clustexp: Clustering of single-cell transcriptome data

In RaceID: Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data

Clustering of single-cell transcriptome data

Description

This functions performs the initial clustering of the RaceID3 algorithm.

Usage

clustexp(
  object,
  sat = TRUE,
  samp = NULL,
  cln = NULL,
  clustnr = 30,
  bootnr = 50,
  rseed = 17000,
  FUNcluster = "kmedoids",
  verbose = TRUE
)

Arguments

object	SCseq class object.
sat	logical. If TRUE, then the number of clusters is determined based on finding the saturation point of the mean within-cluster dispersion as a function of the cluster number. Default is TRUE. If FALSE, then cluster number needs to be given as cln.
samp	Number of random sample of cells used for the inference of cluster number and for inferring Jaccard similarities. Default is 1000.
cln	Number of clusters to be used. Default is NULL and the cluster number is inferred by the saturation criterion.
clustnr	Maximum number of clusters for the derivation of the cluster number by the saturation of mean within-cluster-dispersion. Default is 30.
bootnr	Number of booststrapping runs for clusterboot. Default is 50.
rseed	Integer number. Random seed to enforce reproducible clustering results. Default is 17000.
FUNcluster	Clustering method used by RaceID3. One of "kmedoids", "kmeans", "hclust". Default is "kmedoids".
verbose	logical. If FALSE then status output messages are disabled. Default is TRUE.

Value

SCseq object with clustering data stored in slot cluster and slot clusterpar. The clustering partition is stored in cluster$kpart.

Examples

sc <- SCseq(intestinalDataSmall)
sc <- filterdata(sc)
sc <- compdist(sc)
sc <- clustexp(sc)

RaceID documentation built on Sept. 28, 2023, 5:06 p.m.