biosigner v1.0.6

Signature discovery from omics data

Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the online infrastructure for computational metabolomics.

Functions in biosigner

Name Description
biosign-class Class "biosign"
biosign Builds the molecular signature.
plot.biosign Plot method for 'biosign' signature objects
getSignatureLs Signatures selected by the models
getAccuracyMN Accuracies of the full model and the models restricted to the signatures
diaplasma Analysis of plasma from diabetic patients by LC-HRMS
predict.biosign Predict method for 'biosign' signature objects
show.biosign Show method for 'biosign' signature objects
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Last year downloads


Type Package
Date 2016-06-11
biocViews Classification, FeatureExtraction, Transcriptomics, Proteomics, Metabolomics, Lipidomics
License CeCILL
LazyLoad yes
NeedsCompilation no
Packaged 2016-05-03 10:14:01 UTC; admin-local

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