computeUnivariateSupport: Estimate the baseline support

In divergence: Divergence: Functionality for assessing omics data by divergence with respect to a baseline

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)

divergence documentation built on Nov. 8, 2020, 8:02 p.m.