K2tax: Function K2 Taxonomer

In montilab/K2Taxonomer: Creating annotated submodules of high-throughput data through recursive partitioning

Function K2 Taxonomer

Description

This function performs K2 Taxonomer procedure only. Arguments values are extracted from K2meta(K2res) unless othewise specified.

Usage

K2tax(
  K2res,
  nFeats = NULL,
  featMetric = NULL,
  recalcDataMatrix = NULL,
  nBoots = NULL,
  clustFunc = NULL,
  clustCors = NULL,
  clustList = NULL,
  linkage = NULL,
  oneoff = NULL,
  stabThresh = NULL
)

Arguments

K2res	An object of class K2. The output of K2preproc().
nFeats	A numeric value <= P of subsets of the data to use.
featMetric	Metric to use to assign variance/signal score. Options are 'square' (default), 'mad' to use MAD scores, 'sd' to use standard deviation
recalcDataMatrix	Recalculate dataMatrix for each partion?
nBoots	A numeric value of the number of bootstraps to run at each split.
clustFunc	Wrapper function to cluster a P x N (See details).
clustCors	Number of cores to use for clustering.
clustList	List of objects to use for clustering procedure.
linkage	Linkage criteria for splitting cosine matrix ('method' in hclust).
oneoff	Logical. Allow 1 member clusters?
stabThresh	A numeric value < 1, to set stopping threshold (use any negative value for no threshold).

Value

An object of class K2.

References

\insertRef

reed_2020K2Taxonomer

Examples

## Read in ExpressionSet object
library(Biobase)
data(sample.ExpressionSet)

## Pre-process and create K2 object
K2res <- K2preproc(sample.ExpressionSet)

## Run K2 Taxonomer algorithm
K2res <- K2tax(K2res,
            stabThresh=0.5)

montilab/K2Taxonomer documentation built on Nov. 8, 2024, 2:36 a.m.