entropy {CellMixS} | R Documentation

## entropy

### Description

entropy

### Usage

    entropy(
      sce,
      group,
      k,
      dim_red = "PCA",
      assay_name = "logcounts",
      n_dim = 10,
      res_name = NULL
    )

### Arguments

`sce` | `SingleCellExperiment` object, with the integrated data.
`group` | Character. Name of group/batch variable. Needs to be one of `names(colData(sce))`.
`k` | Numeric. Number of k-nearest neighbours (knn) to use.
`dim_red` | Character. Name of embeddings to use as subspace for distance distributions. Default is "PCA".
`assay_name` | Character. Name of the assay to use for PCA. Only relevant if no existing 'dim_red' is provided. Must be one of `names(assays(sce))`. Default is "logcounts".
`n_dim` | Numeric. Number of dimensions to include to define the subspace.
`res_name` | Character. Appendix of the result score's name (e.g. method used to combine batches).

### Details

The entropy function calculates the Shannon entropy of the group variable within each cell's k-nearest neighbourhood. For balanced batches a Shannon entropy close to 1 indicates high randomness and mixing. For unbalanced batches entropy should be interpreted with caution, but could work as a relative measure in a comparative setting.

### Value

A `SingleCellExperiment` with the entropy score within colData.

### Examples

    library(SingleCellExperiment)
    sim_list <- readRDS(system.file("extdata/sim50.rds", package = "CellMixS"))
    sce <- sim_list[[1]][, c(1:15, 400:420, 16:30)]

    sce <- entropy(sce, "batch", k = 20)

[Package CellMixS version 1.4.2 Index]