biosign {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

    biosign(x, ...)

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

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

    ## S4 method for signature 'matrix'
    biosign(x, y, methodVc = c("all", "plsda", "randomforest",
      "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE,
      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
`...` | Currently not used.
`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
`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?
`printL` | Logical: Should informations regarding the data set and the model be printed? [default = TRUE]
`plotL` | Logical: Should the 'summary' plot be displayed? [default = TRUE]
`.sinkC` | Character: Name of the file for R output diversion [default = NULL: no diversion]; Diversion of messages is required for the integration into Galaxy

### 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

    set.seed(123)
    diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5)

    detach(diaplasma)

[Package biosigner version 1.10.0 Index]