GenesRanking-class {geNetClassifier} | R Documentation

## Class "GenesRanking"

### Description

Contains a genes ranking and the genes info calculated by `geNetClassifier`.
(Slots `@classificationGenes` and `@genesRanking` from `geNetClassifier` output)

### Methods

genesDetails

  `signature(object = "GenesRanking")`: Returns data.frames with information about the genes.

getRanking

  `signature(object = "GenesRanking")`: Returns a matrix containing the ranked genes.

getTopRanking

  `signature(object = "GenesRanking", numGenesClass)`: Returns a new GenesRanking object containing only the top genes of each class.

gClasses

  `signature(object = "GenesRanking")`: Returns the classes for which the genes are ranked.

numGenes

  `signature(object = "GenesRanking")`: Returns the number of available ranked genes per class.

numSignificantGenes

  `signature(object = "GenesRanking")`: Returns the number of significant genes per class (genes over the given posterior probability threshold).

plot

  `signature(x = "GenesRanking", y = "missing")`: Plots the genes' posterior probability. Wrapper of `calculateGenesRanking`.

### Author(s)

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

### See Also

For more information on how the ranking is calculated and how to interpret the given information, see the package vignette.

Main package function and classifier training: `geNetClassifier`
Plot the ranking genes's posterior probability: `plot.GenesRanking`

### Examples

    ######
    # Calculate a genesRanking
    ######

    # 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])

    # Calculate the genesRanking with calculateGenesRanking()
    ## Not run:
    genesRanking <- calculateGenesRanking(leukemiasEset[,trainSamples],
        sampleLabels="LeukemiaType", returnRanking="full")
    ## End(Not run)

    # geNetClassifier() also calculates a genes ranking
    # Sample output:
    data(leukemiasClassifier)
    genesRanking <- leukemiasClassifier@genesRanking

    ######
    # Exploring the rankings
    ######
    # Number of available genes in the ranking:
    numGenes(genesRanking)

    # Number of significant genes (genes with posterior probability over the threshold.
    # Default: lpThreshold=0.95):
    numSignificantGenes(genesRanking)

    # Top 10 genes of CML:
    genesDetails(genesRanking)$CML[1:10,]

    # To get a sub ranking with the top 10 genes:
    getTopRanking(genesRanking, 10)

    # Genes details of the top 10 genes:
    genesDetails(getTopRanking(genesRanking, 10))

    ######
    # Exploring the genes used for training the classifier
    ######
    numGenes(leukemiasClassifier@classificationGenes)
    leukemiasClassifier@classificationGenes
    #genesDetails(leukemiasClassifier@classificationGenes)  # List by classes
    genesDetails(leukemiasClassifier@classificationGenes)$AML # Show a class genes
    # If your R console wraps the table rows, try widening your display width:
    # options(width=200)

    ######
    # Creating a GenesRanking object
    # i.e. To use geNetClassifier() with a ranking based on another algorithm
    ######

    ### 1. Calculate gene scores
    # Two classes:
    geneScore <- matrix(sample(seq(0,1,by=0.01), size=100, replace=TRUE))
    colnames(geneScore) <- "BothClasses"
    rownames(geneScore) <- paste("Gene", 1:100, sep="")

    # More than two classes:
    geneScore <- matrix(sample(seq(0,1,by=0.01), size=300, replace=TRUE), ncol=3)
    colnames(geneScore) <- paste("Class", 1:3, sep="")
    rownames(geneScore) <- paste("Gene", 1:100, sep="")

    ### 2. Create object
    postProb <- geneScore
    ord <- apply(postProb, 2, function(x) order(x, decreasing=TRUE))
    numGenesClass <- apply(postProb, 2, function(x) sum(!is.na(x)))
    customRanking <- new("GenesRanking", postProb=postProb, ord=ord, numGenesClass=numGenesClass)

    # GenesRanking object ready:
    customRanking
    genesDetails(customRanking)
    customRanking@numGenesClass
    numSignificantGenes(customRanking)

    # geNetClassifier(..., precalcGenesRanking = customRanking)

[Package geNetClassifier version 1.22.0 Index]