biosigner-package {biosigner} | R Documentation

## Molecular signature discovery from omics data

### Description

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 Workflow4metabolomics.org online infrastructure for computational metabolomics.

### Author(s)

Philippe Rinaudo <phd.rinaudo@gmail.com> and Etienne Thevenot <etienne.thevenot@cea.fr>.

Maintainer: Philippe Rinaudo <phd.rinaudo@gmail.com>

### Examples

    ## loading the diaplasma dataset

    data(diaplasma)
    attach(diaplasma)

    ## restricting to a smaller dataset for this example

    featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500
    dataMatrix <- dataMatrix[, featureSelVl]
    variableMetadata <- variableMetadata[featureSelVl, ]

    ## signature selection for all 3 classifiers
    ## a bootI = 5 number of bootstraps is used for this example
    ## we recommend to keep the default bootI = 50 value for your analyzes

    set.seed(123)
    diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5)

    detach(diaplasma)

[Package biosigner version 1.22.0 Index]