findUnivariateGammaWithSupport: Search for optimal gamma and associated support

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

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)

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