cluster_sc: Cluster Single Cell

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

Description

This will perform clustering on your single cell data.

Usage

cluster_sc(
  input,
  dimension,
  method,
  num_clust = NA,
  s = 2,
  xcol = "x",
  ycol = "y",
  set.seed = F
)

Arguments

input	the input ex_sc
dimension	either "Comp" or "2d"
method	can either be "spectral" or "density" which is on 2d
num_clust	the number of clusters
s	the number of standard deviations from the curve to select cluster centers
xcol	first column to use with dimentions for the 2d method
ycol	second column to use with dimentions for the 2d method

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/SignallingSingleCell documentation built on Dec. 29, 2021, 4:12 p.m.