gbs2ploidy (version 1.0)

gbs2ploidy-package: \Sexpr[results=rd,stage=build]{tools:::Rd_package_title("#1")}gbs2ploidyInference of Ploidy from (Genotyping-by-Sequencing) GBS Data

Description

\Sexpr[results=rd,stage=build]{tools:::Rd_package_description("#1")}gbs2ploidyFunctions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.

Details

The DESCRIPTION file: \Sexpr[results=rd,stage=build]{tools:::Rd_package_DESCRIPTION("#1")}gbs2ploidyThis package was not yet installed at build time.

\Sexpr[results=rd,stage=build]{tools:::Rd_package_indices("#1")}gbs2ploidy Index: This package was not yet installed at build time.

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.

References

Gompert Z. and Mock K. (XXXX) Detection of individual ploidy levels with genotyping-by-sequencing (GBS) analysis. Molecular Ecology Resources, submitted.

Examples

## 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[[1]]),cov2=t(dat[[2]]),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[[1]]+dat[[2]])==FALSE,1,mean)
# dx<-apply(dat[[1]]+dat[[2]],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[[3]],pcs=1:2)
# 
# ## boxplots to visualize posterior assignment probabilities by true ploidy 
# ## (which is known because these are simulated data)
# boxplot(pl$pp[,1] ~ dat[[3]],ylab="assignment probability",xlab="ploidy")
# 
# ## run estploidy with a training data set with known ploidy; the data set is 
# ## split into 100 individuals with known ploidy and 100 that are used for 
# ## inference
# truep<-dat[[3]]
# trn<-sort(sample(1:200,100,replace=FALSE))
# truep[-trn]<-NA
# plt<-estploidy(alphas=props,het=hx,depth=dx,train=TRUE,pl=truep,set=trn,
#     nclasses=2,ids=dat[[3]],pcs=1:2)
# 
# ## boxplots to visualize posterior assignment probabilities for individuals that
# ## were not part of the training set by true ploidy (which is known because 
# ## these are simulated data)
# boxplot(plt$pp[,1] ~ dat[[3]][-trn],ylab="assignment probability",xlab="ploidy")
# ## End(Not run)