DIscBIO (version 1.2.2)

Clustexp: Clustering of single-cell transcriptome data

Description

This functions performs the initial clustering of the RaceID algorithm.

Usage

Clustexp(
  object,
  clustnr = 3,
  bootnr = 50,
  metric = "pearson",
  do.gap = TRUE,
  SE.method = "Tibs2001SEmax",
  SE.factor = 0.25,
  B.gap = 50,
  cln = 0,
  rseed = NULL,
  quiet = FALSE
)
# S4 method for DISCBIO
Clustexp(
  object,
  clustnr = 3,
  bootnr = 50,
  metric = "pearson",
  do.gap = TRUE,
  SE.method = "Tibs2001SEmax",
  SE.factor = 0.25,
  B.gap = 50,
  cln = 0,
  rseed = NULL,
  quiet = FALSE
)

Value

The DISCBIO-class object input with the cpart slot filled.

Arguments

object

DISCBIO class object.

clustnr

Maximum number of clusters for the derivation of the cluster number by the saturation of mean within-cluster-dispersion. Default is 20.

bootnr

A numeric value of booststrapping runs for clusterboot. Default is 50.

metric

Is the method to transform the input data to a distance object. Metric has to be one of the following: ["spearman", "pearson", "kendall", "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"].

do.gap

A logical vector that allows generating the number of clusters based on the gap statistics. Default is TRUE.

SE.method

The SE.method determines the first local maximum of the gap statistics. The SE.method has to be one of the following:["firstSEmax", "Tibs2001SEmax", "globalSEmax", "firstmax", "globalmax"]. Default is "Tibs2001SEmax"

SE.factor

A numeric value of the fraction of the standard deviation by which the local maximum is required to differ from the neighboring points it is compared to. Default is 0.25.

B.gap

Number of bootstrap runs for the calculation of the gap statistics. Default is 50

cln

Number of clusters to be used. Default is NULL and the cluster number is inferred by the saturation criterion.

rseed

Random integer to enforce reproducible clustering results.

quiet

if `TRUE`, intermediate output is suppressed

Examples

sc <- DISCBIO(valuesG1msTest) # changes signature of data
sc <- Clustexp(sc, cln = 2)