glPca: Principal Component Analysis for genlight objects

Description

These functions implement Principal Component Analysis (PCA) for massive SNP datasets stored as genlight object. This implementation has the advantage of never representing to complete data matrix, therefore making huge economies in terms of rapid access memory (RAM). When the parallel package is available, glPca uses multiple-core ressources for more efficient computations. glPca returns lists with the class glPca (see 'value'). Other functions are defined for objects of this class: - print: prints the content of a glPca object. - scatter: produces scatterplots of principal components, with a screeplot of eigenvalues as inset. - loadingplot: plots the loadings of the analysis for one given axis, using an adapted version of the generic function loadingplot.

Usage

glPca(x, center = TRUE, scale = FALSE, nf = NULL, loadings = TRUE, 
    alleleAsUnit = FALSE, useC = TRUE, parallel = require("parallel"),
  n.cores = NULL, returnDotProd=FALSE, matDotProd=NULL)
## S3 method for class 'glPca':
print(x, \dots)
## S3 method for class 'glPca':
scatter(x, xax = 1, yax = 2, posi = "bottomleft", bg = "white", 
    ratio = 0.3, label = rownames(x$scores), clabel = 1, xlim = NULL, 
    ylim = NULL, grid = TRUE, addaxes = TRUE, origin = c(0, 0), 
    include.origin = TRUE, sub = "", csub = 1, possub = "bottomleft", 
    cgrid = 1, pixmap = NULL, contour = NULL, area = NULL, ...)
## S3 method for class 'glPca':
loadingplot(x, at=NULL, threshold=NULL, axis=1,
    fac=NULL, byfac=FALSE, lab=rownames(x$loadings), cex.lab=0.7, cex.fac=1,
    lab.jitter=0, main="Loading plot", xlab="SNP positions",
    ylab="Contributions", srt = 90, adj = c(0, 0.5), ...)

Arguments

for glPca, a genlight object; for print, scatter, and loadingplot, a glPca object.

center

a logical indicating whether the numbers of alleles should be centered; defaults to TRUE

scale

a logical indicating whether the numbers of alleles should be scaled; defaults to FALSE

an integer indicating the number of principal components to be retained; if NULL, a screeplot of eigenvalues will be displayed and the user will be asked for a number of retained axes.

loadings

a logical indicating whether loadings of the alleles should be computed (TRUE, default), or not (FALSE). Vectors of loadings are not always useful, and can take a large amount of RAM when millions of SNPs are considered.

alleleAsUnit

a logical indicating whether alleles are considered as units (i.e., a diploid genotype equals two samples, a triploid, three, etc.) or whether individuals are considered as units of information.

useC

a logical indicating whether compiled C code should be used for faster computations; this option cannot be used alongside parallel option.

parallel

a logical indicating whether multiple cores -if available- should be used for the computations (TRUE, default), or not (FALSE); requires the package parallel to be installed (see details); this option cannot be used alongside useC

n.cores

if parallel is TRUE, the number of cores to be used in the computations; if NULL, then the maximum number of cores available on the computer is used.

returnDotProd

a logical indicating whether the matrix of dot products between individuals should be returned (TRUE) or not (FALSE, default).

matDotProd

an optional matrix of dot products between individuals, NULL by default. This option is used internally to speed up computation time when re-running the same PCA several times. Leave this argument as NULL unless you really know what you ar

...

further arguments to be passed to other functions.

xax,yax

integers specifying which principal components should be shown in x and y axes.

posi,bg,ratio

arguments used to customize the inset in scatterplots of glPca results. See add.scatter documentation in the ade4 package for more details.

label,clabel,xlim,ylim,grid,addaxes,origin,include.origin,sub,csub,possub,cgrid,pixmap,contour,area

arguments passed to s.class; see ?s.label for more information

an optional numeric vector giving the abscissa at which loadings are plotted. Useful when variates are SNPs with a known position in an alignement.

threshold

a threshold value above which values of x are identified. By default, this is the third quartile of x.

axis

an integer indicating the column of x to be plotted; used only if x is a matrix-like object.

fac

a factor defining groups of SNPs.

byfac

a logical stating whether loadings should be averaged by groups of SNPs, as defined by fac.

lab

a character vector giving the labels used to annotate values above the threshold.

cex.lab

a numeric value indicating the size of annotations.

cex.fac

a numeric value indicating the size of annotations for groups of observations.

lab.jitter

a numeric value indicating the factor of randomisation for the position of annotations. Set to 0 (by default) implies no randomisation.

main

the main title of the figure.

xlab

the title of the x axis.

ylab

the title of the y axis.

srt

rotation of the labels; see ?text.

adj

adjustment of the labels; see ?text.

Value

=== glPca objects === The class glPca is a list with the following components:
callthe matched call.
eiga numeric vector of eigenvalues.
scoresa matrix of principal components, containing the coordinates of each individual (in row) on each principal axis (in column).
loadings(optional) a matrix of loadings, containing the loadings of each SNP (in row) for each principal axis (in column).
- === other outputs === Other functions have different outputs: - scatter return the matched call. - loadingplot returns information about the most contributing SNPs (see loadingplot.default)

encoding

UTF-8

Details

=== Using multiple cores === Most recent machines have one or several processors with multiple cores. R processes usually use one single core. The package parallel allows for parallelizing some computations on multiple cores, which can decrease drastically computational time.

Lastly, note that using compiled C code (useC=TRUE)is an alternative for speeding up computations, but cannot be used together with the parallel option.

Examples

Run this code

## simulate a toy dataset
x <- glSim(50,4e3, 50, ploidy=2)
x
plot(x)

## perform PCA
pca1 <- glPca(x, nf=2)

## plot eigenvalues
barplot(pca1$eig, main="eigenvalues", col=heat.colors(length(pca1$eig)))

## basic plot
scatter(pca1, ratio=.2)

## plot showing groups
s.class(pca1$scores, pop(x), col=colors()[c(131,134)])
add.scatter.eig(pca1$eig,2,1,2)

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