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PCA Plot

Source: R/viz_pca.R
bb_pca.Rd

Creates a PCA plot from a normalized count matrix with sample metadata.

Usage

bb_pca(
  counts,
  metadata,
  color_by,
  shape_by = NULL,
  n_genes = 500L,
  label = FALSE,
  point_size = 3
)

Arguments

counts

Numeric matrix. Normalized count matrix (genes x samples).

metadata

A data.frame with sample information. Rownames must match column names of counts.

color_by

Character. Column name in metadata to use for coloring points.

shape_by

Character or NULL. Column name in metadata for point shapes.

n_genes

Integer. Number of top variable genes to use for PCA. Default 500.

label

Logical. Whether to label sample points. Default FALSE.

point_size

Numeric. Size of points. Default 3.

Value

A ggplot2::ggplot object.

Examples

set.seed(42)
counts <- matrix(rpois(600, 100), nrow = 100, ncol = 6,
  dimnames = list(paste0("gene", 1:100), paste0("S", 1:6)))
meta <- data.frame(
  condition = rep(c("Control", "Treatment"), each = 3),
  row.names = paste0("S", 1:6)
)
bb_pca(counts, meta, color_by = "condition")