CMA-package: 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
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