GenesRanking-class {geNetClassifier} | R Documentation |
Contains a genes ranking and the genes info calculated by geNetClassifier
.
(Slots @classificationGenes
and @genesRanking
from geNetClassifier
output)
signature(object = "GenesRanking")
: Returns data.frames with information about the genes.
signature(object = "GenesRanking")
: Returns a matrix containing the ranked genes.
signature(object = "GenesRanking", numGenesClass)
: Returns a new GenesRanking object containing only the top genes of each class.
signature(object = "GenesRanking")
: Returns the classes for which the genes are ranked.
signature(object = "GenesRanking")
: Returns the number of available ranked genes per class.
signature(object = "GenesRanking")
: Returns the number of significant genes per class (genes over the given posterior probability threshold).
signature(x = "GenesRanking", y = "missing")
: Plots the genes' posterior probability. Wrapper of calculateGenesRanking
.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
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
###### # 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)