plot,oplsMultiDataSet,ANY-method {ropls} | R Documentation

## Plot Method for (O)PLS(-DA)

### Description

This function plots values based upon a model trained by `opls`.

This function plots values based upon a model trained by `opls`.

### Usage

    ## S4 method for signature 'oplsMultiDataSet,ANY'
    plot(
      x,
      y,
      fig.pdfC = c("none", "interactive", "myfile.pdf")[2],
      info.txtC = c("none", "interactive", "myfile.txt")[2],
      ...
    )

    ## S4 method for signature 'opls,ANY'
    plot(
      x,
      y,
      typeVc = c("correlation", "outlier", "overview", "permutation", "predict-train",
        "predict-test", "summary", "x-loading", "x-score", "x-variance", "xy-score",
        "xy-weight")[7],
      parAsColFcVn = NA,
      parCexN = 0.8,
      parCompVi = c(1, 2),
      parEllipsesL = NA,
      parLabVc = NA,
      parPaletteVc = NA,
      parTitleL = TRUE,
      parCexMetricN = NA,
      plotPhenoDataC = NA,
      plotSubC = NA,
      fig.pdfC = c("none", "interactive", "myfile.pdf")[2],
      info.txtC = c("none", "interactive", "myfile.txt")[2],
      file.pdfC = NULL,
      .sinkC = NULL,
      ...
    )

### Arguments

`x` | An S4 object of class `opls` or `oplsMultiDataSet`, created by the `opls` function.
`y` | Currently not used
`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.
`typeVc` | Character vector: the following plots are available: 'correlation': Variable correlations with the components, 'outlier': Observation diagnostics (score and orthogonal distances), 'overview': Model overview showing R2Ycum and Q2cum (or 'Variance explained' for PCA), 'permutation': Scatterplot of R2Y and Q2Y actual and simulated models after random permutation of response values; 'predict-train' and 'predict-test': Predicted vs Actual Y for reference and test sets (only if Y has a single column), 'summary' [default]: 4-plot summary showing permutation, overview, outlier, and x-score together, 'x-variance': Spread of raw variables corresp. with min, median, and max variances, 'x-loading': X-loadings (the 6 of variables most contributing to loadings are colored in red to facilitate interpretation), 'x-score': X-Scores, 'xy-score': XY-Scores, 'xy-weight': XY-Weights
`parAsColFcVn` | Optional factor character or numeric vector to be converted into colors for the score plot; default is NA [ie colors will be converted from 'y' in case of (O)PLS(-DA) or will be 'black' for PCA]
`parCexN` | Numeric: amount by which plotting text should be magnified relative to the default
`parCompVi` | Integer vector of length 2: indices of the two components to be displayed on the score plot (first two components by default)
`parEllipsesL` | Should the Mahalanobis ellipses be drawn? If 'NA' [default], ellipses are drawn when either a character parAsColVcn is provided (PCA case), or when 'y' is a character factor ((O)PLS-DA cases).
`parLabVc` | Optional character vector for the labels of observations on the plot; default is NA [ie row names of 'x', if available, or indices of 'x', otherwise, will be used]
`parPaletteVc` | Optional character vector of colors to be used in the plots
`parTitleL` | Should the titles of the plots be printed on the graphics (default = TRUE); It may be convenient to set this argument to FALSE when the user wishes to add specific titles a posteriori
`parCexMetricN` | Numeric: magnification of the metrics at the bottom of score plot (default -NA- is 1 in 1x1 and 0.7 in 2x2 display)
`plotPhenoDataC` | Character: if x was generated from an ExpressionSet (i.e. if the 'eset' slot from x is not NULL), the name of the pData(x) column to be used for coloring can be specified here (instead of 'parAsColFcVn')
`plotSubC` | Character: Graphic subtitle
`file.pdfC` | Character: deprecated; use the 'fig.pdfC' argument instead
`.sinkC` | Character: deprecated; use the 'info.txtC' argument instead

### Examples

    # 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)
    }
    # Summary of the MultiDataSet
    nciMset
    # Principal Component Analysis of each data set
    nciPca <- ropls::opls(nciMset)
    # Coloring the Score plot according to cancer types
    ropls::plot(nciPca, y = "cancer", typeVc = "x-score")
    # 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")], ]
    # Building PLS-DA models for the cancer type
    nciPlsda <- ropls::opls(nciMset, "cancer", predI = 2)

    data(sacurine)
    attach(sacurine)

    for(typeC in c("correlation", "outlier", "overview",
                   "permutation", "predict-train","predict-test",
                   "summary", "x-loading", "x-score", "x-variance",
                   "xy-score", "xy-weight")) {

        print(typeC)

        if(grepl("predict", typeC))
            subset <- "odd"
        else
            subset <- NULL

        plsModel <- opls(dataMatrix, sampleMetadata[, "gender"],
                         predI = ifelse(typeC != "xy-weight", 1, 2),
                         orthoI = ifelse(typeC != "xy-weight", 1, 0),
                         permI = ifelse(typeC == "permutation", 10, 0),
                         subset = subset,
                         info.txtC = "none",
                         fig.pdfC = "none")

        plot(plsModel, typeVc = typeC)

    }

    sacPlsda <- opls(dataMatrix, sampleMetadata[, "gender"])
    plot(sacPlsda, parPaletteVc = c("green4", "magenta"))

    #### Application to an ExpressionSet

    sacSet <- Biobase::ExpressionSet(assayData = t(dataMatrix),
                                     phenoData = new("AnnotatedDataFrame",
                                                     data = sampleMetadata),
                                     featureData = new("AnnotatedDataFrame",
                                                       data = variableMetadata),
                                     experimentData = new("MIAME",
                                                          title = "sacurine"))

    sacPlsda <- opls(sacSet, "gender")
    plot(sacPlsda, "gender", typeVc = "x-score")

    detach(sacurine)

[Package ropls version 1.19.16 Index]