findMultivariateGammaWithSupport {divergence} | R Documentation

## Find optimal gamma and corresponding support for list of feature sets

### 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)

[Package divergence version 1.0.0 Index]