samplesMetricMap: Plot a Grid of Sample Error Rates or Accuracies

In ClassifyR: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing

Description

A grid of coloured tiles is drawn. There is one column for each sample and one row for each classification result.

Usage

  ## S4 method for signature 'list'
samplesMetricMap(results,
                     comparison = c("classificationName", "datasetName", "selectionName",
                                "validation"),
                 metric = c("error", "accuracy"), featureValues = NULL, featureName = NULL,
                     metricColours = list(c("#3F48CC", "#6F75D8", "#9FA3E5", "#CFD1F2", "#FFFFFF"),
                                        c("#880015", "#A53F4F", "#C37F8A", "#E1BFC4", "#FFFFFF")),
                     classColours = c("#3F48CC", "#880015"), groupColours = c("darkgreen", "yellow2"),
                     fontSizes = c(24, 16, 12, 12, 12),
                  mapHeight = 4, title = "Error Comparison", showLegends = TRUE,
                 xAxisLabel = "Sample Name", showXtickLabels = TRUE,
                 yAxisLabel = "Analysis", showYtickLabels = TRUE,
                 legendSize = grid::unit(1, "lines"), plot = TRUE)
  ## S4 method for signature 'matrix'
samplesMetricMap(results, classes,
                     metric = c("error", "accuracy"),
                     featureValues = NULL, featureName = NULL,
                     metricColours = list(c("#3F48CC", "#6F75D8", "#9FA3E5", "#CFD1F2", "#FFFFFF"),
                                        c("#880015", "#A53F4F", "#C37F8A", "#E1BFC4", "#FFFFFF")),
                     classColours = c("#3F48CC", "#880015"), groupColours = c("darkgreen", "yellow2"),
                     fontSizes = c(24, 16, 12, 12, 12),
                  mapHeight = 4, title = "Error Comparison", showLegends = TRUE,
                 xAxisLabel = "Sample Name", showXtickLabels = TRUE,
                 yAxisLabel = "Analysis", showYtickLabels = TRUE,
                 legendSize = grid::unit(1, "lines"), plot = TRUE)                 

Arguments

results	A list of ClassifyResult objects. Could also be a matrix, for backwards compatibility.
classes	If results is a matrix, this is a factor vector of the same length as the number of columns that results has.
comparison	The aspect of the experimental design to compare.
metric	The sample-wise metric to plot.
featureValues	If not NULL, can be a named factor or named numeric vector specifying some variable of interest to plot underneath the class bar.
featureName	A label describing the information in featureValues. It must be specified if featureValues is.
metricColours	A vector of colours for metric levels.
classColours	Either a vector of colours for class levels if both classes should have same colour, or a list of length 2, with each component being a vector of the same length. The vector has the colour gradient for each class.
groupColours	A vector of colours for group levels. Only useful if groups is not NULL.
fontSizes	A vector of length 5. The first number is the size of the title. The second number is the size of the axes titles. The third number is the size of the axes values. The fourth number is the size of the legends' titles. The fifth number is the font size of the legend labels.
mapHeight	Height of the map, relative to the height of the class colour bar.
title	The title to place above the plot.
showLegends	Logical. IF FALSE, the legend is not drawn.
xAxisLabel	The name plotted for the x-axis. NULL suppresses label.
showXtickLabels	Logical. IF FALSE, the x-axis labels are hidden.
showYtickLabels	Logical. IF FALSE, the y-axis labels are hidden.
yAxisLabel	The name plotted for the y-axis. NULL suppresses label.
legendSize	The size of the boxes in the legends.
plot	Logical. IF TRUE, a plot is produced on the current graphics device.

Details

The names of results determine the row names that will be in the plot. The length of metricColours determines how many bins the metric values will be discretised to.

Value

A plot is produced and a grob is returned that can be saved to a graphics device.

Author(s)

Dario Strbenac

Examples

  predicted <- data.frame(sample = LETTERS[sample(10, 100, replace = TRUE)],
                          class = rep(c("Healthy", "Cancer"), each = 50))
  actual <- factor(rep(c("Healthy", "Cancer"), each = 5), levels = c("Healthy", "Cancer"))
  features <- sapply(1:100, function(index) paste(sample(LETTERS, 3), collapse = ''))
  result1 <- ClassifyResult("Example", "Differential Expression", "t-test",
                            LETTERS[1:10], features, 100, list(1:100), list(sample(10, 10)),
                            list(function(oracle){}), list(predicted), actual,
                            list("permuteFold", 100, 5))
  predicted[, "class"] <- sample(predicted[, "class"])
  result2 <- ClassifyResult("Example", "Differential Variability", "Bartlett Test",
                            LETTERS[1:10], features, 100, list(1:100), list(sample(10, 10)),
                            list(function(oracle){}), list(predicted), actual,
                            validation = list("leave", 2))
  result1 <- calcCVperformance(result1, "sample error")
  result2 <- calcCVperformance(result2, "sample error")
  groups <- factor(rep(c("Male", "Female"), length.out = 10))
  names(groups) <- LETTERS[1:10]
  cholesterol <- c(4.0, 5.5, 3.9, 4.9, 5.7, 7.1, 7.9, 8.0, 8.5, 7.2)
  names(cholesterol) <- LETTERS[1:10]
  
  wholePlot <- samplesMetricMap(list(Gene = result1, Protein = result2))
  wholePlot <- samplesMetricMap(list(Gene = result1, Protein = result2),
                                featureValues = groups, featureName = "Gender")
  wholePlot <- samplesMetricMap(list(Gene = result1, Protein = result2),
                                featureValues = cholesterol, featureName = "Cholesterol")                                

ClassifyR documentation built on Nov. 8, 2020, 6:53 p.m.