dec 15, 2016 0:17:18 GMT
Principal component analysis
--pca 20
Input the GRM and output the first n (n = 20, by default) eigenvectors (saved as *.eigenvec, plain text file) and all the eigenvalues (saved as *.eigenval, plain text file), which are equivalent to those calculated by the program EIGENSTRAT. The only purpose of this option is to calculate the first m eigenvectors, and subsequently include them as covariates in the model when estimating the variance explained by all the SNPs (see below for the option of estimating the variance explained by genome-wide SNPs). Please find the EIGENSTRAT software if you need more sophisticated principal component analysis of the population structure.
Output file format
test.eigenval (no header line; the first m eigenvalues)
test.eigenvec (no header line; the first m eigenvectors; columns are family ID, individual ID and the first m eigenvectors)
Examples
# Input the GRM file and output the first 20 eigenvectors for a subset of individuals
--pca 20
Input the GRM and output the first n (n = 20, by default) eigenvectors (saved as *.eigenvec, plain text file) and all the eigenvalues (saved as *.eigenval, plain text file), which are equivalent to those calculated by the program EIGENSTRAT. The only purpose of this option is to calculate the first m eigenvectors, and subsequently include them as covariates in the model when estimating the variance explained by all the SNPs (see below for the option of estimating the variance explained by genome-wide SNPs). Please find the EIGENSTRAT software if you need more sophisticated principal component analysis of the population structure.
Output file format
test.eigenval (no header line; the first m eigenvalues)
20.436
7.1293
6.7267
......test.eigenvec (no header line; the first m eigenvectors; columns are family ID, individual ID and the first m eigenvectors)
011 0101 0.00466824 -0.000947 0.00467529 -0.00923534
012 0102 0.00139304 -0.00686406 -0.0129945 0.00681755
013 0103 0.00457615 -0.00287646 0.00420995 -0.0169046
......Examples
# Input the GRM file and output the first 20 eigenvectors for a subset of individuals
gcta64 --grm test --keep test.indi.list --pca 20 --out test