netEmbedding: Manifold learning of the signaling networks based on their...

In sqjin/CellChat: Inference and analysis of cell-cell communication from single-cell and spatial transcriptomics data

Manifold learning of the signaling networks based on their similarity

Description

Manifold learning of the signaling networks based on their similarity

Usage

netEmbedding(
  object,
  slot.name = "netP",
  type = c("functional", "structural"),
  comparison = NULL,
  pathway.remove = NULL,
  umap.method = c("umap-learn", "uwot"),
  n_neighbors = NULL,
  min_dist = 0.3,
  ...
)

Arguments

object	CellChat object
slot.name	the slot name of object that is used to compute centrality measures of signaling networks
type	"functional","structural"
comparison	a numerical vector giving the datasets for comparison. No need to define for a single dataset. Default are all datasets when object is a merged object
pathway.remove	a range of the number of patterns
umap.method	UMAP implementation to run. Can be umap-learn: Run the python umap-learn package; uwot: Runs umap via the uwot R package; If umap.method = "uwot", please make sure you have installed the 'uwot' (https://github.com/jlmelville/uwot)
n_neighbors	the number of nearest neighbors in running umap
min_dist	This controls how tightly the embedding is allowed compress points together. Larger values ensure embedded points are moreevenly distributed, while smaller values allow the algorithm to optimise more accurately with regard to local structure. Sensible values are in the range 0.001 to 0.5.
...	Parameters passing to umap

sqjin/CellChat documentation built on Nov. 10, 2023, 4:29 a.m.