findUnivariateGammaWithSupport {divergence} | R Documentation

## Search for optimal gamma and associated support

### Description

Function for searching through a range of gamma values for finding the smallest gamma that provides expected proportion of divergent features per sample less than or equal to alpha.

### Usage

    findUnivariateGammaWithSupport(seMat, gamma = c(1:9/100, 1:9/10),
      beta = 0.95, alpha = 0.01, 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` | Range of gamma values to search through. By default gamma = {0.01, 0.02, ... 0.09, 0.1, 0.2, ..., 0.9}.
`beta` | Parameter for eliminating outliers (0 < beta <= 1). By default beta=0.95.
`alpha` | Expected proportion of divergent features per sample to be estimated over the samples in Mat. By default alpha = 0.01; i.e. search for the smallest gamma that provides 1% or less number of divergent features per sample.
`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, "optimal": logical indicaing whether the selected gamma value provided the necessary alpha requirement, and "alpha_space": a data frame with alpha values for each gamma searched.

### Examples

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

[Package divergence version 1.6.0 Index]