identifyClusters: Cell cluster identification

In supatt-lab/SingCellaR: SingCellaR: an integrative analysis tool for single-cell RNA sequencing (scRNA-seq) data

Cell cluster identification

Description

Cell cluster identification

Usage

identifyClusters(
  object,
  dim_reduction_method = c("pca", "nnmf", "lsi"),
  useIntegrativeEmbeddings = FALSE,
  integrative_method = c("combat", "seurat", "harmony", "supervised_harmony"),
  n.dims.use = 30,
  n.neighbors = 30,
  knn.n_trees = 50,
  knn.metric = c("euclidean", "cosine"),
  knn.n_threads = 1
)

Arguments

object	The SingCellaR object.
useIntegrativeEmbeddings	is logical, if TRUE the embedding matrix genereated from integrative analysis will be used.
integrative_method	The name of an integrative method.
n.dims.use	The number of dimensions as the input. Default 30
n.neighbors	The size of local neighborhood (in terms of number of neighboring sample points) used for manifold approximation. Default 30
knn.n_trees	The number of trees for building the annoy index. Default 50
knn.metric	Type of distance metric to use to find nearest neighbors.
knn.n_threads	The number of threads. Default 1

supatt-lab/SingCellaR documentation built on Aug. 24, 2023, 5:49 p.m.