selectFeatures {PAA} | R Documentation

## Select features using frequency-based or ensemble feature selection.

### Description

Performs a multivariate feature selection using frequency-based feature selection (based on RF-RFE, RJ-RFE or SVM-RFE) or ensemble feature selection (based on SVM-RFE).

### Usage

    selectFeatures(elist = NULL, n1 = NULL, n2 = NULL, label1 = "A", label2 = "B",
     log=NULL, cutoff = 10, selection.method = "rf.rfe",
     preselection.method = "mMs", subruns = 100, k = 10, subsamples = 10,
     bootstraps = 10, candidate.number = 300, above=1500, between=400,
     panel.selection.criterion="accuracy", importance.measure="MDA", ntree = 500,
     mtry = NULL, plot = FALSE, output.path = NULL, verbose = FALSE,
     method = "frequency")

### Arguments

`elist` | `EListRaw` or `EList` object containing all microarray data (mandatory).
`n1` | integer indicating the sample number in group 1 (mandatory).
`n2` | integer indicating the sample number in group 2 (mandatory).
`label1` | class label of group 1 (default: "A").
`label2` | class label of group 2 (default: "B").
`log` | indicates whether the data is in log scale (mandatory; note: if TRUE log2 scale is expected).
`cutoff` | integer indicating how many features will be selected (default: 10).
`selection.method` | string indicating the feature selection method: `"rf.rfe"` (default), `"svm.rfe"` or `"rj.rfe"`. Has no effect when `method="ensemble"`.
`preselection.method` | string indicating the feature preselection method: `"mMs"` (default), `"tTest"`, `"mrmr"` or `"none"`. Has no effect when `method="ensemble"`.
`subruns` | integer indicating the number of resampling repeats to be performed (default: 100). Has no effect when `method="ensemble"`.
`k` | integer indicating the number of k-fold cross validation subsets (default: 10, i.e., 10-fold CV).
`subsamples` | integer indicating the number of subsamples for ensemble feature selection (default: 10). Has no effect when `method="frequency"`.
`bootstraps` | integer indicating the number of bootstrap samples for ensemble feature selection (default: 10). Has no effect when `method="frequency"` only.
`candidate.number` | integer indicating how many features shall be preselected. Default is `"300"`. Has no effect when `method="ensemble"`.
`above` | mMs above parameter (integer). Default is `"1500"`. There will be no effect when `method="ensemble"`.
`between` | mMs between parameter (integer). Default is `"400"`. There will be no effect when `method="ensemble"`.
`panel.selection.criterion` | indicating the panel selection criterion: `"accuracy"` (default), `"sensitivity"` or `"specificity"`. No effect for `method="ensemble"`.
`importance.measure` | string indicating the random forest importance measure: `"MDA"` (default) or `"MDG"`. Has no effect when `method="ensemble"`.
`ntree` | random forest parameter ntree (default: `"500"`). There will be no effect when `method="ensemble"`.
`mtry` | random forest parameter mtry (default: `sqrt(p)` where p is the number of predictors). Has no effect when `method="ensemble"`.
`plot` | logical indicating whether performance plots shall be plotted (default: FALSE).
`output.path` | string indicating the results output folder (optional).
`verbose` | logical indicating whether additional information shall be printed to the console (default: FALSE).
`method` | the feature selection method: "frequency" (default) for frequency-based or "ensemble" for ensemble feature selection.

### Details

This function takes an `EListRaw` or `EList` object, group-specific sample numbers, group labels and parameters choosing and configuring a multivariate feature selection method (frequency-based or ensemble feature selection) to select a panel of differential features. When an output path is defined (via `output.path`) results will be saved on the hard disk and when `verbose` is TRUE additional information will be printed to the console.

Frequency-based feature selection (`method="frequency"`): The whole data is splitted in k cross validation training and test set pairs. For each training set a multivariate feature selection procedure is performed. The resulting k feature subsets are tested using the corresponding test sets (via classification). As a result, `selectFeatures()` returns the average k-fold cross validation classification accuracy as well as the selected feature panel (i.e., the union set of the k particular feature subsets). As multivariate feature selection methods random forest recursive feature elimination (RF-RFE), random jungle recursive feature elimination (RJ-RFE) and support vector machine recursive feature elimination (SVM-RFE) are supported. To reduce running times, optionally, univariate feature preselection can be performed (control via `preselection.method`). As univariate preselection methods mMs (`"mMs"`), Student's t-test (`"tTest"`) and mRMR (`"mrmr"`) are supported. Alternatively, no preselection can be chosen (`"none"`). This approach is similar to the method proposed in Baek et al.

Ensemble feature selection (`method="ensemble"`): From the whole data the previously defined number of subsamples is drawn defining pairs of training and test sets. Moreover, for each training set a previously defined number of bootstrap samples is drawn. Then, for each bootstrap sample SVM-RFE is performed and a feature ranking is obtained. To obtain a final ranking for a particular training set, all associated bootstrap rankings are aggregated to a single ranking. To score the `cutoff` best features, for each subsample a classification of the test set is performed (using a svm trained with the `cutoff` best features from the training set) and the classification accuracy is determined. Finally, the stability of the subsample-specific panels is assessed (via Kuncheva index, Kuncheva LI, 2007), all subsample-specific rankings are aggregated, the top n features (defined by `cutoff`) are selected, the average classification accuracy is computed, and all these results are returned in a list. This approach has been proposed in Abeel et al.

### Value

If `method` is `"frequency"`, the results list contains the following elements:

`accuracy` | average k-fold cross validation accuracy.
`sensitivity` | average k-fold cross validation sensitivity.
`specificity` | average k-fold cross validation specificity.
`features` | selected feature panel.
`all.results` | complete cross validation results.

If `method` is `"ensemble"`, the results list contains the following elements:

`accuracy` | average accuracy regarding all subsamples.
`sensitivity` | average sensitivity regarding all subsamples.
`specificity` | average specificity regarding all subsamples.
`features` | selected feature panel.
`all.results` | all feature ranking results.
`stability` | stability of the feature panel (i.e., Kuncheva index for the subrun-specific panels).

### Author(s)

Michael Turewicz, michael.turewicz@rub.de

### References

Baek S, Tsai CA, Chen JJ.: Development of biomarker classifiers from high- dimensional data. Brief Bioinform. 2009 Sep;10(5):537-46.

Abeel T, Helleputte T, Van de Peer Y, Dupont P, Saeys Y: Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics. 2010 Feb 1;26(3):392-8.

Kuncheva, LI: A stability index for feature selection. Proceedings of the IASTED International Conference on Artificial Intelligence and Applications. February 12-14, 2007. Pages: 390-395.

### Examples

    cwd <- system.file(package="PAA")
    load(paste(cwd, "/extdata/Alzheimer.RData", sep=""))
    elist <- elist[elist$genes$Block < 10,]

    c1 <- paste(rep("AD",20), 1:20, sep="")
    c2 <- paste(rep("NDC",20), 1:20, sep="")

    pre.sel.results <- preselect(elist=elist, columns1=c1, columns2=c2, label1="AD",
     label2="NDC", log=FALSE, discard.threshold=0.1, fold.thresh=1.9,
     discard.features=TRUE, method="tTest")
    elist <- elist[-pre.sel.results$discard,]

    selectFeatures.results <- selectFeatures(elist, n1=20, n2=20, label1="AD",
        label2="NDC", log=FALSE, subsamples=2, bootstraps=1, candidate.number=20,
        method="ensemble")

[Package PAA version 1.21.0 Index]