GeNetClassifierReturn-class {geNetClassifier} | R Documentation |
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.
signature(x = "GeNetClassifierReturn")
: Shows the available slots in the object.
signature(object = "GeNetClassifierReturn")
: Shows an overview of all the slots in the object.
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.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Main package function and classifier training: geNetClassifier
plot.GeNetClassifierReturn
###### # 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))