snpgdsPCASampLoading: Project individuals onto existing principal component axes

In SNPRelate: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data

Description

To calculate the sample eigenvectors using the specified SNP loadings

Usage

snpgdsPCASampLoading(loadobj, gdsobj, sample.id=NULL, num.thread=1L,
    verbose=TRUE)

Arguments

loadobj	a snpgdsPCASNPLoadingClass or snpgdsEigMixSNPLoadingClass object returned from snpgdsPCASNPLoading
gdsobj	an object of class SNPGDSFileClass, a SNP GDS file
sample.id	a vector of sample id specifying selected samples; if NULL, all samples are used
num.thread	the number of CPU cores used
verbose	if TRUE, show information

Details

The sample.id are usually different from the samples used in the calculation of SNP loadings.

Value

Returns a snpgdsPCAClass object, and it is a list:

sample.id	the sample ids used in the analysis
snp.id	the SNP ids used in the analysis
eigenval	eigenvalues
eigenvect	eigenvactors, “# of samples” x “eigen.cnt”
TraceXTX	the trace of the genetic covariance matrix
Bayesian	whether use bayerisan normalization

Or returns a snpgdsEigMixClass object, and it is a list:

sample.id	the sample ids used in the analysis
snp.id	the SNP ids used in the analysis
eigenval	eigenvalues
eigenvect	eigenvactors, “# of samples” x “eigen.cnt”
afreq	allele frequencies

Author(s)

Xiuwen Zheng

References

Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genetics 2:e190.

Zhu, X., Li, S., Cooper, R. S., and Elston, R. C. (2008). A unified association analysis approach for family and unrelated samples correcting for stratification. Am J Hum Genet, 82(2), 352-365.

See Also

snpgdsPCA, snpgdsPCACorr, snpgdsPCASNPLoading

Examples

# open an example dataset (HapMap)
genofile <- snpgdsOpen(snpgdsExampleFileName())

sample.id <- read.gdsn(index.gdsn(genofile, "sample.id"))

# first PCA
pca <- snpgdsPCA(genofile, eigen.cnt=8)
snp_load <- snpgdsPCASNPLoading(pca, genofile)

# calculate sample eigenvectors from SNP loadings
samp_load <- snpgdsPCASampLoading(snp_load, genofile, sample.id=sample.id[1:100])

diff <- pca$eigenvect[1:100,] - samp_load$eigenvect
summary(c(diff))
# ~ ZERO


# combine eigenvectors
allpca <- list(
    sample.id = c(pca$sample.id, samp_load$sample.id),
    snp.id = pca$snp.id,
    eigenval = c(pca$eigenval, samp_load$eigenval),
    eigenvect = rbind(pca$eigenvect, samp_load$eigenvect),
    varprop = c(pca$varprop, samp_load$varprop),
    TraceXTX = pca$TraceXTX
)
class(allpca) <- "snpgdsPCAClass"
allpca


# close the genotype file
snpgdsClose(genofile)

SNPRelate documentation built on Nov. 8, 2020, 5:31 p.m.