pathway_gsea: Gene Set Enrichment Analysis for PICRUSt2 output

In ggpicrust2: Make 'PICRUSt2' Output Analysis and Visualization Easier

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 = "camera",
  covariates = NULL,
  contrast = NULL,
  inter.gene.cor = 0.01,
  rank_method = "signal2noise",
  nperm = 1000,
  min_size = 5,
  max_size = 500,
  p_adjust_method = "BH",
  seed = 42,
  go_category = "all",
  organism = "ko",
  p.adjust = NULL
)

Arguments

abundance	A data frame containing gene/enzyme abundance data, with features as rows and samples as columns. For KEGG analysis: features should be KO IDs (e.g., K00001). For MetaCyc analysis: features should be EC numbers (e.g., EC:1.1.1.1 or 1.1.1.1), NOT pathway IDs. For GO analysis: features should be KO IDs that will be mapped to GO terms. NOTE: This function requires gene-level data, not pathway-level abundances. For pathway abundance analysis, use pathway_daa instead
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: "camera": Competitive gene set test using limma's camera function (recommended). Accounts for inter-gene correlations and provides more reliable p-values. "fry": Fast approximation to rotation gene set testing using limma's fry function. Self-contained test that is computationally efficient. "fgsea": Fast preranked GSEA implementation. Note: preranked methods may produce unreliable p-values due to not accounting for inter-gene correlations (Wu et al., 2012). "GSEA" or "clusterProfiler": clusterProfiler's GSEA implementation.
covariates	A character vector specifying column names in metadata to use as covariates for adjustment. Only used when method is "camera" or "fry". Default is NULL (no covariates). Example: covariates = c("age", "sex", "BMI")
contrast	For multi-group comparisons with "camera" or "fry" methods, specify the contrast to test. Can be a character string naming a group level, or a numeric vector of contrast weights. Default is NULL (automatic: compares second group to first).
inter.gene.cor	Numeric value specifying the inter-gene correlation for camera method. Default is 0.01. Use NA to estimate correlation from data for each gene set.
rank_method	A character string specifying the ranking statistic for preranked methods (fgsea, GSEA, clusterProfiler): "signal2noise", "t_test", "log2_ratio", or "diff_abundance"
nperm	An integer specifying the number of permutations (for clusterProfiler method only). The fgsea method uses adaptive multilevel splitting and does not require a fixed permutation count.
min_size	An integer specifying the minimum gene set size
max_size	An integer specifying the maximum gene set size
p_adjust_method	A character string specifying the p-value adjustment method
seed	An integer specifying the random seed for reproducibility
go_category	A character string specifying GO category to use. "all" (default) uses all categories present in the reference data. Valid categories are determined by the reference data (currently MF and CC). See table(ko_to_go_reference$category) for available categories.
organism	A character string specifying the organism for KEGG analysis (default: "ko" for KEGG Orthology)
p.adjust	Deprecated. Use p_adjust_method instead.

Details

Method Selection:

The camera method (default) is recommended for most analyses because:

• It accounts for inter-gene correlations, providing more accurate p-values

• It supports covariate adjustment through the design matrix

• It performs a competitive test (genes in set vs. genes not in set)

The fry method is a fast alternative that:

• Performs a self-contained test (are genes in the set differentially expressed?)

• Is computationally very efficient for large numbers of gene sets

• Also supports covariate adjustment

The preranked methods (fgsea, GSEA) are included for compatibility but users should be aware that Wu et al. (2012) demonstrated these can produce "spectacularly wrong p-values" even with low inter-gene correlations.

Covariate Adjustment:

When using method = "camera" or method = "fry", you can adjust for confounding variables by specifying them in the covariates parameter. This is particularly important in microbiome studies where factors like age, sex, BMI, and batch effects can influence results.

Value

A data frame containing GSEA results with columns:

• pathway_id: Pathway identifier

• pathway_name: Pathway name/description

• size: Number of genes in the pathway

• direction: Direction of enrichment ("Up" or "Down", for camera/fry)

• pvalue: Raw p-value

• p.adjust: Adjusted p-value (FDR)

• method: The method used for analysis

For fgsea/clusterProfiler methods, additional columns include ES, NES, and leading_edge.

References

Wu, D., & Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133.

Wu, D., Lim, E., Vaillant, F., Asselin-Labat, M. L., Visvader, J. E., & Smyth, G. K. (2010). ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics, 26(17), 2176-2182.

Examples

## Not run: 
# 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]

# Method 1: Using camera (recommended) - accounts for inter-gene correlations
gsea_results <- pathway_gsea(
  abundance = abundance_data,
  metadata = metadata,
  group = "Environment",
  pathway_type = "KEGG",
  method = "camera"
)

# Method 2: Using camera with covariate adjustment
gsea_results_adj <- pathway_gsea(
  abundance = abundance_data,
  metadata = metadata,
  group = "Disease",
  covariates = c("age", "sex"),
  pathway_type = "KEGG",
  method = "camera"
)

# Method 3: Using fry for fast self-contained testing
gsea_results_fry <- pathway_gsea(
  abundance = abundance_data,
  metadata = metadata,
  group = "Environment",
  pathway_type = "KEGG",
  method = "fry"
)

# Method 4: Using fgsea (preranked, less reliable p-values)
gsea_results_fgsea <- 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)

## End(Not run)

ggpicrust2 documentation built on April 10, 2026, 5:06 p.m.