isi {CellMixS} | R Documentation

## isi

### Description

isi

### Usage

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

### Details

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.

### Value

A `SingleCellExperiment` with the entropy score within colData.

### References

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)

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

[Package CellMixS version 1.10.0 Index]