predictMeth {BiSeq} | R Documentation

## Predicts methylation levels along CpG sites or for a grid of sites in CpG clusters.

### Description

Uses local regression to predict methylation levels per sample.

### Usage

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

### Arguments

`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.

### Details

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.

### Value

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

### Author(s)

Katja Hebestreit

### See Also

`clusterSites`, `binomLikelihoodSmooth`, `mclapply`

### Examples

    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)

[Package BiSeq version 1.22.0 Index]