density_peak: Cluster cells using Density Peak algorithm

In scfurl/m3addon: This package adds to the popular "monocle3"

Description

Unsupervised clustering of cells is a common step in many single-cell expression workflows. \ In an experiment containing a mixture of cell types, each cluster might correspond to a different \ cell type. This method takes a cell_data_set as input along with a requested number of clusters, \ clusters them using density peak clustering), and then returns the cell_data_set with the cluster assignments \ stored in the pData table as "Cluster". Use the plot_rho_delta to visualize the rho and delta parameters that will \ help determine the number of clusters to cluster.

Usage

density_peak(
  cds,
  rho = NULL,
  delta = NULL,
  reduction_method = c("UMAP", "tSNE"),
  gaussian = T,
  pData_col = "Cluster"
)

Arguments

cds	the cell_data_set upon which to perform this operation
rho	The threshold of local density (rho) used to select the density peaks
delta	The threshold of local distance (delta) used to select the density peaks
gaussian	A logic flag passed to densityClust function in desnityClust package to determine whether or not Gaussian kernel will be used for calculating the local density

Value

an updated cell_data_set object

References

Rodriguez, A., & Laio, A. (2014). Clustering by fast search and find of density peaks. \ Science, 344(6191), 1492-1496. doi:10.1126/science.1242072

scfurl/m3addon documentation built on Aug. 9, 2021, 5:30 p.m.