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

Description Usage Arguments Value Examples

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

1 2	computeUnivariateSupport(Mat, gamma = 0.1, beta = 0.95, parallel = TRUE, 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.
`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.
`parallel`	Logical indicating whether to compute features parallelly with mclapply on Unix based systems (defaults to TRUE, switched to FALSE if parallel package is not available).
`verbose`	Logical indicating whether to print status related messages during computation (defaults to TRUE).

A list with elements "Ranges": data frame with the baseline interval for each feature, "Support": binary matrix of the same dimensions as Mat indicating whether each sample was a support for a feature or not (1=support, 0=not in the support), "gamma": gamma value, and "alpha": the expected number of divergent features per sample estimated over the samples.

1
2
3

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