isi: isi
In CellMixS: Evaluate Cellspecific Mixing

Description Usage Arguments Details Value References Examples

isi

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

`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.
`weight`	Boolean. If TRUE, batch probabilities to calculate the isi score are weighted by the mean distance of their cells towards the cell of interest. Relevant for metrics: 'isi'.
`res_name`	Character. Appendix of the result score's name (e.g. method used to combine batches).

The isi function calculates the inverse Simpson index of the group variable within each cell's k-nearest neighbourhood. The Simpson index describes the probability that two entities are taken at random from the dataset and its inverse represent the effective number of batches in a neighbourhood. The inverse Simpson index has been proposed as a diversity score for batch mixing in single cell RNAseq by Korunsky et al. They provide a distance-based neighbourhood weightening in their Lisi package. Here, we provide a simplified way of weightening probabilitities, if the weight argument is enabled.

A SingleCellExperiment with the entropy score within colData.

Korsunsky I Fan J Slowikowski K Zhang F Wei K et. al. (2018). Fast, sensitive, and accurate integration of single cell data with Harmony. bioRxiv (preprint)

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

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