mixMetric: mixMetric

In CellMixS: Evaluate Cellspecific Mixing

Description

mixMetric

Usage

mixMetric(
  sce,
  group,
  k = 300,
  dim_red = "PCA",
  assay_name = "logcounts",
  n_dim = 10,
  k_pos = 5,
  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.
n_dim	Numeric. Number of dimensions to include to define the subspace.
k_pos	Position of the cell, which rank to use for scoring, defaults to 5.
res_name	Character. Appendix of the result score's name (e.g. method used to combine batches).

Details

The mixMetric function implements the mixingMetric function from Seurat (See MixingMetric. It takes the median rank of the '__k_pos__ neighbour from each batch as estimation for the data's entropy according to the batch variable. The same result can be assesed using the MixingMetric function and a seurat object from the __Seurat__ package.

Value

A SingleCellExperiment with the mixing metric within colData.

References

Stuart T Butler A Hoffman P Hafemeister C Papalexi E et. al. (2019) Comprehensive Integration of Single-Cell Data. Cell.

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

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