computeUnivariateDigitization: Perform ternary digitization

In wikum/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

Usage

computeUnivariateDigitization(seMat, seMat.base, computeQuantiles = TRUE,
  gamma = c(1:9/100, 1:9/10), beta = 0.95, alpha = 0.01,
  parallel = TRUE, 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
computeQuantiles	Logical; 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.
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).
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 (optional).
classes	Vector of class labels (optional).

Value

A list with elements: Mat.div: divergence coding of data matrix in ternary (-1, 0, 1) form, of same dimensions at seMat baseMat.div: divergence coding of base matrix in ternary (-1, 0, 1) form, of same dimensions at seMat.base div: data frame with the number of divergent features in each sample, including upper and lower divergence 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, optimal: logical indicaing whether the selected gamma value provided the necessary alpha requirement, alpha_space: a data frame with alpha values for each gamma searched

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 = computeUnivariateDigitization(
  seMat = seMat,
  seMat.base = seMat.base,
 parallel = TRUE
)
assays(seMat)$div = div$Mat.div

wikum/divergence documentation built on Sept. 17, 2021, 12:33 a.m.