computeUnivariateDigitization {divergence} | R Documentation

## Perform ternary digitization

### 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

[Package divergence version 1.6.0 Index]