ldfSce: ldfSce

Description Usage Arguments Details Value References See Also Examples

Description

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

Usage

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ldfSce(
  sce_name,
  sce_pre_list,
  sce_combined,
  group,
  k = 75,
  dim_red = "PCA",
  dim_combined = dim_red,
  assay_pre = "logcounts",
  assay_combined = "logcounts",
  n_dim = 20
)

Arguments

sce_name

Character. Name of the element in sce_pre_list to calculate LDF differences in.

sce_pre_list

A list of SingleCellExperiment objects with single datasets before integration. Names need to correspond to levels in colData(sce_combined)$group and sce_name!!

sce_combined

A SingleCellExperiment object with 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.

Details

The ldfSce function calculates differences in LDF for one specified element in sce_pre_list and their corresponding cells in sce_combined. 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. K-nearest neighbours (knn) are determined in the subspaces before integration defined by 'dim_red'. The same set of knn are used to determine LDF before and after integration.

Value

A data.frame with difference in LDF as column named "diff_ldf".

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

ldfDiff, .ldfKnn.

Other ldf functions: ldfDiff()

Examples

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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_pre_list <- list("1" = sce_batch1)

ldf_1 <- ldfSce("1", sce_pre_list, sce, k = 10, group = "batch",
dim_combined = "MNN", n_dim = 5)

almutlue/CellMixS documentation built on Dec. 22, 2020, 11:07 a.m.