Create a formatted heatmap from PubMatrix results

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,
  cellwidth = NA,
  cellheight = NA,
  scale_font = TRUE
)

Arguments

matrix

A data frame or 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

cellwidth

Optional numeric cell width for pheatmap (in pixels). Default `NA` lets pheatmap auto-size.

cellheight

Optional numeric cell height for pheatmap (in pixels). Default `NA` lets pheatmap auto-size.

scale_font

Logical value determining if font size should scale with cell size. Default is TRUE

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

# Create a small test matrix
test_matrix <- matrix(c(1, 2, 3, 4), nrow = 2, ncol = 2)
rownames(test_matrix) <- c("Gene1", "Gene2")
colnames(test_matrix) <- c("GeneA", "GeneB")

# Create heatmap using the helper
plot_pubmatrix_heatmap(test_matrix, title = "Test Heatmap")


# Equivalent using pheatmap directly:
# Compute overlap matrix as the function does (here trivial because counts are raw)
overlap_matrix <- test_matrix
pheatmap::pheatmap(
  overlap_matrix,
  main = "Test Heatmap (pheatmap)",
  color = colorRampPalette(c("#fee5d9", "#cb181d"))(100),
  display_numbers = TRUE,
  fontsize = 16,
  fontsize_number = 14,
  border_color = "lightgray",
  show_rownames = TRUE,
  show_colnames = TRUE
)