clustexp {RaceID}

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)