ggpicrust2

citation("ggpicrust2")

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

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)
}

# 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

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
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", ]

# 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
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"))

# 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.

# 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 = "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 = "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 = "BH", reference = NULL)

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)

# 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)

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")

# 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")

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"
)

# 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")

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

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

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)

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
)

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)

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
  )
)

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)

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)

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")

library(patchwork)

library(ggprism)