LouvainDepart {scpoisson}R Documentation

Louvain clustering using departure as data representation

Description

This function returns a list with elements useful to check and compare cell clustering.

Usage

LouvainDepart(
  data,
  pdat = NULL,
  PCA = TRUE,
  N = 15,
  pres = 0.8,
  tsne = FALSE,
  umap = FALSE,
  ...
)

Arguments

data

A UMI count matrix with genes as rows and cells as columns or an S3 object for class 'scppp'.

pdat

A matrix used as input for cell clustering. If not specify, the departure matrix will be calculated within the function.

PCA

A logic value specifying whether apply PCA before Louvain clustering, default is TRUE.

N

A numeric value specifying the number of principal components included for further clustering (default 15).

pres

A numeric value specifying the resolution parameter in Louvain clustering (default 0.8)

tsne

A logic value specifying whether t-SNE dimension reduction should be applied for visualization.

umap

A logic value specifying whether UMAP dimension reduction should be applied for visualization.

...

not used.

Details

This is a function used to get cell clustering using Louvain clustering algorithm implemented in the Seurat package.

Value

A list with the following elements:

References

Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck III WM, Hao Y, Stoeckius M, Smibert P, Satija R (2019). “Comprehensive Integration of Single-Cell Data.” Cell, 177, 1888-1902. doi:10.1016/j.cell.2019.05.031.

Examples


set.seed(1234)
test_set <- matrix(rpois(500, 2), nrow = 20)
rownames(test_set) <- paste0("gene", 1:nrow(test_set))
colnames(test_set) <- paste0("cell", 1:ncol(test_set))
LouvainDepart(test_set)


[Package scpoisson version 0.0.1 Index]