Compute Pairwise Gene Co-expression Correlation Matrix

Calculates pairwise correlations between a set of genes across all samples, producing a symmetric correlation matrix that quantifies co-expression relationships.

Usage

gene.correlation.matrix(expression.matrix, probe.ids, method = "pearson")

Arguments

expression.matrix

Numeric matrix of gene expression values as returned by extract.expression()$expression. Rows are probes, columns are samples.

probe.ids

Integer vector of probe IDs to include, as returned by find.probe.by.gene().

method

Character. Correlation method: "pearson" (default), "spearman", or "kendall". Spearman is recommended when distributions are skewed or outliers are a concern.

Value

A symmetric numeric matrix of dimensions length(probe.ids) x length(probe.ids), where each cell contains the pairwise correlation coefficient across all samples. Diagonal values are 1. Row and column names correspond to probe IDs.

Details

Co-expression correlations capture whether two genes tend to be simultaneously up- or down-regulated across samples, which can suggest shared regulatory control or pathway membership. The resulting matrix is the direct input for plot.correlation.heatmap().

Examples

# \donttest{
set.seed(42)
expr.mat <- matrix(rnorm(400), nrow = 4, ncol = 100)
rownames(expr.mat) <- c(101, 102, 103, 104)
probe.ids <- c(101, 102, 103, 104)
cor.mat <- gene.correlation.matrix(expr.mat, probe.ids)
correlation.heatmap.plot(cor.mat, gene.names = c("BRCA1", "TP53", "MYC", "EGFR"))

# }