dataPlot {KnowSeq} | R Documentation

## Plot different graphs depending on the current step of KnowSeq pipeline.

### Description

This function allows to plot different charts only by changing the parameters, for the different KnowSeq pipeline steps. Furthermore, the chosen plot can be saved to PNG and PDF.

### Usage

    dataPlot(
      data,
      labels,
      colours = c("red", "green"),
      main = "",
      ylab = "Expression",
      xlab = "Samples",
      xgrid = FALSE,
      ygrid = FALSE,
      legend = "",
      mode = "boxplot",
      heatmapResultsN = 0,
      toPNG = FALSE,
      toPDF = FALSE
    )

### Arguments

`data` | Normally, the data parameter is an expression matrix or data.frame, however for the confusionMatrix plot, the data are a confussion matrix that can be achieved by using the output of any of the machine learning functions of this package.
`labels` | A vector or factor that contains the labels for each of the samples in the data parameter.
`colours` | A vector that contains the desired colours to plot the different charts. Example: c("red","green","blue").
`main` | The title for the plot.
`ylab` | The description for the y axis.
`xlab` | The description for the x axis.
`xgrid` | Shows the x grid into the plot
`ygrid` | Shows the y grid into the plot
`legend` | A vector with the elements in the legend of the plot.
`mode` | The different plots supported by this package. The possibilities are boxplot, orderedBoxplot, genesBoxplot, heatmap, confusionMatrix, classResults and heatmapResults.
`heatmapResultsN` | Number of genes to show if mode is heatmapResults.
`toPNG` | Boolean variable to indicate if a plot would be save to PNG.
`toPDF` | Boolean variable to indicate if a plot would be save to PDF.

### Value

Nothing to return.

### Examples

    dir <- system.file("extdata", package="KnowSeq")
    load(paste(dir,"/expressionExample.RData",sep = ""))

    dataPlot(expressionMatrix,labels,mode = "boxplot",toPNG = TRUE,toPDF = TRUE)
    dataPlot(DEGsMatrix[1:12,],labels,mode = "orderedBoxplot",toPNG = TRUE,toPDF = TRUE)
    dataPlot(DEGsMatrix[1:12,],labels,mode = "genesBoxplot",toPNG = TRUE,toPDF = FALSE)
    dataPlot(DEGsMatrix[1:12,],labels,mode = "heatmap",toPNG = TRUE,toPDF = TRUE)

    results <- svm_trn(t(DEGsMatrix), labels, rownames(DEGsMatrix), 2)
    dataPlot(results, labels = "", mode = "heatmapResults", main = "Plot to show indicators of trained model")

[Package KnowSeq version 1.2.2 Index]