find_clusters: Find cell clusters

In huipan1973/ezscrnaseq: What the Package Does (One Line, Title Case)

Find cell clusters

Description

Find cell clusterd using igraph.

Usage

find_clusters(
  sce,
  use_dimred = "PCA",
  seed = 100,
  snn_k = 10,
  snn_type = c("rank", "number", "jaccard"),
  ncores = 1,
  method = c("walktrap", "spinglass", "louvain"),
  steps = 4,
  spins = 25,
  min_member = 20,
  prefix = NULL,
  plot = TRUE,
  verbose = TRUE
)

Arguments

sce	A SingleCellExperiment object containing expression values, usually counts.
use_dimred	A string specifying whether existing values in reducedDims(sce) should be used.
seed	Random seed.
snn_k	The number of nearest neighbors to consider during graph construction.
snn_type	The type of weighting scheme to use for shared neighbors.
ncores	Number of cores.
method	"walktrap", "spinglass", or "louvain" for finding communities in graphs via short random walks, a spin-glass model and simulated annealing, or multi-level modularity optimization.
steps	The length of the random walks to perform.
spins	Integer constant, the number of spins to use. This is the upper limit for the number of communities. It is not a problem to supply a (reasonably) big number here, in which case some spin states will be unpopulated.
min_member	Minimal number of cluster members.
prefix	Prefix for file name for the QC metrics histograms.
plot	TRUE/FASLE for whether plot the QC metrics histograms.
verbose	TRUE/FASLE for specifying whether diagnostics should be printed to screen.

Value

A SingleCellExperiment object with cell cluster information.

huipan1973/ezscrnaseq documentation built on July 12, 2022, 9:36 p.m.