BootstrapGenes: Consensus clustering based on BootstrapClusterTest from OOMPA

In nickytong/GenAnalysis: Genomic Analysis

Description

Bootstrap genes to calculate a consensus matrix

Usage

BootstrapGenes(data, FUN, subsetSize, nTimes = 100, verbose = TRUE, ...)

Arguments

data	A data matrix, numerical data frame, or ExpressionSet object.
FUN	A function that, given a data matrix, returns a vector of cluster assignments based on the columns Examples of functions with this behavior are cutHclust, cutKmeans, cutPam, and cutRepeatedKmeans.
subsetSize	An optional integer argument used to select a subset of genes. If present, each iteration of the bootstrap selects subsetSize rows from the original data matrix. If missing, each bootstrap contains the same number of rows as the original data matrix.
nTimes	The number of bootstrap samples to collect.
verbose	A logical flag
...	Additional arguments passed to the classifying function, FUN.

Details

There are generally two doable approach to obtain a consensus matrix for clustering samples: (1) subsampling samples. (2) bootstrap genes. Bootstrap samples is not recommended since when a sample is sampled multiple times, it will form a cluster by itself. This function applies bootstrap on the genes (rows of a matrix) so as to perturb the data when generating a consensus matrix

nickytong/GenAnalysis documentation built on July 20, 2019, 8:57 a.m.