denovoExpr: Estimate expression for de novo splicing variants.
In casper: Characterization of Alternative Splicing based on Paired-End Reads

Description Usage Arguments Value Author(s) References Examples

Obtains expression estimates from denovoGenomeExpr objects, as returned by calcDenovo. When rpkm is set to TRUE, fragments per kilobase per million are returned. Otherwise relative expression estimates are returned.

The estimates can be obtained by Bayesian model averaging (default) or by selecting the model with highest posterior probability. See details.

1	denovoExpr(x, pc, rpkm = TRUE, summarize = "modelAvg", minProbExpr = 0.5, minExpr = 0.05)

`x`	`denovoGenomeExpr` object returned by `calcExp`
`pc`	Named vector of exon path counts as returned by `pathCounts`
`rpkm`	Set to `FALSE` to return relative expression levels, i.e. the proportion of reads generated from each variant per gene. These proportions add up to 1 for each gene. Set to `TRUE` to return fragments per kilobase per million (RPKM).
`summarize`	Set to `"modelAvg"` to obtain model averaging estimates, or to `"bestModel"` to select the model with highest posterior probability. We recommend the former, as even the best model may have low posterior probability.
`minProbExpr`	Variants with (marginal posterior) probability of being expressed below `minProbExpr` are omitted from the results. This argument is useful to eliminate variants that are not at least moderately supported by the data.
`minExpr`	Variants with relative expression `minExpr` are omitted from the results. This is useful to eliminate variants to which few reads are assigned, e.g. due to read miss-alignments or biases.

Expression set with expression estimates. The featureData indicates the gene island id, posterior probability that each variant is expressed (column "probExpressed") and the number of aligned reads per gene island (column "explCnts").

David Rossell

Rossell D, Stephan-Otto Attolini C, Kroiss M, Stocker A. Quantifying Alternative Splicing from Paired-End RNA-sequencing data. Annals of Applied Statistics, 8(1):309-330.

## NOTE: toy example with few reads & genes to illustrate code usage
##       Results with complete data are much more interesting!

data(K562.r1l1)
data(hg19DB)

#Pre-process
bam0 <- rmShortInserts(K562.r1l1, isizeMin=100)
pbam0 <- procBam(bam0)

#Estimate distributions, get path counts
distrs <- getDistrs(hg19DB,bam=bam0,readLength=75)
pc <- pathCounts(pbam0, DB=hg19DB)

#Set prior distrib on model space
mprior <- modelPrior(hg19DB, maxExons=40, smooth=FALSE)

#Fit model
denovo <- calcDenovo(distrs,targetGenomeDB=hg19DB,pc=pc,readLength=75,priorq=3,mprior=mprior,minpp=0)

head(names(denovo))
denovo[['6499']]
posprob(denovo[['6499']])

#Get estimates
eset <- denovoExpr(denovo, pc=pc, rpkm=TRUE, minProbExpr=0.5)

head(exprs(eset))
head(fData(eset))