discretize_gene_supervised {FCBF} | R Documentation

## discretize_gene_supervised

### Description

Uses several discretizations and selects the one that is best for a given variable (gene) in comparison to a target class by equivocation Note that set.seed() should be used for reproducing the results. The inner kmeans #' function would, otherwise, provide different results each time.

### Usage

    discretize_gene_supervised(gene, target, output = "discretized_vector",
      discs = c(".split_vector_in_two_by_median",
      ".split_vector_in_two_by_mean", ".split_vector_by_kmeans",
      ".split_vector_in_three_by_mean_sd",
      ".split_vector_in_two_by_min_max_thresh"), vw_params = c(0.25, 0.5,
      0.75), kmeans_centers = c(2, 3, 4), sd_alpha = c(0.75, 1, 1.25))

### Arguments

`gene` | A previously normalized gene expression vector
`target` | A series of labels matching each of the values in the gene vector
`output` | If it is equal to 'discretized_vector', the output is the vector. I it is 'su', returns a dataframe. Defaults to 'discretized_vector'
`discs` | Defaults to c( ".split_vector_in_two_by_median", split_vector_in_two_by_mean", ".split_vector_by_kmeans", ".split_vector_in_three_by_mean_sd", ".split_vector_in_two_by_vw")
`vw_params` | cuttof parameters for the varying width function. Defaults to 0.25, 0.5 and 0.75
`kmeans_centers` | Numeric vector with the number of centers to use for kmeans. Defaults to 2, 3 and 4
`sd_alpha` | Parameter for adusting the 'medium' level of the mean +- sd discretization. Defaults to sd_alpha = c(0.75, 1, 1.25))

### Details

Note that a seed for random values has to bew set for reproducibility. Otherwise, the "kmeans" value might vary from iteration to iteration.

### Value

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

### Examples

     data(scDengue)
     exprs <- as.data.frame(SummarizedExperiment::assay(scDengue, 'logcounts'))
     gene <- exprs['ENSG00000166825',]
     infection <- SummarizedExperiment::colData(scDengue)
     target <- infection$infection
     set.seed(3)
     discrete_expression <- as.data.frame(discretize_gene_supervised(gene, target))
     table(discrete_expression)

[Package FCBF version 1.4.0 Index]