limmaSelection {ClassifyR} | R Documentation |
Uses a moderated t-test with empirical Bayes shrinkage to select differentially expressed features.
## S4 method for signature 'matrix' limmaSelection(measurements, classes, ...) ## S4 method for signature 'DataFrame' limmaSelection(measurements, classes, datasetName, trainParams, predictParams, resubstituteParams, ..., selectionName = "Moderated t-test", verbose = 3) ## S4 method for signature 'MultiAssayExperiment' limmaSelection(measurements, targets = NULL, ...)
measurements |
Either a |
classes |
A vector of class labels of class |
targets |
Names of data tables to be combined into a single table and used in the analysis. |
... |
Variables not used by the |
datasetName |
A name for the data set used. Stored in the result. |
trainParams |
A container of class |
predictParams |
A container of class |
resubstituteParams |
An object of class |
selectionName |
A name to identify this selection method by. Stored in the result. |
verbose |
Default: 3. A number between 0 and 3 for the amount of progress messages to give. This function only prints progress messages if the value is 3. |
This selection method looks for changes in means and uses a moderated t-test.
An object of class SelectResult
or a list of such objects, if the classifier
which was used for determining the specified performance metric made a number of prediction varieties.
Dario Strbenac
Limma: linear models for microarray data, Gordon Smyth, 2005, In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, Springer, New York, pages 397-420.
#if(require(sparsediscrim)) #{ # Genes 76 to 100 have differential expression. genesMatrix <- sapply(1:25, function(sample) c(rnorm(100, 9, 2))) genesMatrix <- cbind(genesMatrix, sapply(1:25, function(sample) c(rnorm(75, 9, 2), rnorm(25, 14, 2)))) classes <- factor(rep(c("Poor", "Good"), each = 25)) colnames(genesMatrix) <- paste("Sample", 1:ncol(genesMatrix)) rownames(genesMatrix) <- paste("Gene", 1:nrow(genesMatrix)) resubstituteParams <- ResubstituteParams(nFeatures = seq(10, 100, 10), performanceType = "balanced error", better = "lower") selected <- limmaSelection(genesMatrix, classes, "Example", trainParams = TrainParams(), predictParams = PredictParams(), resubstituteParams = resubstituteParams) selected@chosenFeatures #}