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Plot PCA of Sample Expression Profiles

Source: R/visualization.R
pca.plot.Rd

Reduces the dimensionality of the full expression matrix using Principal Component Analysis and plots samples in the first two principal component axes, colored by a phenotype variable. Useful for quality control, batch effect detection, and exploratory assessment of sample clustering.

Usage

pca.plot(
  expression.matrix,
  phenotype,
  color.by = "disease.state",
  scale = TRUE
)

Arguments

expression.matrix

Matrix of gene expression values. Rows are probes, columns are samples.

phenotype

Data frame of sample metadata, as returned by extract.expression()$phenotype.

color.by

Character. Column name in phenotype to use for point coloring. Default is "disease.state".

scale

Logical. Whether to scale probes to unit variance before PCA. Default is TRUE.

Value

A ggplot object showing samples as points in PC1 vs PC2 space, colored by the specified phenotype variable. The percentage of variance explained is shown on each axis label.

Details

PCA is typically applied at the whole-dataset level before any gene filtering, making it a useful first diagnostic after load.geo.soft() and extract.expression(). Tight clustering of samples by disease state in PC1/PC2 space suggests a strong and detectable expression signal. Unexpected clustering by batch, sex, or other covariates can indicate the need for covariate adjustment in downstream run.limma.de() models.

Examples

# \donttest{
# Create a synthetic expression matrix (50 probes x 12 samples)
set.seed(42)
expr.mat <- matrix(rnorm(600), nrow = 50, ncol = 12)
rownames(expr.mat) <- paste0("probe", 1:50)
colnames(expr.mat) <- paste0("sample", 1:12)

# Create a matching phenotype data frame
phenotype <- data.frame(
  disease.state = rep(c("normal", "RCC"), each = 6),
  row.names = paste0("sample", 1:12)
)

pca.plot(expr.mat, phenotype, color.by = "disease.state")

# }