computeMultivariateDigitization: Perform binary digitization

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

Description

Function for obtaining the digitized form, along with other relevant statistics and measures given a data matrix and a baseline matrix with multivariate features of interest

Usage

computeMultivariateDigitization(seMat, seMat.base, FeatureSets,
  computeQuantiles = TRUE, gamma = c(1:9/100, 1:9/10), beta = 0.95,
  alpha = 0.01, distance = "euclidean", verbose = TRUE,
  findGamma = TRUE, Groups = NULL, classes = NULL)

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.
seMat.base	SummarizedExperiment with baseline assay in [0, 1], with each column corresponding to a sample and each row corresponding to a feature
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.
computeQuantiles	Apply quantile transformation to both data and baseline matrices (TRUE or FALSE; defaults to TRUE).
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. The optimal gamma providing this level of divergence in the baseline data will be searched for.
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).
findGamma	Logical indicating whether to search for optimal gamma values through the given gamma values (defaults to TRUE). If FALSE, the first value given in gamma will be used.
Groups	Factor indicating class association of samples
classes	Vector of class labels

Value

A list with elements: Mat.div: divergence coding of data matrix in binary form, of same dimensions at seMat baseMat.div: divergence coding of base matrix in binary form, of same column names at seMat.base, rows being multivariate features. div: data frame with the number of divergent features in each sample features.div: data frame with the divergent probability of each feature; divergence probability for each phenotype in included as well if 'Groups' and 'classes' inputs were provided. Baseline: a list containing a "Ranges" data frame with the baseline interval for each feature, and a "Support" binary matrix of the same dimensions as Mat indicating whether each sample was a support or a feature or not (1=support, 0=not in the support), gamma: selected gamma value alpha: the expected number of divergent features per sample computed over the baseline data matrix

Examples

baseMat = breastTCGA_Mat[, breastTCGA_Group == "NORMAL"]
dataMat = breastTCGA_Mat[, breastTCGA_Group != "NORMAL"]
seMat.base = SummarizedExperiment(assays=list(data=baseMat))
seMat = SummarizedExperiment(assays=list(data=dataMat))
div = computeMultivariateDigitization(
  seMat = seMat,
  seMat.base = seMat.base,
  FeatureSets = msigdb_Hallmarks
)

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