ggpicrust2 (version 2.1.2)

pathway_gsea: Gene Set Enrichment Analysis for PICRUSt2 output

Description

This function performs Gene Set Enrichment Analysis (GSEA) on PICRUSt2 predicted functional data to identify enriched pathways between different conditions.

Usage

pathway_gsea(
  abundance,
  metadata,
  group,
  pathway_type = "KEGG",
  method = "fgsea",
  rank_method = "signal2noise",
  nperm = 1000,
  min_size = 10,
  max_size = 500,
  p.adjust = "BH",
  seed = 42
)

Value

A data frame containing GSEA results

Arguments

abundance

A data frame containing KO/EC/MetaCyc abundance data, with features as rows and samples as columns

metadata

A data frame containing sample metadata

group

A character string specifying the column name in metadata that contains the grouping variable

pathway_type

A character string specifying the pathway type: "KEGG", "MetaCyc", or "GO"

method

A character string specifying the GSEA method: "fgsea", "GSEA", or "clusterProfiler"

rank_method

A character string specifying the ranking statistic: "signal2noise", "t_test", "log2_ratio", or "diff_abundance"

nperm

An integer specifying the number of permutations

min_size

An integer specifying the minimum gene set size

max_size

An integer specifying the maximum gene set size

p.adjust

A character string specifying the p-value adjustment method

seed

An integer specifying the random seed for reproducibility

Examples

if (FALSE) {
# Load example data
data(ko_abundance)
data(metadata)

# Prepare abundance data
abundance_data <- as.data.frame(ko_abundance)
rownames(abundance_data) <- abundance_data[, "#NAME"]
abundance_data <- abundance_data[, -1]

# Run GSEA analysis
gsea_results <- pathway_gsea(
  abundance = abundance_data,
  metadata = metadata,
  group = "Environment",
  pathway_type = "KEGG",
  method = "fgsea"
)

# Visualize results
visualize_gsea(gsea_results, plot_type = "enrichment_plot", n_pathways = 10)
}