ldfDiff: ldfDiff

In CellMixS: Evaluate Cellspecific Mixing

Description

Determines cell-specific changes in the Local Density Factor before and after data integration.

Usage

ldfDiff(
  sce_pre_list,
  sce_combined,
  group,
  k = 75,
  dim_red = "PCA",
  dim_combined = dim_red,
  assay_pre = "logcounts",
  assay_combined = "logcounts",
  n_dim = 20,
  res_name = NULL
)

Arguments

sce_pre_list	A list of SingleCellExperiment objects with single datasets before integration. Names should correspond to levels in colData(sce_combined)$group
sce_combined	A SingleCellExperiment object with the combined data.
group	Character. Name of group/batch variable that separates elements of sce_pre_list. Needs to be one of names(colData(sce_combined)).
k	Numeric. Number of k-nearest neighbours (knn) to use.
dim_red	Character. Name of embeddings to use as subspace to calculate LDF before integration. Default is "PCA".
dim_combined	Character. Name of embeddings to use as subspace to calculate LDF after integration. Default is dim_red.
assay_pre	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_pre)). Default is "logcounts".
assay_combined	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_combined)). Default is "logcounts".
n_dim	Numeric. Number of PCs to include to define subspaces.
res_name	Character. Appendix of the result score's name (e.g. method used to combine batches). Used to specify result name for more than one run on the same input.

Details

The ldfDiff function calculates differences in LDF for each element in sce_pre_list and their corresponding cells in sce_combined using ldfSce. If 'dim_red' is not defined a PCA will be calculated using runPCA. In this case 'assay_pre' need to refer to the data slot that shall define the subspace. Similar refer 'dim-combined' and 'assay_combined' to the integrated subspace or to the resp. "corrected" count data slot. 'k' can be used to define the level of local structure that is tested. The smaller 'k' the more focus is on detailed structures, while a large k will tets overall changes.

Value

A SingleCellExperiment object.

References

Latecki, Longin Jan and Lazarevic, Aleksandar and Pokrajac, Dragoljub (2007). Outlier Detection with Kernel Density Functions. Mach. Learn. Data Min. Pattern Recognit.. Springer Berlin Heidelberg.

See Also

ldfSce, .ldfKnn.

Other ldf functions: ldfSce()

Examples

library(SingleCellExperiment)
sim_list <- readRDS(system.file("extdata/sim50.rds", package = "CellMixS"))
sce <- sim_list[["batch20"]][, c(1:50, 300:350)]
sce_batch1 <- sce[,colData(sce)$batch == "1"]
sce_batch2 <- sce[,colData(sce)$batch == "2"]
sce_pre_list <- list("1" = sce_batch1, "2" = sce_batch2)

sce_ldf <- ldfDiff(sce_pre_list, sce, k = 10, group = "batch",
dim_combined = "MNN", n_dim = 2)

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