cluster_cells: Cluster Single Cell

In kgellatl/SignalCell: Network Analysis for single cell RNA sequencing Data (sc-RNASeq)

Description

This will perform clustering on your single cell data.

Usage

cluster_cells(
  input,
  lem,
  dims,
  method,
  embedding = NULL,
  xy = NULL,
  num_clust = NULL,
  name = "Cluster",
  s = 2
)

Arguments

input	the input ex_sc
lem	the LEM within ReducedDims()
dims	either "2d" or "Comp"
method	can either be "spectral" or "density" which is on 2d
embedding	if method is 2D, an embedding must be provided
num_clust	the number of clusters. Required for spectral but optional for density.
name	name of the colData cluster column
s	the number of standard deviations from the curve to select cluster centers

Details

This will perform clustering on either the high dimensional PCA / ICA components if dimension = Comp, or the 2d tsne result if method = density. Typically spectral clustering works much better on higher dimensional data, which density based clustering works better on 2d data.

Examples

ex_sc_example <- cluster_sc(input = ex_sc_example, dimension = "Comp", method = "spectral", num_clust = 6)

kgellatl/SignalCell documentation built on Sept. 3, 2020, 8:45 a.m.