sMetaC: similarity-based ensemble meta-clustering

In shibiaowan/SHARP: hyperfast and accurate single-cell RNA-Seq data clustering

Description

This function is to do similarity-based ensemble clustering for combining the results from different partitions of single cells. The similarity is measured by the group-group correlations.

Usage

sMetaC(
  rerowColor,
  sE1,
  folds,
  hmethod,
  finalN.cluster,
  minN.cluster,
  maxN.cluster,
  sil.thre,
  height.Ntimes
)

Arguments

nC	a m*n matrix, where m is the number of cells, n is the number of clustering algorithms, and the (i,j)-element of nC represents the cluter for the i-th cell by the j-th clutering predictor.

Examples

finalrowColor = sMetaC(fColor, E1, folds, hmethod, N.cluster, minN.cluster, maxN.cluster, sil.thre, height.Ntimes)

shibiaowan/SHARP documentation built on April 28, 2021, 1:56 p.m.