
<!-- README.md is generated from README.Rmd. Please edit that file -->

# ggpicrust2

<img src="man/figures/logo.png" alt="ggpicrust2 logo" align="right" width="200">

🌟 **If you find `ggpicrust2` helpful, please consider giving us a star
on GitHub!** Your support greatly motivates us to improve and maintain
this project. 🌟

*ggpicrust2* is a comprehensive package designed to provide a seamless
and intuitive solution for analyzing and interpreting the results of
PICRUSt2 functional prediction. It offers a wide range of features,
including pathway name/description annotations, advanced differential
abundance (DA) methods, and visualization of DA results.

One of the newest additions to *ggpicrust2* is the capability to compare
the consistency and inconsistency across different DA methods applied to
the same dataset. This feature allows users to assess the agreement and
discrepancy between various methods when it comes to predicting and
sequencing the metagenome of a particular sample. It provides valuable
insights into the consistency of results obtained from different
approaches and helps users evaluate the robustness of their findings.

By leveraging this functionality, researchers, data scientists, and
bioinformaticians can gain a deeper understanding of the underlying
biological processes and mechanisms present in their PICRUSt2 output
data. This comparison of different methods enables them to make informed
decisions and draw reliable conclusions based on the consistency
evaluation of macrogenomic predictions or sequencing results for the
same sample.

If you are interested in exploring and analyzing your PICRUSt2 output
data, *ggpicrust2* is a powerful tool that provides a comprehensive set
of features, including the ability to assess the consistency and
evaluate the performance of different methods applied to the same
dataset.

[![CRAN
version](https://www.r-pkg.org/badges/version/ggpicrust2)](https://CRAN.R-project.org/package=ggpicrust2)
[![Downloads](https://cranlogs.r-pkg.org/badges/grand-total/ggpicrust2)](https://CRAN.R-project.org/package=ggpicrust2)
[![License:
MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/license/mit)

## News

🔬 **Enhanced GSEA with Limma Camera/Fry Methods (v2.5.6)**

We’ve significantly enhanced the `pathway_gsea()` function with limma’s
competitive gene set testing methods:

- **`camera` method (new default)**: Accounts for inter-gene
  correlations, providing more reliable p-values than preranked methods
  (Wu et al., 2012)
- **`fry` method**: Fast rotation gene set test, efficient for large
  gene set collections
- **Covariate adjustment**: Both camera and fry support adjustment for
  confounding variables (age, sex, BMI, etc.)
- **Contrast specification**: Support for complex experimental designs
  with multiple contrasts

This addresses concerns in the literature that preranked GSEA methods
can produce “spectacularly wrong p-values” due to not accounting for
inter-gene correlations.

📊 **New Visualization Functions: Volcano Plot & Ridge Plot**

We’ve added two new visualization functions for enhanced analysis and
interpretation:

- **`pathway_volcano()`**: Creates publication-quality volcano plots for
  differential abundance analysis, with smart label placement and
  color-coded significance categories.
- **`pathway_ridgeplot()`**: Creates ridge plots (joy plots) for GSEA
  results interpretation, showing distribution of gene abundances within
  enriched pathways.

🔄 **Updated Reference Databases for Improved Pathway Annotation
(v2.1.4)**

We’ve significantly enhanced the reference databases used for pathway
annotation:

- **EC reference data**: Updated from 3,180 to 8,371 entries (163%
  increase)
- **KO reference data**: Updated from 23,917 to 27,531 unique KO IDs
  (15.4% increase)

These updates provide more comprehensive and accurate pathway
annotations, especially for recently discovered enzymes and KEGG
orthology entries. Users will experience improved coverage and precision
in pathway analysis without needing to change any code.

🌟 **New Feature: Gene Set Enrichment Analysis (GSEA) for PICRUSt2
Data**

We’re excited to announce the addition of GSEA functionality to the
ggpicrust2 package! This powerful new feature allows researchers to
perform Gene Set Enrichment Analysis on PICRUSt2 predicted functional
profiles, offering a more nuanced understanding of functional
differences between conditions.

The new GSEA module includes:

- `pathway_gsea()`: Performs GSEA analysis on PICRUSt2 data
- `visualize_gsea()`: Creates various visualizations including
  enrichment plots, dot plots, network plots, and heatmaps
- `compare_gsea_daa()`: Compares GSEA and differential abundance
  analysis results
- `gsea_pathway_annotation()`: Annotates GSEA results with pathway
  information

These new functions complement our existing differential abundance
analysis tools, providing researchers with multiple approaches to
analyze functional profiles.

🧫 **New Feature: Taxa Contribution Workflow for PICRUSt2 Per-sequence
Outputs**

ggpicrust2 now supports parsing and visualizing PICRUSt2 per-sequence
contribution outputs:

- `read_contrib_file()`: Reads `pred_metagenome_contrib.tsv`
- `read_strat_file()`: Reads `pred_metagenome_strat.tsv`
- `aggregate_taxa_contributions()`: Aggregates contributions by taxon,
  sample, and function
- `taxa_contribution_bar()`: Creates stacked bar plots of taxon-level
  contributions
- `taxa_contribution_heatmap()`: Summarizes contribution patterns across
  taxa and functions

This workflow makes it possible to move from pathway-level significance
to an interpretable answer for which taxa are driving those pathway
shifts.

🌟 **Also Check Out: `mLLMCelltype`**

We’re excited to introduce `mLLMCelltype`, our innovative framework for
**single-cell RNA sequencing data annotation**. This iterative multi-LLM
consensus framework leverages the collective intelligence of multiple
large language models (including GPT-4o/4.1, Claude-3.7/3.5, Gemini-2.0,
Grok-3, and others) to significantly improve cell type annotation
accuracy while providing transparent uncertainty quantification.

`mLLMCelltype` addresses critical challenges in scRNA-seq analysis
through its unique architecture:

- **Multi-LLM Consensus**: Overcomes single-model limitations by
  harnessing diverse LLMs’ collective intelligence
- **Structured Deliberation**: Enables models to share reasoning,
  evaluate evidence, and refine annotations through collaborative
  discussion
- **Transparent Uncertainty Metrics**: Provides quantitative measures to
  identify ambiguous cell populations requiring expert review
- **Hallucination Reduction**: Suppresses inaccurate predictions through
  cross-model critical evaluation
- **No Reference Dataset Required**: Performs accurate annotation
  without pre-training or reference data

For researchers working with single-cell data, `mLLMCelltype` offers a
powerful new approach to cell type annotation. Learn more about its
capabilities and methodology on GitHub: [mLLMCelltype
Repository](https://github.com/cafferychen777/mLLMCelltype).

We appreciate your support and interest in our tools and look forward to
seeing how they can enhance your research.

## Table of Contents

- [Citation](#citation)
- [Installation](#installation)
- [Project Links](#stay-updated)
- [Workflow](#workflow)
- [Output](#output)
- [Function Details](#function-details)
  - [ko2kegg_abundance()](#ko2kegg_abundance)
  - [pathway_daa()](#pathway_daa)
  - [compare_daa_results()](#compare_daa_results)
  - [pathway_annotation()](#pathway_annotation)
  - [pathway_errorbar()](#pathway_errorbar)
  - [pathway_heatmap()](#pathway_heatmap)
  - [pathway_pca()](#pathway_pca)
  - [compare_metagenome_results()](#compare_metagenome_results)
  - [taxa contribution workflow](#taxa_contribution)
  - [pathway_gsea()](#pathway_gsea)
  - [visualize_gsea()](#visualize_gsea)
  - [compare_gsea_daa()](#compare_gsea_daa)
  - [gsea_pathway_annotation()](#gsea_pathway_annotation)
- [FAQ](#faq)
- [Author’s Other Projects](#authors-other-projects)

## Citation

If you use *ggpicrust2* in your research, please cite the following
paper:

Chen Yang and others. (2023). ggpicrust2: an R package for PICRUSt2
predicted functional profile analysis and visualization.
*Bioinformatics*, btad470. [DOI
link](https://doi.org/10.1093/bioinformatics/btad470)

The package citation is also available directly in R:

``` r
citation("ggpicrust2")
```

## Installation

You can install the development version of *ggpicrust2* from GitHub
with:

``` r
# install.packages("devtools")
devtools::install_github("cafferychen777/ggpicrust2")
```

## Dependent CRAN Packages

| Package | Description |
|----|----|
| aplot | Create interactive plots |
| dplyr | A fast consistent tool for working with data frame like objects both in memory and out of memory |
| ggplot2 | An implementation of the Grammar of Graphics in R |
| grid | A rewrite of the graphics layout capabilities of R |
| MicrobiomeStat | Statistical analysis of microbiome data |
| readr | Read rectangular data (csv tsv fwf) into R |
| stats | The R Stats Package |
| tibble | Simple Data Frames |
| tidyr | Easily tidy data with spread() and gather() functions |
| ggprism | Interactive 3D plots with ‘prism’ graphics |
| cowplot | Streamlined Plot Theme and Plot Annotations for ‘ggplot2’ |
| ggforce | Easily add secondary axes, zooms, and image overlays to ‘ggplot2’ |
| ggplotify | Convert complex plots into ‘grob’ or ‘ggplot’ objects |
| magrittr | A Forward-Pipe Operator for R |
| utils | The R Utils Package |

## Optional Bioconductor Packages

The package works with a minimal CRAN installation, but several
workflows rely on Bioconductor packages that should be installed only
when you use the corresponding analysis methods or visualizations.

| Package | Description |
|----|----|
| phyloseq | Handling and analysis of high-throughput microbiome census data |
| ALDEx2 | Differential abundance analysis of taxonomic and functional features |
| SummarizedExperiment | SummarizedExperiment container for storing data and metadata together |
| Biobase | Base functions for Bioconductor |
| devtools | Tools to make developing R packages easier |
| ComplexHeatmap | Making Complex Heatmaps in R |
| BiocGenerics | S4 generic functions for Bioconductor |
| BiocManager | Access the Bioconductor Project Package Repositories |
| metagenomeSeq | Statistical analysis for sparse high-throughput sequencing |
| Maaslin2 | Tools for microbiome analysis |
| edgeR | Empirical Analysis of Digital Gene Expression Data in R |
| lefser | R implementation of the LEfSE method for microbiome biomarker discovery |
| limma | Linear Models for Microarray and RNA-Seq Data |
| KEGGREST | R Interface to KEGG REST API |
| DESeq2 | Differential gene expression analysis using RNA-seq data |

``` r
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

pkgs <- c("phyloseq", "ALDEx2", "SummarizedExperiment", "Biobase", "devtools",
          "ComplexHeatmap", "BiocGenerics", "BiocManager", "metagenomeSeq",
          "Maaslin2", "edgeR", "lefser", "limma", "KEGGREST", "DESeq2")

for (pkg in pkgs) {
  if (!requireNamespace(pkg, quietly = TRUE))
    BiocManager::install(pkg)
}
```

## Project Links

Use the project resources below for stable updates and support:

- Package website: <https://cafferyang.com/ggpicrust2/>
- Source repository: <https://github.com/cafferychen777/ggpicrust2>
- Issue tracker: <https://github.com/cafferychen777/ggpicrust2/issues>
- Release notes: [NEWS.md](NEWS.md)

## Workflow

The easiest way to analyze the PICRUSt2 output is using ggpicrust2()
function. The main pipeline can be run with ggpicrust2() function.

ggpicrust2() integrates ko abundance to kegg pathway abundance
conversion, annotation of pathway, differential abundance (DA) analysis,
part of DA results visualization. When you have trouble running
ggpicrust2(), you can debug it by running a separate function, which
will greatly increase the speed of your analysis and visualization.

<figure>
<img
src="https://raw.githubusercontent.com/cafferychen777/ggpicrust2_paper/main/paper_figure/Workflow.png"
alt="ggpicrust2 workflow diagram" />
<figcaption aria-hidden="true">ggpicrust2 workflow diagram</figcaption>
</figure>

### ggpicrust2()

You can download the example dataset from the provided [Github
link](https://github.com/cafferychen777/ggpicrust2_paper/tree/main/Dataset)
and [Google Drive
link](https://drive.google.com/drive/folders/1on4RKgm9NkaBCykMCCRvVJuEJeNVVqAF?usp=share_link)
or use the dataset included in the package.

``` r
# If you want to analyze the abundance of KEGG pathways instead of KO within the pathway, please set `ko_to_kegg` to TRUE.
# KEGG pathways typically have more descriptive explanations.

library(readr)
library(ggpicrust2)
library(tibble)
library(tidyverse)
library(ggprism)
library(patchwork)

# Load necessary data: abundance data and metadata
abundance_file <- "path/to/your/abundance_file.tsv"
metadata <- read_delim(
    "path/to/your/metadata.txt",
    delim = "\t",
    escape_double = FALSE,
    trim_ws = TRUE
)

# Run ggpicrust2 with input file path
results_file_input <- ggpicrust2(file = abundance_file,
                                 metadata = metadata,
                                 group = "your_group_column", # For example dataset, group = "Environment"
                                 pathway = "KO",
                                 daa_method = "LinDA",
                                 ko_to_kegg = TRUE,
                                 order = "pathway_class",
                                 p_values_bar = TRUE,
                                 x_lab = "pathway_name")

# Run ggpicrust2 with imported data.frame
abundance_data <- read_delim(abundance_file, delim = "\t", col_names = TRUE, trim_ws = TRUE)

# Run ggpicrust2 with input data
results_data_input <- ggpicrust2(data = abundance_data,
                                 metadata = metadata,
                                 group = "your_group_column", # For example dataset, group = "Environment"
                                 pathway = "KO",
                                 daa_method = "LinDA",
                                 ko_to_kegg = TRUE,
                                 order = "pathway_class",
                                 p_values_bar = TRUE,
                                 x_lab = "pathway_name")

# Access the plot and results dataframe for the first DA method
example_plot <- results_file_input[[1]]$plot
example_results <- results_file_input[[1]]$results

# Use the example data in ggpicrust2 package
data(ko_abundance)
data(metadata)
results_file_input <- ggpicrust2(data = ko_abundance,
                                 metadata = metadata,
                                 group = "Environment",
                                 pathway = "KO",
                                 daa_method = "LinDA",
                                 ko_to_kegg = TRUE,
                                 order = "pathway_class",
                                 p_values_bar = TRUE,
                                 x_lab = "pathway_name")

# Analyze the EC or MetaCyc pathway
data(metacyc_abundance)
results_file_input <- ggpicrust2(data = metacyc_abundance,
                                 metadata = metadata,
                                 group = "Environment",
                                 pathway = "MetaCyc",
                                 daa_method = "LinDA",
                                 ko_to_kegg = FALSE,
                                 order = "group",
                                 p_values_bar = TRUE,
                                 x_lab = "description")
results_file_input[[1]]$plot
results_file_input[[1]]$results
```

### If an error occurs with ggpicrust2, please use the following workflow.

``` r
library(readr)
library(ggpicrust2)
library(tibble)
library(tidyverse)
library(ggprism)
library(patchwork)

# If you want to analyze KEGG pathway abundance instead of KO within the pathway, turn ko_to_kegg to TRUE.
# KEGG pathways typically have more explainable descriptions.

# Load metadata as a tibble
# data(metadata)
metadata <- read_delim("path/to/your/metadata.txt", delim = "\t", escape_double = FALSE, trim_ws = TRUE)

# Load KEGG pathway abundance
# data(kegg_abundance)
kegg_abundance <- ko2kegg_abundance("path/to/your/pred_metagenome_unstrat.tsv")

# Perform pathway differential abundance analysis (DAA) using ALDEx2 method.
# Please change group to "your_group_column" if you are not using example dataset.
# From v2.5.14 ALDEx2 results include effect_size, diff_btw, log2_fold_change,
# rab_all, and overlap columns by default (via ALDEx2::aldex.effect()), matching
# the other DAA methods that return log2 fold changes by default. Pass
# include_effect_size = FALSE to skip that step.
daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata = metadata, group = "Environment", daa_method = "ALDEx2", select = NULL, reference = NULL)

# Filter results for ALDEx2_Welch's t test method
# Please check the unique(daa_results_df$method) and choose one
daa_sub_method_results_df <- daa_results_df[daa_results_df$method == "ALDEx2_Wilcoxon rank test", ]

# Ranking by |log2_fold_change| is generally more biologically informative than
# ranking by p-value, especially for large datasets where small effects can
# reach statistical significance without being biologically meaningful.
top_hits <- daa_sub_method_results_df[order(-abs(daa_sub_method_results_df$log2_fold_change)), ]

# Annotate pathway results using KO to KEGG conversion
daa_annotated_sub_method_results_df <- pathway_annotation(pathway = "KO", daa_results_df = daa_sub_method_results_df, ko_to_kegg = TRUE)

# Generate pathway error bar plot
# Please change Group to metadata$your_group_column if you are not using example dataset
p <- pathway_errorbar(abundance = kegg_abundance, daa_results_df = daa_annotated_sub_method_results_df, Group = metadata$Environment, p_values_threshold = 0.05, order = "pathway_class", select = NULL, ko_to_kegg = TRUE, p_value_bar = TRUE, colors = NULL, x_lab = "pathway_name")

# If you want to analyze EC, MetaCyc, and KO without conversions, turn ko_to_kegg to FALSE.

# Load metadata as a tibble
# data(metadata)
metadata <- read_delim("path/to/your/metadata.txt", delim = "\t", escape_double = FALSE, trim_ws = TRUE)

# Load KO abundance as a data.frame
# data(ko_abundance)
ko_abundance <- read.delim("path/to/your/pred_metagenome_unstrat.tsv")

# Perform pathway DAA using ALDEx2 method
# Please change column_to_rownames() to the feature column if you are not using example dataset
# Please change group to "your_group_column" if you are not using example dataset
# ALDEx2 effect size columns (effect_size, diff_btw, log2_fold_change, rab_all,
# overlap) are included by default; see the section above.
daa_results_df <- pathway_daa(abundance = ko_abundance %>% column_to_rownames("#NAME"), metadata = metadata, group = "Environment", daa_method = "ALDEx2", select = NULL, reference = NULL)

# Filter results for ALDEx2_Kruskal-Wallace test method
daa_sub_method_results_df <- daa_results_df[daa_results_df$method == "ALDEx2_Wilcoxon rank test", ]

# Annotate pathway results without KO to KEGG conversion
daa_annotated_sub_method_results_df <- pathway_annotation(pathway = "KO", daa_results_df = daa_sub_method_results_df, ko_to_kegg = FALSE)

# Generate pathway error bar plot
# Please change column_to_rownames() to the feature column
# Please change Group to metadata$your_group_column if you are not using example dataset
p <- pathway_errorbar(abundance = ko_abundance %>% column_to_rownames("#NAME"), daa_results_df = daa_annotated_sub_method_results_df, Group = metadata$Environment, p_values_threshold = 0.05, order = "group",
select = daa_annotated_sub_method_results_df %>% arrange(p_adjust) %>% slice(1:20) %>% dplyr::select(feature) %>% pull(),
ko_to_kegg = FALSE,
p_value_bar = TRUE,
colors = NULL,
x_lab = "description")

# Workflow for MetaCyc Pathway and EC

# Load MetaCyc pathway abundance and metadata
data("metacyc_abundance")
data("metadata")

# Perform pathway DAA using LinDA method
# Please change column_to_rownames() to the feature column if you are not using example dataset
# Please change group to "your_group_column" if you are not using example dataset
metacyc_daa_results_df <- pathway_daa(abundance = metacyc_abundance %>% column_to_rownames("pathway"), metadata = metadata, group = "Environment", daa_method = "LinDA")

# Annotate MetaCyc pathway results without KO to KEGG conversion
metacyc_daa_annotated_results_df <- pathway_annotation(pathway = "MetaCyc", daa_results_df = metacyc_daa_results_df, ko_to_kegg = FALSE)

# Generate pathway error bar plot
# Please change column_to_rownames() to the feature column
# Please change Group to metadata$your_group_column if you are not using example dataset
pathway_errorbar(abundance = metacyc_abundance %>% column_to_rownames("pathway"), daa_results_df = metacyc_daa_annotated_results_df, Group = metadata$Environment, ko_to_kegg = FALSE, p_values_threshold = 0.05, order = "group", select = NULL, p_value_bar = TRUE, colors = NULL, x_lab = "description")

# Generate pathway heatmap
# Please change column_to_rownames() to the feature column if you are not using example dataset
# Please change group to "your_group_column" if you are not using example dataset
feature_with_p_0.05 <- metacyc_daa_results_df %>% filter(p_adjust < 0.05)
pathway_heatmap(abundance = metacyc_abundance %>% filter(pathway %in% feature_with_p_0.05$feature) %>% column_to_rownames("pathway"), metadata = metadata, group = "Environment")

# Generate pathway PCA plot
# Please change column_to_rownames() to the feature column if you are not using example dataset
# Please change group to "your_group_column" if you are not using example dataset
pathway_pca(abundance = metacyc_abundance %>% column_to_rownames("pathway"), metadata = metadata, group = "Environment")

# Run pathway DAA for multiple methods
# Please change column_to_rownames() to the feature column if you are not using example dataset
# Please change group to "your_group_column" if you are not using example dataset
methods <- c("ALDEx2", "DESeq2", "edgeR")
daa_results_list <- lapply(methods, function(method) {
  pathway_daa(abundance = metacyc_abundance %>% column_to_rownames("pathway"), metadata = metadata, group = "Environment", daa_method = method)
})

# Compare results across different methods
comparison_results <- compare_daa_results(daa_results_list = daa_results_list, method_names = c("ALDEx2_Welch's t test", "ALDEx2_Wilcoxon rank test", "DESeq2", "edgeR"))
```

## Output

The typical output of the ggpicrust2 is like this.

<figure>
<img
src="https://cdn.jsdelivr.net/gh/cafferychen777/ggpicrust2_paper@main/paper_figure/figure1.jpg"
alt="ggpicrust2 pathway annotation overview" />
<figcaption aria-hidden="true">ggpicrust2 pathway annotation
overview</figcaption>
</figure>

## function details

### ko2kegg_abundance()

KEGG Orthology(KO) is a classification system developed by the Kyoto
Encyclopedia of Genes and Genomes (KEGG) data-base(Kanehisa et al.,
2022). It uses a hierarchical structure to classify enzymes based on the
reactions they catalyze. To better understand pathways’ role in
different groups and classify the pathways, the KO abundance table needs
to be converted to KEGG pathway abundance. But PICRUSt2 removes the
function from PICRUSt. ko2kegg_abundance() can help convert the table.

``` r
# Sample usage of the ko2kegg_abundance function
devtools::install_github('cafferychen777/ggpicrust2')

library(ggpicrust2)

# Assume that the KO abundance table is stored in a file named "ko_abundance.tsv"
ko_abundance_file <- "ko_abundance.tsv"

# Convert KO abundance to KEGG pathway abundance
kegg_abundance <- ko2kegg_abundance(file = ko_abundance_file)

# Alternatively, if the KO abundance data is already loaded as a data frame named "ko_abundance"
data("ko_abundance")
kegg_abundance <- ko2kegg_abundance(data = ko_abundance)

# The resulting kegg_abundance data frame can now be used for further analysis and visualization.
```

### pathway_daa()

Differential abundance (DA) analysis plays a major role in PICRUSt2
downstream analysis. `pathway_daa()` integrates the main DA methods used
for predicted functional profiles, excluding ANCOM and ANCOMBC. It
includes
[ALDEx2](https://www.bioconductor.org/packages/release/bioc/html/ALDEx2.html)
(Fernandes et al., 2013),
[DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html)
(Love et al., 2014),
[Maaslin2](https://www.bioconductor.org/packages/release/bioc/html/Maaslin2.html)
(Mallick et al., 2021),
[LinDA](https://doi.org/10.1186/s13059-022-02655-5) (Zhou et al., 2022),
[edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html)
(Robinson et al., 2010), [limma
voom](https://ucdavis-bioinformatics-training.github.io/2018-June-RNA-Seq-Workshop/thursday/DE.html)
(Ritchie et al., 2015),
[metagenomeSeq](https://www.bioconductor.org/packages/release/bioc/html/metagenomeSeq.html)
(Paulson et al., 2013), and
[Lefser](https://bioconductor.org/packages/release/bioc/html/lefser.html)
(Segata et al., 2011).

``` r
# The abundance table is recommended to be a data.frame rather than a tibble.
# The abundance table should have feature names or pathway names as row names, and sample names as column names.
# You can use the output of ko2kegg_abundance
ko_abundance_file <- "path/to/your/pred_metagenome_unstrat.tsv"
kegg_abundance <- ko2kegg_abundance(ko_abundance_file) # Or use data(kegg_abundance)

metadata <- read_delim("path/to/your/metadata.txt", delim = "\t", escape_double = FALSE, trim_ws = TRUE)

# The default DAA method is "ALDEx2"
# Please change group to "your_group_column" if you are not using example dataset
daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata = metadata, group = "Environment", daa_method = "LinDA", select = NULL, p_adjust_method = "BH", reference = NULL)

# If you have more than 3 group levels and want to use the LinDA, limma voom, or Maaslin2 methods, you should provide a reference.
metadata <- read_delim("path/to/your/metadata.txt", delim = "\t", escape_double = FALSE, trim_ws = TRUE)

# Please change group to "your_group_column" if you are not using example dataset
daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata = metadata, group = "Group", daa_method = "LinDA", select = NULL, p_adjust_method = "BH", reference = "Harvard BRI")

# Other example
data("metacyc_abundance")
data("metadata")
metacyc_daa_results_df <- pathway_daa(abundance = metacyc_abundance %>% column_to_rownames("pathway"), metadata = metadata, group = "Environment", daa_method = "LinDA", select = NULL, p_adjust_method = "BH", reference = NULL)
```

### compare_daa_results()

``` r
library(ggpicrust2)
library(tidyverse)
data("metacyc_abundance")
data("metadata")

# Run pathway_daa function for multiple methods
# Please change column_to_rownames() to the feature column if you are not using example dataset
# Please change group to "your_group_column" if you are not using example dataset
methods <- c("ALDEx2", "DESeq2", "edgeR")
daa_results_list <- lapply(methods, function(method) {
  pathway_daa(abundance = metacyc_abundance %>% column_to_rownames("pathway"), metadata = metadata, group = "Environment", daa_method = method)
})

method_names <- c("ALDEx2","DESeq2", "edgeR")
# Compare results across different methods
comparison_results <- compare_daa_results(daa_results_list = daa_results_list, method_names = method_names)
```

### pathway_annotation()

**If you are in China and you are using kegg pathway annotation, Please
make sure your internet can break through the firewall.**

**New Feature (v2.1.4)**: The `pathway_annotation()` function now
supports species-specific KEGG pathway annotation through the new
`organism` parameter. You can specify KEGG organism codes (e.g., “hsa”
for human, “eco” for E. coli) to get species-specific pathway
information. If no organism is specified (default), the function
retrieves generic KO information not specific to any organism.

**Note**: When `ko_to_kegg = TRUE`, only pathways with
`p_adjust < p_adjust_threshold` are sent to the KEGG API for annotation.
The `p_adjust_threshold` parameter defaults to 0.05 and can be
customized. When called from `ggpicrust2()`, this threshold is
automatically set to match the `p_values_threshold` parameter for
consistency.

``` r

# Make sure to check if the features in `daa_results_df` correspond to the selected pathway

# Annotate KEGG Pathway
data("kegg_abundance")
data("metadata")
# Please change group to "your_group_column" if you are not using example dataset
daa_results_df <- pathway_daa(abundance = kegg_abundance, metadata = metadata, group = "Environment", daa_method = "LinDA")

# Generic KO to KEGG pathway annotation (not specific to any organism)
daa_annotated_results_df <- pathway_annotation(pathway = "KO", daa_results_df = daa_results_df, ko_to_kegg = TRUE)

# Species-specific KEGG pathway annotation (e.g., for human)
human_annotated_results_df <- pathway_annotation(pathway = "KO", daa_results_df = daa_results_df, ko_to_kegg = TRUE, organism = "hsa")

# Species-specific KEGG pathway annotation (e.g., for E. coli)
ecoli_annotated_results_df <- pathway_annotation(pathway = "KO", daa_results_df = daa_results_df, ko_to_kegg = TRUE, organism = "eco")

# Annotate KO
data("ko_abundance")
data("metadata")
# Please change column_to_rownames() to the feature column if you are not using example dataset
# Please change group to "your_group_column" if you are not using example dataset
daa_results_df <- pathway_daa(abundance = ko_abundance %>% column_to_rownames("#NAME"), metadata = metadata, group = "Environment", daa_method = "LinDA")
daa_annotated_results_df <- pathway_annotation(pathway = "KO", daa_results_df = daa_results_df, ko_to_kegg = FALSE)

# Annotate KEGG
# daa_annotated_results_df <- pathway_annotation(pathway = "EC", daa_results_df = daa_results_df, ko_to_kegg = FALSE)

# Annotate MetaCyc Pathway
data("metacyc_abundance")
data("metadata")
# Please change column_to_rownames() to the feature column if you are not using example dataset
# Please change group to "your_group_column" if you are not using example dataset
metacyc_daa_results_df <- pathway_daa(abundance = metacyc_abundance %>% column_to_rownames("pathway"), metadata = metadata, group = "Environment", daa_method = "LinDA")
metacyc_daa_annotated_results_df <- pathway_annotation(pathway = "MetaCyc", daa_results_df = metacyc_daa_results_df, ko_to_kegg = FALSE)
```

### pathway_errorbar()

``` r
data("ko_abundance")
data("metadata")
kegg_abundance <- ko2kegg_abundance(data = ko_abundance) # Or use data(kegg_abundance)
# Please change group to "your_group_column" if you are not using example dataset
daa_results_df <- pathway_daa(kegg_abundance, metadata = metadata, group = "Environment", daa_method = "LinDA")
daa_annotated_results_df <- pathway_annotation(pathway = "KO", daa_results_df = daa_results_df, ko_to_kegg = TRUE)
# Please change Group to metadata$your_group_column if you are not using example dataset
p <- pathway_errorbar(abundance = kegg_abundance,
           daa_results_df = daa_annotated_results_df,
           Group = metadata$Environment,
           ko_to_kegg = TRUE,
           p_values_threshold = 0.05,
           order = "pathway_class",
           select = NULL,
           p_value_bar = TRUE,
           colors = NULL,
           x_lab = "pathway_name")

# If you want to analysis the EC. MetaCyc. KO without conversions.
data("metacyc_abundance")
data("metadata")
metacyc_daa_results_df <- pathway_daa(abundance = metacyc_abundance %>% column_to_rownames("pathway"), metadata = metadata, group = "Environment", daa_method = "LinDA")
metacyc_daa_annotated_results_df <- pathway_annotation(pathway = "MetaCyc", daa_results_df = metacyc_daa_results_df, ko_to_kegg = FALSE)
p <- pathway_errorbar(abundance = metacyc_abundance %>% column_to_rownames("pathway"),
           daa_results_df = metacyc_daa_annotated_results_df,
           Group = metadata$Environment,
           ko_to_kegg = FALSE,
           p_values_threshold = 0.05,
           order = "group",
           select = NULL,
           p_value_bar = TRUE,
           colors = NULL,
           x_lab = "description")
```

### pathway_heatmap()

In this section, we will demonstrate how to create a pathway heatmap
using the `pathway_heatmap` function in the ggpicrust2 package. This
function visualizes the relative abundance of pathways in different
samples.

Use the fake dataset

``` r
# Create example functional pathway abundance data
abundance_example <- matrix(rnorm(30), nrow = 3, ncol = 10)
colnames(abundance_example) <- paste0("Sample", 1:10)
rownames(abundance_example) <- c("PathwayA", "PathwayB", "PathwayC")

# Create example metadata
# Please change your sample id's column name to sample_name
metadata_example <- data.frame(sample_name = colnames(abundance_example),
                               group = factor(rep(c("Control", "Treatment"), each = 5)))

# Create a heatmap
pathway_heatmap(abundance_example, metadata_example, "group")
```

Use the real dataset

``` r
library(tidyverse)
library(ggh4x)
library(ggpicrust2)
# Load the data
data("metacyc_abundance")

# Load the metadata
data("metadata")

# Perform differential abundance analysis
metacyc_daa_results_df <- pathway_daa(
  abundance = metacyc_abundance %>% column_to_rownames("pathway"),
  metadata = metadata,
  group = "Environment",
  daa_method = "LinDA"
)

# Annotate the results
annotated_metacyc_daa_results_df <- pathway_annotation(
  pathway = "MetaCyc",
  daa_results_df = metacyc_daa_results_df,
  ko_to_kegg = FALSE
)

# Filter features with p < 0.05
feature_with_p_0.05 <- metacyc_daa_results_df %>%
  filter(p_adjust < 0.05)

# Create the heatmap
pathway_heatmap(
  abundance = metacyc_abundance %>%
    right_join(
      annotated_metacyc_daa_results_df %>% select(all_of(c("feature","description"))),
      by = c("pathway" = "feature")
    ) %>%
    filter(pathway %in% feature_with_p_0.05$feature) %>%
    select(-"pathway") %>%
    column_to_rownames("description"),
  metadata = metadata,
  group = "Environment"
)
```

### pathway_pca()

In this section, we will demonstrate how to perform Principal Component
Analysis (PCA) on functional pathway abundance data and create
visualizations of the PCA results using the `pathway_pca` function in
the ggpicrust2 package.

Use the fake dataset

``` r
# Create example functional pathway abundance data
abundance_example <- matrix(rnorm(30), nrow = 3, ncol = 10)
colnames(kegg_abundance_example) <- paste0("Sample", 1:10)
rownames(kegg_abundance_example) <- c("PathwayA", "PathwayB", "PathwayC")

# Create example metadata
metadata_example <- data.frame(sample_name = colnames(kegg_abundance_example),
                                group = factor(rep(c("Control", "Treatment"), each = 5)))
# Perform PCA and create visualizations
pathway_pca(abundance = abundance_example, metadata = metadata_example, "group")
```

Use the real dataset

``` r
# Create example functional pathway abundance data
data("metacyc_abundance")
data("metadata")

pathway_pca(abundance = metacyc_abundance %>% column_to_rownames("pathway"), metadata = metadata, group = "Environment")
```

### compare_metagenome_results()

``` r
library(ComplexHeatmap)
set.seed(123)
# First metagenome
metagenome1 <- abs(matrix(rnorm(1000), nrow = 100, ncol = 10))
rownames(metagenome1) <- paste0("KO", 1:100)
colnames(metagenome1) <- paste0("sample", 1:10)
# Second metagenome
metagenome2 <- abs(matrix(rnorm(1000), nrow = 100, ncol = 10))
rownames(metagenome2) <- paste0("KO", 1:100)
colnames(metagenome2) <- paste0("sample", 1:10)
# Put the metagenomes into a list
metagenomes <- list(metagenome1, metagenome2)
# Define names
names <- c("metagenome1", "metagenome2")
# Call the function
results <- compare_metagenome_results(metagenomes, names)
# Print the correlation matrix
print(results$correlation$cor_matrix)
# Print the p-value matrix
print(results$correlation$p_matrix)
```

### taxa contribution workflow

The taxa contribution workflow connects PICRUSt2 per-sequence outputs
with downstream pathway interpretation. Use `read_contrib_file()` for
`pred_metagenome_contrib.tsv`, or `read_strat_file()` for
`pred_metagenome_strat.tsv`, then aggregate to the taxonomic level you
want to visualize.

``` r
library(ggpicrust2)

# Parse PICRUSt2 per-sequence contribution output
contrib_data <- read_contrib_file("pred_metagenome_contrib.tsv")

# Optional: use pathway-level DAA results to keep only significant pathways
data("kegg_abundance")
data("metadata")

daa_results <- pathway_daa(
  abundance = kegg_abundance,
  metadata = metadata,
  group = "Environment",
  daa_method = "ALDEx2"
)

# Aggregate contributions to genus level and keep top taxa
taxa_contrib <- aggregate_taxa_contributions(
  contrib_data = contrib_data,
  taxonomy = your_taxonomy_table,
  tax_level = "Genus",
  top_n = 10,
  daa_results_df = daa_results
)

# Visualize per-sample contributions
taxa_contribution_bar(
  contrib_agg = taxa_contrib,
  metadata = metadata,
  group = "Environment",
  facet_by = "function"
)

# Summarize mean contribution patterns across taxa and functions
taxa_contribution_heatmap(
  contrib_agg = taxa_contrib,
  n_functions = 20
)
```

### pathway_gsea()

The `pathway_gsea()` function performs Gene Set Enrichment Analysis
(GSEA) on PICRUSt2 predicted functional profiles. GSEA is a powerful
method for identifying enriched pathways between different conditions,
offering a more nuanced understanding of functional differences compared
to traditional differential abundance analysis.

**New in v2.5.6**: The function now supports limma’s `camera` and `fry`
methods, which provide more reliable p-values by accounting for
inter-gene correlations (Wu et al., 2012). The `camera` method is now
the default and recommended approach.

| Method | Type | Covariate Support | Description |
|----|----|----|----|
| `camera` (default) | Competitive | ✅ Yes | **Recommended.** Accounts for inter-gene correlations |
| `fry` | Self-contained | ✅ Yes | Fast rotation test, efficient for large gene set collections |
| `fgsea` | Preranked | ❌ No | Fast preranked GSEA (legacy) |
| `clusterProfiler` | Preranked | ❌ No | Traditional GSEA implementation (legacy) |

``` r
library(ggpicrust2)
library(tidyverse)

# Load example data
data("ko_abundance")
data("metadata")

# Perform GSEA analysis with camera (recommended)
gsea_results <- pathway_gsea(
  abundance = ko_abundance %>% column_to_rownames("#NAME"),
  metadata = metadata,
  group = "Environment",
  method = "camera",   # Recommended: accounts for inter-gene correlations
  pathway_type = "KEGG",
  min_size = 5,
  max_size = 500
)

# With covariate adjustment (powerful feature of camera/fry)
gsea_results_adjusted <- pathway_gsea(
  abundance = ko_abundance %>% column_to_rownames("#NAME"),
  metadata = metadata,
  group = "Disease",
  covariates = c("age", "sex"),  # Adjust for confounders
  method = "camera",
  pathway_type = "KEGG"
)

# View the results
head(gsea_results)
```

### visualize_gsea()

The `visualize_gsea()` function creates various visualizations for GSEA
results, including enrichment plots, dot plots, network plots, and
heatmaps.

``` r
library(ggpicrust2)
library(tidyverse)

# Load example data and perform GSEA
data("ko_abundance")
data("metadata")

gsea_results <- pathway_gsea(
  abundance = ko_abundance %>% column_to_rownames("#NAME"),
  metadata = metadata,
  group = "Environment"
)

# Create an enrichment plot for a specific pathway
# Annotate results so plots can use readable pathway names
annotated_results <- gsea_pathway_annotation(
  gsea_results = gsea_results,
  pathway_type = "KEGG"
)

# Create an enrichment-style summary plot
enrichment_plot <- visualize_gsea(
  gsea_results = annotated_results,
  plot_type = "enrichment_plot",
  n_pathways = 10
)

# Create a dot plot showing top enriched pathways
dot_plot <- visualize_gsea(
  gsea_results = annotated_results,
  plot_type = "dotplot",
  n_pathways = 20,  # Show top 20 pathways
  sort_by = "NES"   # Sort by Normalized Enrichment Score
)

# Create a network plot showing pathway relationships
network_plot <- visualize_gsea(
  gsea_results = gsea_results,
  plot_type = "network",
  n_pathways = 15,
  network_params = list(
    similarity_measure = "jaccard",
    similarity_cutoff = 0.2,
    layout = "fruchterman",
    node_color_by = "NES"
  )
)

# Create a heatmap showing pathway gene expression
heatmap_plot <- visualize_gsea(
  gsea_results = gsea_results,
  plot_type = "heatmap",
  abundance = ko_abundance %>% column_to_rownames("#NAME"),
  metadata = metadata,
  group = "Environment",
  n_pathways = 10,
  heatmap_params = list(
    cluster_rows = TRUE,
    cluster_columns = TRUE,
    show_column_names = TRUE,
    show_row_names = FALSE
  )
)
```

### compare_gsea_daa()

The `compare_gsea_daa()` function compares results from GSEA and
differential abundance analysis (DAA) to identify pathways that are
consistently identified by both methods or uniquely identified by each
method.

``` r
library(ggpicrust2)
library(tidyverse)

# Load example data
data("ko_abundance")
data("metadata")

# Prepare pathway-level abundance for DAA so identifiers match GSEA pathway IDs
kegg_pathway_abundance <- ko2kegg_abundance(data = ko_abundance)

# Perform GSEA analysis
gsea_results <- pathway_gsea(
  abundance = ko_abundance %>% column_to_rownames("#NAME"),
  metadata = metadata,
  group = "Environment"
)

# Perform DAA analysis
daa_results <- pathway_daa(
  abundance = kegg_pathway_abundance,
  metadata = metadata,
  group = "Environment",
  daa_method = "ALDEx2"
)

# Compare GSEA and DAA results
comparison <- compare_gsea_daa(
  gsea_results = gsea_results,
  daa_results = daa_results,
  p_threshold = 0.05,
  plot_type = "venn"  # Can be "venn", "upset", or "scatter"
)

# View the comparison plot
comparison$plot

# View the overlapping pathways
head(comparison$results$overlap)
```

### gsea_pathway_annotation()

The `gsea_pathway_annotation()` function annotates GSEA results with
pathway information, including pathway names, descriptions, and
classifications.

``` r
library(ggpicrust2)
library(tidyverse)

# Load example data and perform GSEA
data("ko_abundance")
data("metadata")

gsea_results <- pathway_gsea(
  abundance = ko_abundance %>% column_to_rownames("#NAME"),
  metadata = metadata,
  group = "Environment"
)

# Annotate GSEA results
annotated_results <- gsea_pathway_annotation(
  gsea_results = gsea_results,
  pathway_type = "KEGG"
)

# View the annotated results
head(annotated_results)
```

## FAQ

### Issue 1: pathway_errorbar error

When using `pathway_errorbar` with the following parameters:

``` r
pathway_errorbar(abundance = abundance,
                 daa_results_df = daa_results_df,
                 Group = metadata$Environment,
                 ko_to_kegg = TRUE,
                 p_values_threshold = 0.05,
                 order = "pathway_class",
                 select = NULL,
                 p_value_bar = TRUE,
                 colors = NULL,
                 x_lab = "pathway_name")
```

You may encounter an error:

    Error in `ggplot_add()`:
    ! Can't add `e2` to a <ggplot> object.
    Run `rlang::last_trace()` to see where the error occurred.

Make sure you have the `patchwork` package loaded:

``` r
library(patchwork)
```

### Issue 2: guide_train.prism_offset_minor error

You may encounter an error with `guide_train.prism_offset_minor`:

    Error in guide_train.prism_offset_minor(guide, panel_params[[aesthetic]]) :
      No minor breaks exist, guide_prism_offset_minor needs minor breaks to work

    Error in get(as.character(FUN)，mode = "function"object envir = envir)
    guide_prism_offset_minor' of mode'function' was not found

Ensure that the `ggprism` package is loaded:

``` r
library(ggprism)
```

### Issue 3: SSL certificate problem

When encountering the following error:

    SSL peer certificate or SSH remote key was not OK: [rest.kegg.jp] SSL certificate problem: certificate has expired

If you are in China, make sure your computer network can bypass the
firewall.

### Issue 4: Bad Request (HTTP 400)

When encountering the following error:

    Error in .getUrl(url, .flatFileParser) : Bad Request (HTTP 400).

Please restart R session.

### Issue 5: Error in grid.Call(C_textBounds, as.graphicsAnnot(xlabel),x$x, x$y, :

When encountering the following error:

    Error in grid.Call(C_textBounds, as.graphicsAnnot(xlabel),x$x, x$y, :

Please having some required fonts installed. You can refer to this
[thread](https://stackoverflow.com/questions/71362738/r-error-in-grid-callc-textbounds-as-graphicsannotxlabel-xx-xy-polygo).

### Issue 6: Visualization becomes cluttered when there are more than 30 features of statistical significance.

When faced with this issue, consider the following solutions:

**Solution 1: Utilize the ‘select’ parameter**

The ‘select’ parameter allows you to specify which features you wish to
visualize. Here’s an example of how you can apply this in your code:

    ggpicrust2::pathway_errorbar(
      abundance = kegg_abundance,
      daa_results_df = daa_results_df_annotated,
      Group = metadata$Day,
      p_values_threshold = 0.05,
      order = "pathway_class",
      select = c("ko05340", "ko00564", "ko00680", "ko00562", "ko03030", "ko00561", "ko00440", "ko00250", "ko00740", "ko04940", "ko00010", "ko00195", "ko00760", "ko00920", "ko00311", "ko00310", "ko04146", "ko00600", "ko04141", "ko04142", "ko00604", "ko04260", "ko00909", "ko04973", "ko00510", "ko04974"),
      ko_to_kegg = TRUE,
      p_value_bar = FALSE,
      colors = NULL,
      x_lab = "pathway_name"
    )

**Solution 2: Limit to the Top 20 features**

If there are too many significant features to visualize effectively, you
might consider limiting your visualization to the top 20 features with
the smallest adjusted p-values:

    daa_results_df_annotated <- daa_results_df_annotated[!is.na(daa_results_df_annotated$pathway_name),]

    daa_results_df_annotated$p_adjust <- round(daa_results_df_annotated$p_adjust,5)

    low_p_feature <- daa_results_df_annotated[order(daa_results_df_annotated$p_adjust), ]$feature[1:20]


    p <- ggpicrust2::pathway_errorbar(
      abundance = kegg_abundance,
      daa_results_df = daa_results_df_annotated,
      Group = metadata$Day,
      p_values_threshold = 0.05,
      order = "pathway_class",
      select = low_p_feature,
      ko_to_kegg = TRUE,
      p_value_bar = FALSE,
      colors = NULL,
      x_lab = "pathway_name")

### Issue 7: There are no statistically significant biomarkers

If you are not finding any statistically significant biomarkers in your
analysis, there could be several reasons for this:

1.  **The true difference between your groups is small or
    non-existent.** If the microbial communities or pathways you’re
    comparing are truly similar, then it’s correct and expected that you
    won’t find significant differences.

2.  **Your sample size might be too small to detect the differences.**
    Statistical power, the ability to detect differences if they exist,
    increases with sample size.

3.  **The variation within your groups might be too large.** If there’s
    a lot of variation in microbial communities within a single group,
    it can be hard to detect differences between groups.

Here are a few suggestions:

1.  **Increase your sample size**: If possible, adding more samples to
    your analysis can increase your statistical power, making it easier
    to detect significant differences.

2.  **Decrease intra-group variation**: If there’s a lot of variation
    within your groups, consider whether there are outliers or subgroups
    that are driving this variation. You might need to clean your data,
    or to stratify your analysis to account for these subgroups.

3.  **Change your statistical method or adjust parameters**: Depending
    on the nature of your data and your specific question, different
    statistical methods might be more or less powerful. If you’re
    currently using a parametric test, consider using a non-parametric
    test, or vice versa. Also, consider whether adjusting the parameters
    of your current test might help.

Remember, not finding significant results is also a result and can be
informative, as it might indicate that there are no substantial
differences between the groups you’re studying. It’s important to
interpret your results in the context of your specific study and not to
force statistical significance where there isn’t any.

With these strategies, you should be able to create a more readable and
informative visualization, even when dealing with a large number of
significant features.

## Author’s Other Projects

1.  [MicrobiomeStat](https://www.microbiomestat.wiki/): The
    MicrobiomeStat package is a dedicated R tool for exploring
    longitudinal microbiome data. It also accommodates multi-omics data
    and cross-sectional studies, valuing the collective efforts within
    the community. This tool aims to support researchers through their
    extensive biological inquiries over time, with a spirit of gratitude
    towards the community’s existing resources and a collaborative ethos
    for furthering microbiome research.

If you’re interested in helping to test and develop MicrobiomeStat,
please contact <cafferychen7850@gmail.com>.

2.  [MicrobiomeGallery](https://cafferyyang.shinyapps.io/MicrobiomeGallery/):
    This is a web-based platform currently under development, which aims
    to provide a space for sharing microbiome data visualization code
    and datasets.

<figure>
<img
src="https://raw.githubusercontent.com/cafferychen777/ggpicrust2_paper/main/paper_figure/MicrobiomeGallery_preview.jpg"
alt="Preview of related microbiome visualization tools" />
<figcaption aria-hidden="true">Preview of related microbiome
visualization tools</figcaption>
</figure>

We look forward to sharing more updates as these projects progress.
