computeMultivariateSupport: Estimate the baseline support
In wikum/divergence.preSE: Divergence Computations

Description Usage Arguments Value Examples

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

1 2	computeMultivariateSupport(Mat, FeatureSets, gamma = 0.1, beta = 0.95, distance = "euclidean", verbose = TRUE)

`Mat`	Matrix of data 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).

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.

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baseMat = breastTCGA_Mat[, breastTCGA_Group == "NORMAL"]
baseMat.q = computeQuantileMatrix(baseMat)
baseline = computeMultivariateSupport(Mat=baseMat.q, FeatureSets=msigdb_Hallmarks)