geNetClassifier {geNetClassifier} | R Documentation

## Main function of the geNetClassifier package.
Trains the multi-class SVM classifier based on the given gene expression data through transparent detection of gene markers and their associated networks.

### Description

Allows to train the classifier, calculate the genes network...

### Usage

    geNetClassifier(eset, sampleLabels, plotsName = NULL,
    buildClassifier = TRUE, estimateGError = FALSE,
    calculateNetwork = TRUE, labelsOrder = NULL, geneLabels = NULL,
    numGenesNetworkPlot = 100,
    minGenesTrain = 1, maxGenesTrain = 100, continueZeroError = FALSE,
    numIters = 6, lpThreshold = 0.95, numDecimals = 3,
    removeCorrelations = FALSE, correlationsThreshold = 0.8,
    correlationMethod = "pearson",
    removeInteractions = FALSE, interactionsThreshold = 0.5,
    minProbAssignCoeff = 1, minDiffAssignCoeff = 0.8,
    IQRfilterPercentage = 0, skipInteractions = TRUE,
    precalcGenesNetwork = NULL, precalcGenesRanking = NULL,
    returnAllGenesRanking = TRUE, kernel="linear", verbose=TRUE, ...)

### Arguments

`eset` | ExpressionSet or matrix. Gene expression of the train samples (positive & non-logaritmic normalized values).
`sampleLabels` | Character. PhenoData variable (column name) containing the train samples class labels. Matrix or Factor. Class labels of the train samples.
`labelsOrder` | Vector or Factor. Order in which the labels should be shown in the returned results and plots.
`plotsName` | Character. File name with which the plots should be saved. If not provided, no plots will be drawn.
`buildClassifier` | Logical. If TRUE trains a classifier with the given samples.
`estimateGError` | Logical. If TRUE uses cross-validation to estimate the Generalization Error of a classiffier trained with the given samples.
`calculateNetwork` | Logical. If TRUE calculates the coexpression network between the best genes.
`geneLabels` | Vector or Matrix. Gene name, ID or label which should be shown in the returned results and plots.
`numGenesNetworkPlot` | Integer. Number of genes to show in the coexpression network for each class.
`minGenesTrain` | Integer. Maximum number of genes per class to train the classifier with.
`maxGenesTrain` | Integer. Maximum number of genes per class to train the classifier with.
`continueZeroError` | Logical. If TRUE, the program will continue testing combinations with more genes even if error 0 has been reached.
`numIters` | Integer. Number of iterations to determine the optimum number of genes (between `minGenesTrain` and `maxGenesTrain`).
`lpThreshold` | Numeric between 0 and 1. Required posterior probability value to consider a gene 'significant'.
`removeCorrelations` | Logical. If TRUE, no correlated genes will be chosen to train the classifier.
`correlationsThreshold` | Numeric between 0 and 1. Minimum Pearson's correlation coefficient to consider genes correlated.
`correlationMethod` | "pearson", "kendall" or "spearman". Type of correlation to calculate between genes.
`removeInteractions` | Logical. If TRUE, genes with Mutual Information coefficient over the threshold will not be chosen to train the classifier.
`interactionsThreshold` | Numeric between 0 and 1. Minimum Mutual Information coefficient to consider two genes equivalent.
`numDecimals` | Integer. Number of decimals to show in the statistics.
`minProbAssignCoeff` | Numeric. Allows modifying the required probability to assign a sample to a class in the internal crossvalidation. For details see: `queryGeNetClassifier`
`minDiffAssignCoeff` | Numeric. Allows modifying the difference of probabilities required between the most likely class and second most likely class to assign a sample. For details see: `queryGeNetClassifier`
`IQRfilterPercentage` | Integer. InterQuartile Range (IQR) filter applied to the initial data. Not recommended for more than two classes.
`skipInteractions` | Logical. If TRUE, the interactions between genes are not calculated (they will not appear on the genes network). Saves some execution time. Only available if `removeInteractions=FALSE`.
`precalcGenesNetwork` | `GenesNetwork` from a previous execution with the same expression data and parameters.
`precalcGenesRanking` | `GenesRanking` from a previous execution with the same expression data and parameters.
`returnAllGenesRanking` | Logical. If TRUE, returns the whole genes ranking. If FALSE the returned ranking contains only the significant genes (genes over lpThreshold).
`verbose` | Logical. If TRUE, messages indicating the execution progress will be shown.
`kernel` | Character. Type of SVM kernel. Default: "linear",
`...` | Other arguments to pass to the `svm` function.

### Value

A `GeNetClassifierReturn` object containing the classifier and the genes chosen to train it (`classificationGenes`), Cross-Validation statistics, the whole `GenesRanking` and each class' `GenesNetwork` (if requested). Several plots saved as '`plotsName_....pdf`' in the working directory.

### Author(s)

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

### References

Packages used by this function:
`EBarrays: emfit` (Implements EM algorithm for gene expression mixture model) and ebPatterns, for calculating the gene ranking.
Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski (2007). EBarrays: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification. R package.
`e1071: svm`.
Evgenia Dimitriadou, Kurt Hornik, Friedrich Leisch, David Meyer and Andreas Weingessel (2011). e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. R package.
http://CRAN.R-project.org/package=e1071
`ipred: kfoldcv` (computes feasible sample sizes for the k groups in k-fold cv) for the cross-validations.
Andrea Peters and Torsten Hothorn (2012). ipred: Improved Predictors. R package. http://CRAN.R-project.org/package=ipred
`minet` for the Mutual Information network.
Patrick E. Meyer, Frederic Lafitte and Gianluca Bontempi (2008). MINET: An open source R/Bioconductor Package for Mutual Information based Network Inference. BMC Bioinformatics.
http://www.biomedcentral.com/1471-2105/9/461
`RColorBrewer` (`brewer.pal`) for palettes in some of the plots.
Erich Neuwirth (2011). RColorBrewer: ColorBrewer palettes. R package.
http://CRAN.R-project.org/package=RColorBrewer
`igraph` for the graphical representation of the networks.
Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. http://igraph.sf.net

### See Also

To query the classifier: `queryGeNetClassifier`
All functions in the package: `geNetClassifier-package`

### Examples

    ########
    # Load libraries and training data
    ########

    # 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])

    ########
    # Training
    ########

    # NOTE: Training the classifier takes a while...
    # Choose ONE of the followings, or modify to suit your needs:
    ## Not run:

    # "Basic" execution: All default parameters
    leukemiasClassifier <- geNetClassifier(eset=leukemiasEset[,trainSamples],
        sampleLabels="LeukemiaType", plotsName="leukemiasClassifier")

    # All default parameters also estimatings the classiffier's Generalization Error:
    # ( by default:  buildClassifier=TRUE, calculateNetwork=TRUE)
    # Takes longer time than the basic execution
    leukemiasClassifier <- geNetClassifier(eset=leukemiasEset[,trainSamples],
        sampleLabels="LeukemiaType", plotsName="leukemiasClassifier",
        estimateGError=TRUE)

    # Faster execution (few minutes - depending on the computer):
    # By skipping the calculation of the interactions (MI) betwen the genes,
    # and reducing the number of genes to explore when training the classifier
    # (100 by default), the execution time can be sightly reduced
    leukemiasClassifier <- geNetClassifier(eset=leukemiasEset[,trainSamples],
    sampleLabels="LeukemiaType", plotsName="leukemiasClassifier",
    skipInteractions= TRUE, maxGenesTrain=20)

    # To any of these examples, you can add/remove the argument geneLabels,
    # in order to show/remove the gene name in the rankings and plots:
    # The argument labelsOrder allows showing the classes in a specific order
    # i.e.: labelsOrder=c("ALL","CLL","AML",CML","NoL")

    save(leukemiasClassifier, file="leukemiasClassifier.RData")  # Save execution result
    # For loading the saved object in the future...
    # (If it doesn't find it, use getwd() to make sure you are in the right directory)
    #load("leukemiasClassifier.RData")

    # To avoid having to train a classifier to continue learning to use the package,
    # you can load the package's pre-executed example:
    data(leukemiasClassifier)
    #This example classifier was trained with the following code:
    #leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples],
    #    "LeukemiaType", plotsName="leukemiasClassifier", buildClassifier=TRUE,
    #    estimateGError=TRUE, calculateNetwork=TRUE, geneLabels=geneSymbols)

    ########
    # Explore the returned object:
    ########
    names(leukemiasClassifier)
    # More details on the class' help file:
    ?GeNetClassifierReturn

    # Further options:
    # The trained classifier can be used to find the class of new samples:
    ?queryGeNetClassifier

    # The default plots can be modified and presonalized to fit the user needs:
    ?calculateGenesRanking
    ?plotNetwork
    ?plotDiscriminantPower
    ?plotExpressionProfiles

    ## End(Not run)

[Package geNetClassifier version 1.26.0 Index]