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ibd-pca.R
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ibd-pca.R
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# Written 2013 by Peter Ralph and Graham Coop
#
# contact: petrel.harp@gmail.com
#
# To the extent possible under law, the author(s) have dedicated all copyright and related and neighboring rights to this software to the public domain worldwide. This software is distributed without any warranty.
#
# You should have received a copy of the CC0 Public Domain Dedication along with this software. If not, see <http://creativecommons.org/publicdomain/zero/1.0/>.
#
#
source("ibd-blocks-fns.R")
source("laplace-inversion-fns.R")
# Actual blocks and other metainformation
load("all-blocks-winnowed-fine.Rdata")
load("eda-data-fine.Rdata")
# make a variable which is country-pair
blocks$countrypair <- countrypairs[cbind(as.numeric(blocks$country1),as.numeric(blocks$country2))]
# and individual-pair
blocks$indivpair <- factor( paste( pmin(blocks$id1,blocks$id2), pmax(blocks$id1,blocks$id2), sep="-" ) )
####
# PCs of individual-by-individual sharing matrix
require(spam)
require(vegan)
# positions of means
long.lat <- with(indivinfo, cbind( tapply(long,COUNTRY_SELF,mean), tapply(lat,COUNTRY_SELF,mean)) )
na.zero <- function (x) { x[is.na(x)] <- 0; x }
tsample <- function (x,n) { if(length(x)==1 & n>0) { x } else { sample(x,min(length(x),n)) } }
get.pcs <- function (countrylist, rotate=NULL, rescale=!is.null(rotate), nsubsamp=1000, renorm="none", recenter=FALSE, eps=0) {
subsamp.indivs <- unlist( lapply( countrylist, function (country) tsample(indivinfo$SUBJID[indivinfo$COUNTRY_SELF==country], nsubsamp) ) )
subsamp.indivinfo <- droplevels( indivinfo[indivinfo$SUBJID%in%subsamp.indivs,] )
subsamp.indpairs <- subset( indpairs, (id1 %in% subsamp.indivinfo$SUBJID) & (id2 %in% subsamp.indivinfo$SUBJID) )
# long blocks
ind.by.ind <- with(subsamp.indpairs, list( values=nblocks1cM+nblocks5cM+nblocks10cM, i=factor(id1,levels=subsamp.indivinfo$SUBJID), j=factor(id2,levels=subsamp.indivinfo$SUBJID) ) )
if (renorm=="indiv") {
# Normalize by each individual's marginal amount of sharing
ind.marg <- with(ind.by.ind, na.zero(tapply( values, i, sum )) + na.zero(tapply( values, j, sum )) )
# geometric
ind.by.ind$values <- log(1+ with(ind.by.ind, values / sqrt( ind.marg[as.numeric(i)] * ind.marg[as.numeric(j)] ) ) )
# arithmetic
# ind.by.ind$values <- with(ind.by.ind, values / ( ind.marg[as.numeric(i)] + ind.marg[as.numeric(j)] ) )
} else if (renorm=="country") {
# Normalize so each country gets weight one (using eps to not upweight singles too much)
countrysizes <- pmax(eps, with( subsamp.indivinfo, nsamples[ COUNTRY_SELF ] ))
ind.by.ind$values <- with( ind.by.ind, values / sqrt( countrysizes[as.numeric(i)] * countrysizes[as.numeric(j)] ) )
}
ind.by.ind <- spam( x=ind.by.ind, nrow=nrow(subsamp.indivinfo), ncol=nrow(subsamp.indivinfo) )
ind.by.ind <- ind.by.ind + t(ind.by.ind)
if (recenter) {
# center the matrix
ind.by.ind <- ind.by.ind - apply(ind.by.ind,1,sum)[row(ind.by.ind)]/nrow(ind.by.ind) - apply(ind.by.ind,2,sum)[col(ind.by.ind)]/ncol(ind.by.ind) + sum(ind.by.ind)/prod(dim(ind.by.ind))
}
# dimnames(ind.by.ind) <- list( subsamp.indivinfo$SUBJID, subsamp.indivinfo$SUBJID )
# ibi.pca <- svd(exp(-ind.by.ind))
ibi.pca <- svd(ind.by.ind)
xy <- ibi.pca$v[,1:2]
mean.pca <- do.call( cbind, lapply( 1:10, function (k) tapply( ibi.pca$v[,k], subsamp.indivinfo$COUNTRY_SELF, mean ) ) )
long.lat <- with(subsamp.indivinfo, cbind( tapply(long,COUNTRY_SELF,mean), tapply(lat,COUNTRY_SELF,mean)) )
countries <- with(subsamp.indivinfo, levels(COUNTRY_SELF)[as.numeric(COUNTRY_SELF)] )
if (rescale) {
# match the means
# rotate <- procrustes( X=long.lat[rownames(mean.pca),], Y=mean.pca[,1:2], scale=TRUE )
# match the individuals
rotate <- procrustes( X=long.lat[countries,], Y=xy, scale=TRUE )
xy <- predict(rotate, newdata=xy)
mean.pca <- predict(rotate, mean.pca)
} else if (is.matrix(rotate)) {
xy <- (xy)%*%rotate
mean.pca <- (mean.pca)%*%rotate
} else { rotate=rotate }
return( list( xy=xy, countries=countries, mean.pca=mean.pca, rotate=rotate, v=ibi.pca$v[,1:10] ) )
}
# PCA of sharing matrix
# pcs <- get.pcs( setdiff(levels(indivinfo$COUNTRY_SELF),c("Yugoslavia","Albania","Spain","Portugal")), rescale=FALSE, rotate=matrix(c(0,-1,-1,0),nrow=2), renorm=TRUE, nsubsamp=10 )
# mean rates of sharing for countries within distance x
nearby <- function (x) sapply( names(countrycols), function(ccc) with( subset(poppairs, (country1==ccc | country2==ccc) & gdist <= x ), mean( (nblocks1cM+nblocks5cM+nblocks10cM)/npairs ) ) )
pclist <- lapply( list( all=levels(indivinfo$COUNTRY_SELF), noAL=setdiff(levels(indivinfo$COUNTRY_SELF),c("Albania","Kosovo")) ),
function (ccc) {
lapply( c(none="none",country="country"), function (renorm) { get.pcs( ccc, rescale=FALSE, renorm=renorm, nsubsamp=100, eps=0 ) } )
} )
# look at the first few pcs
npcs <- 3
pdf(file="tmp-pcs.pdf",width=2.5*choose(npcs,2),height=10,pointsize=10)
layout( matrix(1:(4*choose(npcs,2)),nrow=4,byrow=TRUE) )
par(mar=c(4,4,0,0))
for ( usethese in c("all","noAL") ) {
with( pclist[[usethese]][["country"]], {
for (i in 1:(npcs-1)) for (j in (i+1):npcs) {
plot( v[,j], v[,i], col=adjustcolor(countrycols[countries],.5), xlab=paste("PC",j), ylab=paste("PC",i), pch=20, cex=1.5 )
text( mean.pca[,j], mean.pca[,i], labels=countryabbrevs[rownames(mean.pca)], )
if (i==1 & j==2) textlab("topleft",usethese)
}
} )
with( pclist[[usethese]][["none"]], {
for (i in 1:(npcs-1)) for (j in (i+1):npcs) {
plot( v[,j], v[,i], col=adjustcolor(countrycols[countries],.5), xlab=paste("PC",j), ylab=paste("PC",i), pch=20, cex=1.5 )
text( mean.pca[,j], mean.pca[,i], labels=countryabbrevs[rownames(mean.pca)], )
if (i==1 & j==2) textlab("topleft",usethese)
}
} )
}
dev.off()
pdf(file="indiv-sharing-map-of-europe.pdf", width=7, height=3, pointsize=10)
layout( matrix(1:3,nrow=1) )
par(mar=c(2,2,1,0)+.1,mgp=c(.5,.5,.5))
with( pclist[["all"]][["country"]], {
i <- 1; j <- 2
plot( v[,j], v[,i], col=adjustcolor(countrycols[countries],.5), xlab=paste("PC",j), ylab=paste("PC",i), pch=20, cex=1.5, xaxt='n', yaxt='n' )
text( mean.pca[,j], mean.pca[,i], labels=countryabbrevs[rownames(mean.pca)], )
textlab( "topleft", "All countries" )
} )
with( pclist[["noAL"]][["country"]], {
i <- 1; j <- 2
plot( v[,j], v[,i], col=adjustcolor(countrycols[countries],.5), xlab=paste("PC*",j), ylab=paste("PC*",i), pch=20, cex=1.5, xaxt='n', yaxt='n' )
text( mean.pca[,j], mean.pca[,i], labels=countryabbrevs[rownames(mean.pca)], )
textlab("topleft", "without AL/KO" )
i <- 1; j <- 3
plot( v[,j], v[,i], col=adjustcolor(countrycols[countries],.5), xlab=paste("PC*",j), ylab=paste("PC*",i), pch=20, cex=1.5, xaxt='n', yaxt='n' )
text( mean.pca[,j], mean.pca[,i], labels=countryabbrevs[rownames(mean.pca)], )
textlab("topright", "without AL/KO" )
} )
# map of self rates
# xy <- euplot( nearby( 10 ), scale=6, lab="Self IBD" )
# text( xy, labels=countryabbrevs, cex=8/10 )
dev.off()