computeMultivariateBinaryMatrix: Compute the binary matrix with digitized divergence coding

In divergence: Divergence: Functionality for assessing omics data by divergence with respect to a baseline

Description

Function for obtaining the binary form for a matrix for multivariate divergence of data given a baseline range

Usage

computeMultivariateBinaryMatrix(seMat, Baseline)

Arguments

seMat	SummarizedExperiment with assay to be digitized, in [0, 1], with each column corresponding to a sample and each row corresponding to a feature; usually in quantile form.
Baseline	A Baseline object; this corresponds to the output of findMultivariateGammaWithSupport() or computeMultivariateSupport()

Value

A matrix with the same columns as Mat, with rows being the multivariate features, containing the binary form data.

Examples

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

divergence documentation built on Nov. 8, 2020, 8:02 p.m.