VisualiseIndividualGenes: Wrapper for Visualising Individual Genes in Gene Sets

This wrapper function helps explore individual gene behavior within a gene set used in markeR. It dispatches to specific visualisation functions based on plot_type, supporting various plot types: heatmaps, violin plots, correlation analysis, PCA, ROC/AUC, and effect size heatmaps.

Usage

VisualiseIndividualGenes(type, data, genes, metadata = NULL, ...)

Arguments

type

Character. Specifies the type of plot to generate. Must be one of:

• "violin": Violin plots of individual gene expression by group

• "correlation": Correlation heatmap of selected genes

• "expression": Expression heatmap of selected genes

• "roc": ROC plots for classification performance of individual genes

• "auc": AUC plots for classification performance of individual genes

• "rocauc": Combined ROC and AUC plots

• "cohend": Effect size (Cohen's D) heatmap

• "pca": PCA plot of selected genes

data

Required. Expression data matrix or data frame, with samples as rows and genes as columns.

genes

Required. Character vector of gene names to include in the visualisation.

metadata

Optional. Data frame with sample metadata, required for some plot types (e.g., violin, roc, cohend).

...

Additional arguments passed to the specific plotting function.

Value

The output of the specific plotting function called, usually a ggplot or ComplexHeatmap object, often wrapped in a list with additional data.

Additional required arguments (passed via ...) per plot_type

violin

Requires GroupingVariable (column name in metadata for grouping).

roc, auc, rocauc

Requires condition_var (metadata column with condition labels) and class (positive class label).

cohend

Requires condition_var and class (same as roc).

correlation, expression, pca

No additional mandatory arguments required.

See also

IndividualGenes_Violins, CorrelationHeatmap, ExpressionHeatmap, CohenD_IndividualGenes, plotPCA, ROCandAUCplot

Examples

# Example data
set.seed(123)
expr_data <- matrix(rexp(1000, rate = 1), nrow = 50, ncol = 20)
rownames(expr_data) <- paste0("Gene", 1:50)
colnames(expr_data) <- paste0("Sample", 1:20)

sample_info <- data.frame(
  SampleID = colnames(expr_data),
  Condition = rep(c("A", "B"), each = 10),
  Diagnosis = rep(c("Disease", "Control"), each = 10),
  stringsAsFactors = FALSE
)
rownames(sample_info) <- sample_info$SampleID

selected_genes <- row.names(expr_data)[1:5]

# Violin plot
VisualiseIndividualGenes(
  type = "violin",
  data = expr_data,
  metadata = sample_info,
  genes = selected_genes,
  GroupingVariable = "Condition",
  nrow=1
)
#> Using gene as id variables

VisualiseIndividualGenes(
  type = "correlation",
  data = expr_data,
  genes = selected_genes
)


# Expression heatmap
VisualiseIndividualGenes(
  type = "expression",
  data = expr_data,
  genes = selected_genes
)


# PCA plot
VisualiseIndividualGenes(
  type = "pca",
  data = expr_data,
  genes = selected_genes,
  metadata = sample_info,
  ColorVariable="Condition"

)


# ROC plot
VisualiseIndividualGenes(
  type = "roc",
  data = expr_data,
  metadata = sample_info,
  genes = selected_genes,
  condition_var = "Diagnosis",
  class = "Disease"

)


# AUC plot
VisualiseIndividualGenes(
  type = "auc",
  data = expr_data,
  metadata = sample_info,
  genes = selected_genes,
  condition_var = "Diagnosis",
  class = "Disease"
)


# ROC&AUC plot
VisualiseIndividualGenes(
  type = "rocauc",
  data = expr_data,
  metadata = sample_info,
  genes = selected_genes,
  condition_var = "Diagnosis",
  class = "Disease"
)


# Cohen's D plot
VisualiseIndividualGenes(
  type = "cohend",
  data = expr_data,
  metadata = sample_info,
  genes = selected_genes,
  condition_var = "Diagnosis",
  class = "Disease"
)