findClusters-methods: Identify clusters.

In edroaldo/fusca: Framework for Unified Single-Cell Analysis

Identify clusters.

Description

Identify clusters based on graph-clustering or model based clustering. For model based clustering it is necessary to load the mclust package manually.

Usage

findClusters(
  object,
  assay.type = "RNA",
  sample.name = "Sample1",
  method = "graph.clustering",
  k = 20,
  num.pcs = 20,
  nn.type = "nng",
  sim.type = "jaccard"
)

## S4 method for signature 'CellRouter'
findClusters(
  object,
  assay.type = "RNA",
  sample.name = "Sample1",
  method = "graph.clustering",
  k = 20,
  num.pcs = 20,
  nn.type = "nng",
  sim.type = "jaccard"
)

Arguments

object	CellRouter object
assay.type	character; the type of data to use.
sample.name	character; the name of the tissue sample.
method	character; graph.clustering or model.clustering.
k	numeric; number of nearest neighbors to build a k-nearest neighbors graph.
num.pcs	numeric; number of principal components that will define the space from where the kNN graph is identified. For example, if num.pcs = 10, the kNN graph will be created from a 10-dimensional PCA space.
nn.type	character; method to find the k-nearest neighbor graph. If 'nng', the code will use the nng function of the cccd package. If 'knn' or 'snn', it will use the functions from the bluster package, which are recommended for big data due to their efficiency.
sim.type	character; updates the kNN graph to encode cell-cell similarities using the jaccard or invlog methods.

Value

CellRouter object with the slots updated according to the chosen method: graph.clustering updates graph, rawdata, ndata, and sampTab; model.clustering updates sampTab.

edroaldo/fusca documentation built on March 1, 2023, 1:43 p.m.