discretize {fcoex} | R Documentation

## Set the discretized expression attribute Uses the discretize_exprs function of the FCBF package

### Description

Set the discretized expression attribute Uses the discretize_exprs function of the FCBF package

### Usage

    discretize(fc, number_of_bins = 4, method = "varying_width",
      alpha = 1, centers = 3, min_max_cutoff = 0.25, show_pb = TRUE)

    ## S4 method for signature 'fcoex'
    discretize(fc, number_of_bins = 4,
      method = "varying_width", alpha = 1, centers = 3,
      min_max_cutoff = 0.25, show_pb = TRUE)

### Arguments

`fc` | Object of class `fcoex`
`number_of_bins` | Number of equal-width bins for discretization. Note: it is a binary discretization, with the first bin becoming one class ('low') and the other bins, another class ('high').#' Defaults to 4.
`method` | Method applied to all genes for discretization. Methods available: "varying_width" (Binarization modulated by the number_of_bins param), "mean" (Split in ON/OFF by each gene mean expression), "median" (Split in ON/OFF by each gene median expression), "mean_sd"(Split in low/medium/high by each assigning "medium" to the interval between mean +- standard_deviation. Modulated by the alpha param, which enlarges (>1) or shrinks (<1) the "medium" interval. ), ), "kmeans"(Split in different groups by the kmeans algorithm. As many groups as specified by the centers param) and "min_max_%" (Similat to the "varying width", a binarization threshold in a "GMM" (A Gaussian Mixture Model as implemented by the package mclust, trying to fit 2:5 Gaussians). Default is "varying_width"
`alpha` | Modulator for the "mean_sd" method.Enlarges (>1) or shrinks (<1) the "medium" interval. Defaults to 1.
`centers` | Modulator for the "kmeans" method. Defaults to 3.
`min_max_cutoff` | <- Modulator for the "min_max_%" method. Defaults to 0.25.
`show_pb` | Enables a progress bar for the discretization. Defaults to TRUE.

### Value

A data frame with the discretized features in the same order as previously

### Examples

    library(SingleCellExperiment)
    data("mini_pbmc3k")
    targets <- colData(mini_pbmc3k)$clusters
    exprs <- as.data.frame(assay(mini_pbmc3k, "logcounts"))
    fc <- new_fcoex(exprs, targets)
    fc <- discretize(fc)

[Package fcoex version 1.0.0 Index]