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Generate Heatmaps for AUC Scores using ggplot2

Source: R/ROCAUC_Scores.R
AUC_Scores.Rd

This function computes AUC scores for multiple gene signatures and scoring methods, and generates a heatmap for each gene signature. The heatmap displays the AUC scores, with the contrasts as rows and methods as columns. The heatmaps are then arranged in a grid layout.

Usage

AUC_Scores(
  data,
  metadata,
  gene_sets,
  method = c("logmedian", "ssGSEA", "ranking", "all"),
  mode = c("simple", "medium", "extensive"),
  variable,
  nrow = NULL,
  ncol = NULL,
  limits = NULL,
  widthTitle = 22,
  titlesize = 12,
  ColorValues = c("#F9F4AE", "#B44141"),
  title = NULL
)

Arguments

data

A data frame of gene expression data with genes as rows and samples as columns. Row names should contain gene names and column names sample identifiers.

metadata

A data frame of sample metadata. The first column must contain sample identifiers matching those in data.

gene_sets

A named list of gene sets.

method

A character string specifying the scoring method(s) ("logmedian", "ssGSEA", "ranking", or "all").

mode

A string specifying the level of detail for contrasts. Options are:

  • "simple": Pairwise comparisons (e.g., A - B).

  • "medium": Pairwise comparisons plus comparisons against the mean of other groups.

  • "extensive": All possible groupwise contrasts, ensuring balance in the number of terms on each side.

variable

A string specifying the grouping variable in metadata used for computing AUC comparisons.

nrow

Optional. An integer specifying the number of rows in the heatmap grid. If NULL, the number of rows is computed automatically.

ncol

Optional. An integer specifying the number of columns in the heatmap grid. If NULL, the number of columns is computed automatically.

limits

Optional. A numeric vector of length 2 specifying the color scale limits (e.g., c(min, max)). If NULL, the limits are determined from the data.

widthTitle

An integer specifying the width used for wrapping gene set signature names in the heatmap titles. Default is 22.

titlesize

An integer specifying the text size for each of the heatmap titles. Default is 12.

ColorValues

A character vector specifying the colors for the gradient fill in the heatmaps. Default is c("#F9F4AE", "#B44141").

title

Title for the grid of plots.

Value

A list with two elements:

plt

A combined heatmap arranged in a grid using ggpubr::ggarrange.

data

A list containing the AUC scores for each gene signature, as computed by ROCAUC_Scores_Calculate.

Details

The function first calculates AUC scores for each gene signature using ROCAUC_Scores_Calculate. The resulting matrices are converted to a long format so that each cell in the heatmap can display the AUC value. A title for each heatmap is dynamically created. The heatmaps are then adjusted to display axis text and ticks only for the left-most column and bottom row, and combined into a grid layout. If neither nrow nor ncol are specified, the layout is automatically determined to best approximate a square grid.

Examples

# Example data
data <- as.data.frame(abs(matrix(rnorm(1000), ncol = 10)))
rownames(data) <- paste0("Gene", 1:100)  # Name columns as Gene1, Gene2, ..., Gene10
colnames(data) <- paste0("Sample", 1:10)  # Name rows as Sample1, Sample2, ..., Sample100

# Metadata with sample ID and condition
metadata <- data.frame(
  SampleID = colnames(data),  # Sample ID matches the colnames of the data
  Condition = rep(c("A", "B"), each = 5)  # Two conditions (A and B)
)

# Example gene set
gene_sets <- list(Signature1 = c("Gene1", "Gene2", "Gene3"),
                  Signature2 = c("Gene2","Gene4","Gene10"),
                  Signature3 = c("Gene6","Gene46","Gene13"))  # Example gene sets

AUC_Scores(
    data = data,
    metadata = metadata,
    gene_sets = gene_sets,
    method = "ssGSEA",
    variable = "Condition",
    nrow = 1,
    ncol = NULL,
    limits = c(0, 1),
    widthTitle = 30,
    titlesize = 14,
    ColorValues = c("#F9F4AE", "#B44141")
  )
#> Considering unidirectional gene signature mode for signature Signature1
#> No id variables; using all as measure variables
#> Considering unidirectional gene signature mode for signature Signature2
#> No id variables; using all as measure variables
#> Considering unidirectional gene signature mode for signature Signature3
#> No id variables; using all as measure variables



AUC_Scores(
  data = data,
  metadata = metadata,
  gene_sets = gene_sets,
  method = "all",
  variable = "Condition",
  nrow = 1,
  ncol = NULL,
  limits = c(0, 1),
  widthTitle = 30,
  titlesize = 14,
  ColorValues = c("#F9F4AE", "#B44141")
)
#> Considering unidirectional gene signature mode for signature Signature1
#> No id variables; using all as measure variables
#> Considering unidirectional gene signature mode for signature Signature2
#> No id variables; using all as measure variables
#> Considering unidirectional gene signature mode for signature Signature3
#> No id variables; using all as measure variables
#> Considering unidirectional gene signature mode for signature Signature1
#> Considering unidirectional gene signature mode for signature Signature2
#> Considering unidirectional gene signature mode for signature Signature3
#> Considering unidirectional gene signature mode for signature Signature1
#> Considering unidirectional gene signature mode for signature Signature2
#> Considering unidirectional gene signature mode for signature Signature3