plotDiscriminantPower: Plots the genes' Discriminant Power.

In geNetClassifier: Classify diseases and build associated gene networks using gene expression profiles

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")

geNetClassifier documentation built on Nov. 8, 2020, 4:53 p.m.