pca: Principal Components Analysis

Description Usage Arguments Value Author(s) Examples

Description

PCA from a HapAllele-based kinship matrix.

Usage

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ghap.pca(haplo, kinship, npc = 2)

Arguments

haplo

A GHap.haplo object.

kinship

A HapAllele-based kinship matrix, as supplied by ghap.kinship.

npc

Number of principal components to be retrieved (default = 2).

Value

The returned object is a list with items:

eigenvec

A data.frame containing the principal components of the kinship matrix.

eigenval

Vector with eigenvalues of the kinship matrix.

propvar

Vector with the proportion of variance explained by each principal component.

Author(s)

Yuri Tani Utsunomiya <ytutsunomiya@gmail.com>

Examples

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# #### DO NOT RUN IF NOT NECESSARY ###
# 
# # Copy the example data in the current working directory
# ghap.makefile()
# 
# # Load data
# phase <- ghap.loadphase("human.samples", "human.markers", "human.phase")
# 
# # Subset data - markers with maf > 0.05
# maf <- ghap.maf(phase, ncores = 2)
# markers <- phase$marker[maf > 0.05]
# phase <- ghap.subsetphase(phase, unique(phase$id), markers)
# 
# # Generate blocks of 5 markers sliding 5 markers at a time
# blocks.mkr <- ghap.blockgen(phase, windowsize = 5, slide = 5, unit = "marker")
#
# # Generate matrix of haplotype genotypes
# ghap.haplotyping(phase, blocks.mkr, batchsize = 100, ncores = 2, outfile = "human")
#
# # Load haplotype genotypes
# haplo <- ghap.loadhaplo("human.hapsamples", "human.hapalleles", "human.hapgenotypes")
# 
# # Exclude minor alleles and singletons
# hapstats <- ghap.hapstats(haplo, ncores = 2)
# haplo <- ghap.subsethaplo(haplo,ids=haplo$id,alleles = hapstats$TYPE %in% c("REGULAR","MAJOR"))
#
# # Compute Kinship matrix
# K <- ghap.kinship(haplo, batchsize = 100)
#
# 
# ### RUN ###
# 
# # PCA analysis
# pca <- ghap.pca(haplo,K)
# 
# # Plot
# plot(x=pca$eigenvec$PC1, y=pca$eigenvec$PC2, xlab="PC1", ylab="PC2", pch="")
# pop <- pca$eigenvec$POP
# pop.col <- as.numeric(as.factor(pop))
# pop <- sort(unique(pop))
# legend("bottomleft", legend = pop, col = 1:length(pop), pch = 1:length(pop), ncol = 3)
# points(x=pca$eigenvec$PC1, y=pca$eigenvec$PC2, pch = pop.col, col = pop.col, cex = 1.2)


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