biosign,MultiDataSet-method {biosigner} | R Documentation

## Builds the molecular signature.

### Description

Main function of the 'biosigner' package. For each of the available classifiers (PLS-DA, Random Forest, and SVM), the significant features are selected and the corresponding models are built.

### Usage

    ## S4 method for signature 'MultiDataSet'
    biosign(
      x,
      y,
      seedI = NULL,
      fig.pdfC = c("none", "interactive", "myfile.pdf")[2],
      info.txtC = c("none", "interactive", "myfile.txt")[2],
      ...
    )

    ## S4 method for signature 'ExpressionSet'
    biosign(x, y, ...)

    ## S4 method for signature 'data.frame'
    biosign(x, y, ...)

    ## S4 method for signature 'matrix'
    biosign(
      x,
      y,
      methodVc = c("all", "plsda", "randomforest", "svm")[1],
      bootI = 50,
      pvalN = 0.05,
      permI = 1,
      fixRankL = FALSE,
      seedI = 123,
      plotSubC = NA,
      fig.pdfC = c("none", "interactive", "myfile.pdf")[2],
      info.txtC = c("none", "interactive", "myfile.txt")[2],
      printL = TRUE,
      plotL = TRUE,
      .sinkC = NULL,
      ...
    )

### Arguments

`x` | Numerical data frame or matrix (observations x variables), or ExpressionSet object with non empty assayData and phenoData; NAs are allowed for PLS-DA but for SVM, samples with NA will be removed
`y` | Two-level factor corresponding to the class labels, or a character indicating the name of the column of the pData to be used, when x is an ExpressionSet object
`seedI` | integer: optional seed to obtain exactly the same signature when rerunning biosigner; default is '123'; set to NULL to prevent seed setting
`fig.pdfC` | Character: File name with '.pdf' extension for the figure; if 'interactive' (default), figures will be displayed interactively; if 'none', no figure will be generated
`info.txtC` | Character: File name with '.txt' extension for the printed results (call to sink()'); if 'interactive' (default), messages will be printed on the screen; if 'none', no verbose will be generated
`...` | Currently not used.
`methodVc` | Character vector: Either one or all of the following classifiers: Partial Least Squares Discriminant Analysis ('plsda'), or Random Forest ('randomforest'), or Support Vector Machine ('svm')
`bootI` | Integer: Number of bootstaps for resampling
`pvalN` | Numeric: To speed up the selection, only variables which significantly improve the model up to two times this threshold (to take into account potential fluctuations) are computed
`permI` | Integer: Random permutation are used to assess the significance of each new variable included into the model (forward selection)
`fixRankL` | Logical: Should the initial ranking be computed with the full model only, or as the median of the ranks from the models built on the sampled dataset?
`plotSubC` | Character: Graphic subtitle
`printL` | Logical: deprecated: use the 'info.txtC' argument instead
`plotL` | Logical: deprecated: use the 'fig.pdfC' argument instead
`.sinkC` | Character: deprecated: use the 'info.txtC' argument instead

### Value

An S4 object of class 'biosign' containing the following slots: 1) 'methodVc' character vector: selected classifier(s) ('plsda', 'randomforest', and/or 'svm'), 2) 'accuracyMN' numeric matrix: balanced accuracies for the full models, and the models restricted to the 'S' and 'AS' signatures (predictions are obtained by using the resampling scheme selected with the 'bootI' and 'crossvalI' arguments), 3) 'tierMC' character matrix: contains the tier ('S', 'A', 'B', 'C', 'D', or 'E') of each feature for each classifier (features with tier 'S' have been found significant in all backward selections; features with tier 'A' have been found significant in all but the last selection, and so on), 4) modelLs list: selected classifier(s) trained on the subset restricted to the 'S' features, 5) signatureLs list: 'S' signatures for each classifier; and 6) 'AS' list: 'AS' signatures and corresponding trained classifiers, in addition to the dataset restricted to tiers 'S' and 'A' ('xMN') and the labels ('yFc')

### Author(s)

Philippe Rinaudo and Etienne Thevenot (CEA)

### See Also

`predict.biosign`, `plot.biosign`

### Examples

    ## loading the diaplasma dataset

    data(diaplasma)
    attach(diaplasma)

    ## restricting to a smaller dataset for this example

    featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500
    dataMatrix <- dataMatrix[, featureSelVl]
    variableMetadata <- variableMetadata[featureSelVl, ]

    # signature selection for all 3 classifiers
    # a bootI = 5 number of bootstraps is used for this example
    # we recommend to keep the default bootI = 50 value for your analyzes

    diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5)

    ## Application to an ExpressionSet

    diaSet <- ExpressionSet(assayData = t(dataMatrix),
                            phenoData = new("AnnotatedDataFrame",
                                            data = sampleMetadata),
                            featureData = new("AnnotatedDataFrame",
                                              data = variableMetadata),
                            experimentData = new("MIAME",
                                                 title = "diaplasma"))

    diaSign <- biosign(diaSet, "type", bootI = 5)
    diaSet <- getEset(diaSign)
    head(fData(diaSet))

    detach(diaplasma)

    ## Application to a MultiDataSet

    # Loading the 'NCI60_4arrays' from the 'omicade4' package
    data("NCI60_4arrays", package = "omicade4")
    # Selecting two of the four datasets
    setNamesVc <- c("agilent", "hgu95")
    # Creating the MultiDataSet instance
    nciMset <- MultiDataSet::createMultiDataSet()
    # Adding the two datasets as ExpressionSet instances
    for (setC in setNamesVc) {
      # Getting the data
      exprMN <- as.matrix(NCI60_4arrays[[setC]])
      pdataDF <- data.frame(row.names = colnames(exprMN),
                            cancer = substr(colnames(exprMN), 1, 2),
                            stringsAsFactors = FALSE)
      fdataDF <- data.frame(row.names = rownames(exprMN),
                            name = rownames(exprMN),
                            stringsAsFactors = FALSE)
      # Building the ExpressionSet
      eset <- Biobase::ExpressionSet(assayData = exprMN,
                                     phenoData = new("AnnotatedDataFrame",
                                                     data = pdataDF),
                                     featureData = new("AnnotatedDataFrame",
                                                       data = fdataDF),
                                     experimentData = new("MIAME",
                                                          title = setC))
      # Adding to the MultiDataSet
      nciMset <- MultiDataSet::add_eset(nciMset, eset, dataset.type = setC,
                                        GRanges = NA, warnings = FALSE)
    }
    # Restricting to the 'ME' and 'LE' cancer types
    sampleNamesVc <- Biobase::sampleNames(nciMset[["agilent"]])
    cancerTypeVc <- Biobase::pData(nciMset[["agilent"]])[, "cancer"]
    nciMset <- nciMset[sampleNamesVc[cancerTypeVc %in% c("ME", "LE")], ]
    # Summary of the MultiDataSet
    nciMset
    # Building PLS-DA models for the cancer type, and getting back the updated MultiDataSet
    nciPlsda <- ropls::opls(nciMset, "cancer", predI = 2)
    nciMset <- ropls::getMset(nciPlsda)
    # Selecting the significant features for PLS-DA, RF, and SVM classifiers, and getting back the updated MultiDataSet
    nciBiosign <- biosigner::biosign(nciMset, "cancer")
    nciMset <- biosigner::getMset(nciBiosign)

[Package biosigner version 1.14.4 Index]