locStructure {CellMixS} | R Documentation

## locStructure

### Description

locStructure

### Usage

    locStructure(
      sce,
      group,
      dim_combined,
      k = 100,
      dim_red = "PCA",
      assay_name = "logcounts",
      n_dim = 10,
      n_combined = 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))`.
`dim_combined` | Charactyer. Name of the reduced dimensional representation of the integrated data. Needs to be one of `reducedDimNames(sce))`.
`k` | Numeric. Number of k-nearest neighbours (knn) to use.
`dim_red` | Character. Name of embeddings to calculate neighbourhoods before integration. Default is "PCA".
`assay_name` | Character. Name of the assay to use for PCA of the original (not integrated) data. Should not refer to "corrected" counts.
`n_dim` | Numeric. Number of dimensions to include for the original data.
`n_combined` | Numeric. Number of dimensions to include for the integrated data.
`res_name` | Character. Appendix of the result score's name (e.g. method used to combine batches).

### Details

The locStructure function implements the localStructure function from Seurat (See `LocalStruct`. For each group it calculates the k nearest neighbour within PCA space before integration and compares it to the knn within the reduced dimensional representation after integration. The score represents the proportion of overlapping neighbours. The `LocalStruct` function is based on the `RunPCA` function, while here `runPCA` is used. This can cause small deviance from the `LocalStruct` function, but overall these functions are equivalent.

### 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[["batch20"]][, c(1:50, 300:350)]

    sce <- locStructure(sce, "batch", "MNN", k = 20, assay_name = "counts")

[Package CellMixS version 1.2.6 Index]