Bioc package: The Smart Transcriptomic Pipeline

dataPlot

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 when mode is equal to 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 <- knn_trn(t(DEGsMatrix), labels, rownames(DEGsMatrix), 3)
dataPlot(results, labels = "", mode = "heatmapResults", main = "Plot to show indicators of trained model")

CasedUgr/KnowSeq documentation built on Aug. 16, 2022, 6:19 a.m.