computeUnivariateSupport {divergence} | R Documentation

## Estimate the baseline support

### Description

Function for computing the basline support for univariate features given gamma and beta parameters.

### Usage

    computeUnivariateSupport(seMat, gamma = 0.1, beta = 0.95,
      parallel = TRUE, verbose = TRUE)

### Arguments

`seMat` | SummariziedExperiment with an assay in [0, 1], with each column corresponding to a sample and each row corresponding to a feature; usually in quantile form.
`gamma` | Parameter for selecting radius around each support point (0 < gamma < 1). By default gamma = 0.1.
`beta` | Parameter for eliminating outliers (0 < beta <= 1). By default beta=0.95.
`parallel` | Logical indicating whether to compute features parallelly with mclapply on Unix based systems (defaults to TRUE, switched to FALSE if parallel package is not available).
`verbose` | Logical indicating whether to print status related messages during computation (defaults to TRUE).

### Value

A list with elements "Ranges": data frame with the baseline interval for each feature, "Support": binary matrix of the same dimensions as Mat indicating whether each sample was a support for a feature or not (1=support, 0=not in the support), "gamma": gamma value, and "alpha": the expected number of divergent features per sample estimated over the samples.

### Examples

    baseMat = breastTCGA_Mat[, breastTCGA_Group == "NORMAL"]
    seMat.base = SummarizedExperiment(assays=list(data=baseMat))
    assays(seMat.base)$quantile = computeQuantileMatrix(seMat.base)
    baseline = computeUnivariateSupport(seMat=seMat.base)

[Package divergence version 1.2.0 Index]