entropy: entropy

In CellMixS: Evaluate Cellspecific Mixing

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)

CellMixS documentation built on Dec. 19, 2020, 2 a.m.