randConsensusMatrix: Calculate consensus weight matrix based on the permuted input...

In ccml: Consensus Clustering for Different Sample Coverage Data

Calculate consensus weight matrix based on the permuted input label matrix. Internal function used by callNCW

Description

Calculate consensus weight matrix based on the permuted input label matrix. Internal function used by callNCW

Usage

randConsensusMatrix(
  l.seed,
  l.label = label,
  l.ns = ns,
  l.nc = nc,
  l.nv = nv,
  l.index = index,
  l.pair.ind = pair.ind,
  l.ppath = ppath,
  l.plot = plot
)

Arguments

l.seed	A numerical value to set the random seed for reproducible results, 1000 random label matrix will be generated based on this seed number.
l.label	A matrix or data frame of input labels, columns=different clustering results and rows are samples.
l.ns	A integer value of number of samples, =nrow(l.label)
l.nc	A integer value of number of samples, =ncol(l.label)
l.nv	A integer vector of the number of non missing values for each column in l.label
l.index	A list of index with length of l.nc of non missing values for each column in l.label
l.pair.ind	A n-by-2 index matrix of array indices of upper triangular of l.label with non missing values
l.ppath	A character value for output directory.
l.plot	character value. NULL(default) - print to screen, 'pdf', 'png', 'pngBMP' for bitmap png, helpful for large datasets, or 'pdf'.

Value

A character of finished seed.

Write a binary file of 1000 random consensus weight matrix(as a vector n-by-1, n= nrow(l.pair.ind)) with the seed l.seed, output file name: paste0("s",l.seed,"rcw").

ccml documentation built on Aug. 30, 2023, 9:07 a.m.