SNPRelate (version 1.6.4)

snpgdsPCASNPLoading: SNP loadings in principal component analysis

Description

To calculate the SNP loadings in Principal Component Analysis

Usage

snpgdsPCASNPLoading(pcaobj, gdsobj, num.thread=1L, verbose=TRUE)

Arguments

pcaobj

the snpgdsPCAClass object returned from the function snpgdsPCA

gdsobj

an object of class SNPGDSFileClass, a SNP GDS file

num.thread

the number of (CPU) cores used; if NA, detect the number of cores automatically

verbose

if TRUE, show information

Value

Return a snpgdsPCASNPLoading object, which is a list:

sample.id

the sample ids used in the analysis

snp.id

the SNP ids used in the analysis

eigenval

eigenvalues

snploading

the SNP loadings, or SNP eigenvectors

TraceXTX

the trace of the genetic covariance matrix

Bayesian

whether use bayerisan normalization

avefreq

the allele frequency used in snpgdsPCA

scale

internal parameter

Details

Calculate the SNP loadings (or SNP eigenvectors) from the principal component analysis conducted in snpgdsPCA.

References

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

Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 38, 904-909.

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, snpgdsPCASampLoading, snpgdsPCACorr

Examples

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

PCARV <- snpgdsPCA(genofile, eigen.cnt=8)
SnpLoad <- snpgdsPCASNPLoading(PCARV, genofile)

names(SnpLoad)
# [1] "sample.id"  "snp.id"     "eigenval"   "snploading" "TraceXTX"
# [6] "Bayesian"   "avefreq"    "scale"
dim(SnpLoad$snploading)
# [1]     8 8722

plot(SnpLoad$snploading[1,], type="h", ylab="PC 1")

# close the genotype file
snpgdsClose(genofile)