Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.
The DESCRIPTION file:
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A typical analysis will begin by estimating allelic proportions using the
estprops function. This is done in a Bayesian framework and is the most computationally intensive part of the analysis (i.e., depending on the size of the data set, this might take a day or more). This function depends on
rjags, which means the user needs to install the stand-alone program
JAGS as well. Principal component analysis and discriminant analysis are then used to obtain cytotype assignment probabilities via the
estploidy function. This can be done with or without a training set of individuals with known ploidies.
Maintainer: Zachariah Gompert <email@example.com>
Gompert Z. and Mock K. (XXXX) Detection of individual ploidy levels with genotyping-by-sequencing (GBS) analysis. Molecular Ecology Resources, submitted.
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## load a simulated data set data(dat) ## Not run: ## obtain posterior estimates of allelic proportions; short chains are used for ## the example, we recommend increasing this to at least 1000 MCMC steps with a ## 500 step burnin props<-estprops(cov1=t(dat[]),cov2=t(dat[]),mcmc.steps=20,mcmc.burnin=5, mcmc.thin=2) ## calculate observed heterozygosity and depth of coverage from the allele count ## data hx<-apply(is.na(dat[]+dat[])==FALSE,1,mean) dx<-apply(dat[]+dat[],1,mean,na.rm=TRUE) ## run estploidy without using known ploidy data pl<-estploidy(alphas=props,het=hx,depth=dx,train=FALSE,pl=NA,set=NA,nclasses=2, ids=dat[],pcs=1:2) ## boxplots to visualize posterior assignment probabilities by true ploidy ## (which is known because these are simulated data) boxplot(pl$pp[,1] ~ dat[],ylab="assignment probability",xlab="ploidy") ## run estploidy with a training data set with known ploidy; the data set is ## split into 100 individuals with known ploidy and 100 that are used for ## inference truep<-dat[] trn<-sort(sample(1:200,100,replace=FALSE)) truep[-trn]<-NA plt<-estploidy(alphas=props,het=hx,depth=dx,train=TRUE,pl=truep,set=trn, nclasses=2,ids=dat[],pcs=1:2) ## boxplots to visualize posterior assignment probabilities for individuals that ## were not part of the training set by true ploidy (which is known because ## these are simulated data) boxplot(plt$pp[,1] ~ dat[][-trn],ylab="assignment probability",xlab="ploidy") ## End(Not run)
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