computeMultivariateSupport: Estimate the baseline support

Description Usage Arguments Value Examples

View source: R/main.R

Description

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

Usage

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computeMultivariateSupport(seMat, FeatureSets, gamma = 0.1,
  beta = 0.95, 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

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.

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_list: 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 used for baseline computation. distance: the type of distance used for baselien computation.

Examples

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

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