discretize: Set the discretized expression attribute Uses the...
In lubianat/fcoex: FCBF-based Co-Expression Networks for Single Cells

Description Usage Arguments Value Examples

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

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)

`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.

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

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)