computeMultivariateSupport {divergence} | R Documentation

## Estimate the baseline support

### Description

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

### Usage

    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

    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)

[Package divergence version 1.8.0 Index]