predictMeth: Predicts methylation levels along CpG sites or for a grid of...
In BiSeq: Processing and analyzing bisulfite sequencing data

Description Usage Arguments Details Value Author(s) See Also Examples

Uses local regression to predict methylation levels per sample.

1	predictMeth(object, h, grid.dist, mc.cores)

`object`	A `BSraw` with a `cluster.id` metadata column on the `rowRanges`, usually the output of `clusterSites`.
`h`	Bandwidth in base pairs. Large values produce a smoother curve. Default is 80.
`grid.dist`	OPTIONAL. If `numeric`, than methylation values are predicted at intervals of `grid.dist` base pairs. By default, methylation is smoothed at each CpG site.
`mc.cores`	Passed to `mclapply`. Default is 1.

Uses binomLikelihoodSmooth with pos = CpG position, m = number methylated reads and n = number of reads. pred.pos corresponds to all CpG positions, or to the grid sites respectively, within the CpG clusters.

A BSrel object containing the predicted methylation levels in the methLevel slot.

Katja Hebestreit

clusterSites, binomLikelihoodSmooth, mclapply

data(rrbs)

rrbs.clust.unlim <- clusterSites(object = rrbs,
                                 groups = colData(rrbs)$group,
                                 perc.samples = 4/5,
                                 min.sites = 20, max.dist = 100)

ind.cov <- totalReads(rrbs.clust.unlim) > 0
quant <- quantile(totalReads(rrbs.clust.unlim)[ind.cov], 0.9)
rrbs.clust.lim <- limitCov(rrbs.clust.unlim, maxCov = quant)

# with a small subset to save calculation time:
rrbs.part <- rrbs.clust.lim[1:100,]

predictedMeth <- predictMeth(object=rrbs.part)