Plot Gene Co-expression Correlation Heatmap

Visualizes a gene-by-gene correlation matrix as a clustered heatmap, revealing groups of co-expressed genes within a candidate gene set.

Usage

correlation.heatmap.plot(cor.matrix, gene.names = NULL)

Arguments

cor.matrix

Numeric matrix as returned by gene.correlation.matrix(). Must be square and symmetric with row/column names corresponding to probe IDs.

gene.names

Character vector of gene names to use as axis labels in place of probe IDs. Must be the same length and order as cor.matrix rows. Default is NULL, which uses probe IDs.

Value

A pheatmap object displaying the correlation matrix with hierarchical clustering applied to both rows and columns. Color scale runs from blue (strong negative correlation) through white (no correlation) to red (strong positive correlation).

Details

Hierarchical clustering of the correlation matrix groups genes with similar co-expression patterns into visible blocks on the heatmap. These blocks often correspond to genes in the same pathway or under shared regulatory control. This function is a targeted companion to gene.correlation.matrix() for a pre-selected gene set, and complements the genome-wide network view produced by WGCNA.

Examples

# \donttest{
mat <- matrix(
  c(1.00, 0.85, 0.62, 0.91,
    0.85, 1.00, 0.74, 0.88,
    0.62, 0.74, 1.00, 0.69,
    0.91, 0.88, 0.69, 1.00),
  nrow = 4,
  dimnames = list(
    c("BRCA1", "TP53", "MYC", "EGFR"),
    c("BRCA1", "TP53", "MYC", "EGFR")
  )
)
correlation.heatmap.plot(mat, gene.names = c("BRCA1", "TP53", "MYC", "EGFR"))

# }