findMultivariateGammaWithSupport: Find optimal gamma and corresponding support for list of...

In wikum/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 and support that provides expected proportion of divergent features per sample less than or equal to alpha.

Usage

findMultivariateGammaWithSupport(seMat, FeatureSets, gamma = 1:9/10,
  beta = 0.95, alpha = 0.01, distance = "euclidean",
  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.
FeatureSets	The multivariate features in list or matrix form. In list form, each list element should be a vector of individual features; in matrix form, it should be a binary matrix with rownames being individual features and column names being the names of the feature sets.
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.
distance	Type of distance to be calculated between points. Any type of distance that can be passed on to the dist function can be used (default 'euclidean').
verbose	Logical indicating whether to print status related messages during computation (defaults to TRUE).

Value

A list with elements: Support: a matrix indicating which samples were included in the support. Baseline: a list where each element is the baseline of a multivariate feature. featureMat: the multivariate features in matrix form. alpha: the expected number of divergent multivariate features per sample. gamma: the gamma parameter selected. distance: the type of distance used for baselien computation. optimal: TRUE or FALSE indicating whether the alpha criteria was met alpha_space: the alpha values correspinding to the gamma values searched through

Examples

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

wikum/divergence documentation built on Sept. 17, 2021, 12:33 a.m.