plotDiscriminantPower {geNetClassifier} | R Documentation

## Plots the genes' Discriminant Power.

### Description

Calculates and plots the Discriminant Power of the genes in the given classifier.

### Usage

    plotDiscriminantPower(classifier, classificationGenes = NULL,
    geneLabels = NULL, classNames = NULL, plotDP = TRUE,
    fileName = NULL, returnTable = FALSE, verbose = TRUE)

### Arguments

`classifier` | Classifier returned by `geNetClassifier`. (@classifier)
`classificationGenes` | Vector or Matrix. IDs of the genes to plot. If matrix: genes should be ordered by classes. Columns should be named after the classes.
`geneLabels` | Vector or Matrix. Gene name, ID or label which should be shown in the returned results and plots.
`classNames` | Named vector. Short version of the class names if different from the ones used to train the classifier.
`plotDP` | Logical. If TRUE, plots the discriminant power of the given genes.
`fileName` | Character. File name to save the plot with. If not provided, the plots will be shown through the standard output device.
`returnTable` | Logical. If TRUE, returns a table with the genes discriminant power.
`verbose` | Logical. If TRUE, messages indicating the execution progress will be printed on screen.

### Details

The Discriminant Power represents the weight the (SVM) classifier gives each gene to separate the classes. It is calculated based on the coordinates of the support vectors. Genes with a high Discriminant Power are better for identifying samples from the class.

### Value

- Data frame Optional. Data.frame containing the genes and their Discriminant Power.

- Discriminant Power plot Optional. Shown throught the standard output devide or saved in the working directory as 'fileName.pdf' if `fileName` was provided.

### Author(s)

Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain

### See Also

Main package function and classifier training: `geNetClassifier`

### Examples

    ######
    # Load data and train a classifier
    ######

    # Load an expressionSet:
    library(leukemiasEset)
    data(leukemiasEset)

    # Select the train samples:
    trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58)
    # summary(leukemiasEset$LeukemiaType[trainSamples])

    # Train a classifier or load a trained one:
    # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples],
    #    sampleLabels="LeukemiaType", plotsName="leukemiasClassifier")
    data(leukemiasClassifier) # Sample trained classifier

    ######
    # Discriminant Power
    ######
    # Default (plots up to 20 genes)
    plotDiscriminantPower(leukemiasClassifier)
    # Plot a specific gene:
    plotDiscriminantPower(leukemiasClassifier, classificationGenes="ENSG00000169575")
    # Plot top5 genes of a class, and return their discriminant power:
    # Note: The discriminant Power can only be calculated for 'classificationGenes'
    #            (genes chosen for training the classifier)
    genes <- getRanking(leukemiasClassifier@classificationGenes,
        showGeneID=TRUE)$geneID[1:5,"AML",drop=FALSE] # Top 5 genes of AML
    discPowerTable2 <- plotDiscriminantPower(leukemiasClassifier,
        classificationGenes=genes, returnTable=TRUE)

    # For plotting more than 20 genes or saving the plots as .pdf, provide a fileName
    plotDiscriminantPower(leukemiasClassifier,
         fileName="leukemiasClassifier_DiscriminantPower.pdf")

[Package geNetClassifier version 1.28.0 Index]