Synthesis of microarray-based classification

Description

The aim of the package is to provide a user-friendly environment for the evaluation of classification methods using gene expression data. A strong focus is on combined variable selection, hyperparameter tuning, evaluation, visualization and comparison of (up to now) 21 classification methods from three main fields: Discriminant Analysis, Neural Networks and Machine Learning. Although the package has been created with the intention to be used for Microarray data, it can as well be used in various (p > n)-scenarios.

Details

Package:	CMA
Type:	Package
Version:	1.3.3
Date:	2009-9-14
License:	GPL (version 2 or later)

Most Important Steps for the workflow are:

1.

Generate evaluation datasets using GenerateLearningsets

2.

(Optionally): Perform variable selection using GeneSelection

3.

(Optionally): Peform hyperparameter tuning using tune

4.

Perform classification using 1.-3.

5.

Repeat 2.-4. based on 1. for several methods: compBoostCMA, dldaCMA, ElasticNetCMA, fdaCMA, flexdaCMA, gbmCMA, knnCMA, ldaCMA, LassoCMA, nnetCMA, pknnCMA, plrCMA, pls_ldaCMA, pls_lrCMA, pls_rfCMA, pnnCMA, qdaCMA, rfCMA, scdaCMA, shrinkldaCMA, svmCMA

6.

Evaluate the results from 5. using evaluation and make a comparison by calling compare

Author(s)

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

Christoph Bernau bernau@ibe.med.uni-muenchen.de

Maintainer: Christoph Bernau bernau@ibe.med.uni-muenchen.de.

References

Slawski, M. Daumer, M. Boulesteix, A.-L. (2008) CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9: 439