Perform Principal Component Analysis (PCA) on functional pathway abundance data and create visualizations of the PCA results.

Perform Principal Component Analysis (PCA) on functional pathway abundance data and create visualizations of the PCA results.

Usage

pathway_pca(abundance, metadata, group)

Arguments

abundance

A data frame, predicted functional pathway abundance.

metadata

A tibble, consisting of sample information.

group

A character, group name.

Value

A ggplot object showing the PCA results.

Examples

# Create example functional pathway abundance data
abundance_example <- data.frame(A = rnorm(10), B = rnorm(10), C = rnorm(10))

# Create example metadata
metadata_example <- tibble::tibble(sample_id = 1:10,
                                   group = factor(rep(c("Control", "Treatment"), each = 5)))

# Perform PCA and create visualizations
pca_plot <- pathway_pca(t(abundance_example), metadata_example, "group")
print(pca_plot)