GeNetClassifierReturn-class {geNetClassifier} | R Documentation

## Class "GeNetClassifierReturn"

### Description

Object wich wraps all the items returned by `geNetClassifier`. It usually contains the classifier, the genes ranking and information, the network and any other requested statistics.

### Methods

names

  `signature(x = "GeNetClassifierReturn")`: Shows the available slots in the object.

overview

  `signature(object = "GeNetClassifierReturn")`: Shows an overview of all the slots in the object.

### Slots

Available slots deppends on the arguments used to call `geNetClassifier()`:

`call`:

  call. Always available.

`classifier`:

  list. SVM classifier. Only available if geNetClassifier() was called with option `buildClassifier=TRUE` (default settings).

`classificationGenes`:

  `GenesRanking`. Genes used to train the classifier. Only available if geNetClassifier() was called with option `buildClassifier=TRUE` (default settings).

`generalizationError`:

  `GeneralizationError`. Statistics calculated for the current training set and options.
  Only available if geNetClassifier() was called with option `estimateGError=TRUE` (False by default).

`genesRanking`:

  `GenesRanking`. Whole genes ranking (if `returnAllGenesRanking=TRUE`) or significant genes ranking (if `returnAllGenesRanking=FALSE`, includes only the genes with posterior probability over `lpThreshold`)

`genesRankingType`:

  character. "all", "significant" or "significantNonRedundant"

`genesNetwork`:

  List of `GenesNetwork`. Only available if geNetClassifier() was called with option `calculateNetwork=TRUE` (default settings).

`genesNetworkType`:

  character. At the moment, only "topGenes" available.

### 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`
`plot.GeNetClassifierReturn`

### 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

    ######
    # Explore the returned object
    ######
    # Global view of the object and its structure:
    leukemiasClassifier
    names(leukemiasClassifier)

    ### Depending on the available slots:
    # Call and acess to the classifier:
    leukemiasClassifier@call
    leukemiasClassifier@classifier

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

    # Generalization Error estimated by cross-validation:
    leukemiasClassifier@generalizationError
    overview(leukemiasClassifier@generalizationError)
    # i.e. probabilityMatrix:
    leukemiasClassifier@generalizationError@probMatrix
    # i.e. statistics of the genes chosen in any of the CV loops for for AML:
    leukemiasClassifier@generalizationError@classificationGenes.stats$AML

    # List of Networks by classes:
    leukemiasClassifier@genesNetwork
    # Access to the nodes or edges of each network:
    	getEdges(leukemiasClassifier@genesNetwork$AML)
    	getNodes(leukemiasClassifier@genesNetwork$AML)

    # Genes ranking:
    leukemiasClassifier@genesRanking
    	# Number of available genes in the ranking:
    	numGenes(leukemiasClassifier@genesRanking)
    	# Number of significant genes
    	# (genes with posterior probability over lpThreshold, default=0.95)
    	numSignificantGenes(leukemiasClassifier@genesRanking)
    	# Top 10 genes of CML:
    	genesDetails(leukemiasClassifier@genesRanking)$CML[1:10,]
    	# To get a sub ranking with the top 10 genes:
    	getTopRanking(leukemiasClassifier@genesRanking, 10)
    	# Genes details of the top 10 genes:
    	genesDetails(getTopRanking(leukemiasClassifier@genesRanking, 10))

[Package geNetClassifier version 1.34.0 Index]