identifyClusters: Identify cell clusters

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

Description

Identify cell clusters

Usage

identifyClusters(
  object,
  mode = c("separate", "integrated"),
  resolution = NULL,
  method = c("matrix", "igraph"),
  algorithm = 4,
  resRange = NULL,
  nDims.consensus = 30,
  clustering.method = c("hierarchical", "community"),
  graph.name = NULL,
  ...
)

Arguments

object	a list of Seurat objects or a single Seurat object
mode	"separate" or "integrated"; "separate" means performing clustering for each dataset separately, and "integrated" means performing clustering on the integrated data
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 resolution values in Leiden algorithm; if it is NULL, the default 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

a list of Seurat objects or a single Seurat object

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