identifyClusters_internal: identify cell clusters of a single Seurat object

In amsszlh/scMC: Integrating and comparing multiple single cell genomic datasets

Description

identify cell clusters of a single Seurat object

Usage

identifyClusters_internal(
  object,
  resolution = NULL,
  method = "matrix",
  algorithm = 4,
  resRange = NULL,
  nDims.consensus = 30,
  clustering.method = c("hierarchical", "community"),
  graph.name = NULL,
  ...
)

Arguments

object	Seurat object
resolution	the resolution in Leiden algorithm; if it is NULL, the optimal resoultion will be inferred based on eigen spectrum
method	Method for running leiden (defaults to matrix which is fast for small datasets). Enable method = "igraph" to avoid casting large data to a dense matrix.
algorithm	Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python.
resRange	the range of resolutions in Leiden algorithm; if it is NULL, the optimal range of resoultion will be from 0.1 to 0.5
nDims.consensus	the number of singular values to estimate from the consensus matrix.
clustering.method	method for performing clustering on the consensus matrix from a range of resolutions
graph.name	Name of graph to use for the clustering algorithm
...	other parameter passing to FindClusters function in Seurat package

Value

Seurat object

amsszlh/scMC documentation built on Jan. 2, 2021, 1:51 p.m.