runDiffusionMap: Diffusion map analysis

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

Diffusion map analysis

Description

Diffusion map analysis

Usage

runDiffusionMap(
  object,
  dim_reduction_method = c("pca", "nnmf"),
  useIntegrativeEmbeddings = F,
  integrative_method = c("combat", "seurat", "harmony", "supervised_harmony"),
  n.dims.use = 50,
  n.dims.embed = 3,
  n.neighbors = 30,
  distance = c("euclidean", "cosine"),
  n.seed = 1
)

Arguments

object	The SingCellaR object.
dim_reduction_method	Dimensional reduction method name that specifies the embedding matrix.
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.
n.dims.embed	is the number of output dimension. Default 3
n.neighbors	The size of local neighborhood (in terms of number of neighboring sample points) used for manifold approximation. Default 30
distance	The distance metric.
n.seed	The number of seed.

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