Create a formatted heatmap from PubMatrix results — plot_pubmatrix

This function creates a heatmap displaying Jaccard distance values calculated from a PubMatrix result matrix, with Euclidean distance clustering for rows and columns.

Usage

plot_pubmatrix_heatmap(
  matrix,
  title = "PubMatrix Co-occurrence Heatmap",
  cluster_rows = TRUE,
  cluster_cols = TRUE,
  show_numbers = TRUE,
  color_palette = NULL,
  filename = NULL,
  width = 10,
  height = 8
)

Arguments

matrix: A numeric matrix from PubMatrix results containing publication co-occurrence counts
title: Character string for the heatmap title. Default is "PubMatrix Co-occurrence Heatmap"
cluster_rows: Logical value determining if rows should be clustered using Euclidean distance. Default is TRUE
cluster_cols: Logical value determining if columns should be clustered using Euclidean distance. Default is TRUE
show_numbers: Logical value determining if Jaccard distance values should be displayed in cells. Default is TRUE
color_palette: Color palette for the heatmap. Default uses a red gradient color scale
filename: Optional filename to save the heatmap. If NULL, displays the plot
width: Width of saved plot in inches. Default is 10
height: Height of saved plot in inches. Default is 8

Value

A pheatmap object (invisible)

Details

The function displays Jaccard distance values in the heatmap cells (same as compute_jaccard_matrix) and uses Euclidean distance for clustering rows and columns. Jaccard distance is calculated as 1 - (intersection/union) where intersection is the number of common non-zero elements and union is the total number of non-zero elements. NA values in the input matrix are converted to 0 before calculation to ensure stability.

Examples

if (FALSE) { # \dontrun{
# First run PubMatrix
result <- PubMatrix(A = c("gene1", "gene2"), B = c("disease1", "disease2"))

# Create heatmap
plot_pubmatrix_heatmap(result)

# Save to file
plot_pubmatrix_heatmap(result, filename = "my_heatmap.png")
} # }