Calculate rank metric for GSEA

Description

Calculate rank metric for GSEA

Usage

calculate_rank_metric(abundance, metadata, group, method = "signal2noise")

Arguments

Value

A named vector of ranking statistics

Compare the Consistency of Statistically Significant Features

Description

This function compares the consistency and inconsistency of statistically significant features obtained using different methods in 'pathway_daa' from the 'ggpicrust2' package. It creates a report showing the number of common and different features identified by each method, and the features themselves.

Arguments

Value

A data frame with the comparison results. The data frame has the following columns:

• method: The name of the method.

• num_features: The total number of statistically significant features obtained by the method.

• num_common_features: The number of features that are common to other methods.

• num_diff_features: The number of features that are different from other methods.

• diff_features: The names of the features that are different from other methods.

Examples

library(magrittr)
library(ggpicrust2)
library(tibble)
data("metacyc_abundance")
data("metadata")

# Run pathway_daa function for multiple methods
methods <- c("DESeq2", "edgeR","Maaslin2")
daa_results_list <- lapply(methods, function(method) {
pathway_daa(abundance = metacyc_abundance %>% column_to_rownames("pathway"),
metadata = metadata, group = "Environment", daa_method = method)
})

names(daa_results_list) <- methods
# Correct Maaslin2 feature names by replacing dots with hyphens.
# Note: When using Maaslin2 as the differential abundance analysis method,
# it modifies the original feature names by replacing hyphens (-) with dots (.).
# This replacement can cause inconsistencies when trying to compare results from Maaslin2
# with those from other methods that do not modify feature names.
# Therefore, this line of code reverses that replacement, converting the dots back into
# hyphens for accurate and consistent comparisons across different methods.
daa_results_list[["Maaslin2"]]$feature <- gsub("\\.", "-", daa_results_list[["Maaslin2"]]$feature)

# Compare results across different methods
comparison_results <- compare_daa_results(daa_results_list = daa_results_list,
method_names = c("DESeq2", "edgeR", "Maaslin2"))

Compare GSEA and DAA results

Description

This function compares the results from Gene Set Enrichment Analysis (GSEA) and Differential Abundance Analysis (DAA) to identify similarities and differences.

Usage

compare_gsea_daa(
  gsea_results,
  daa_results,
  plot_type = "venn",
  p_threshold = 0.05
)

Arguments

Value

A ggplot2 object or a list containing the plot and comparison results

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]

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

# Run DAA analysis
daa_results <- pathway_daa(
  abundance = abundance_data,
  metadata = metadata,
  group = "Environment"
)

# Compare results
comparison <- compare_gsea_daa(
  gsea_results = gsea_results,
  daa_results = daa_results,
  plot_type = "venn"
)

## End(Not run)

Compare Metagenome Results

Description

Compare Metagenome Results

Arguments

Value

A list containing two elements:

• "daa": a list of results from the 'pathway_daa' function. Each result is a data frame containing the differential abundance analysis results with columns for the feature ID, the test statistic, the raw p-value, and the adjusted p-value.

• "correlation": a list with two elements: "cor_matrix" and "p_matrix", which are matrices of Spearman correlation coefficients and their corresponding p-values, respectively, between every pair of metagenomes.

Examples

library(dplyr)
library(ComplexHeatmap)
# Generate example data
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, daa_method = "LinDA")
# Print the correlation matrix
print(results$correlation$cor_matrix)
# Print the p-value matrix
print(results$correlation$p_matrix)

Create heatmap visualization of GSEA results

Description

Create heatmap visualization of GSEA results

Usage

create_heatmap_plot(
  gsea_results,
  abundance,
  metadata,
  group,
  n_pathways = 20,
  cluster_rows = TRUE,
  cluster_columns = TRUE,
  show_rownames = TRUE,
  annotation_colors = NULL
)

Arguments

Value

A ComplexHeatmap object

Create network visualization of GSEA results

Description

Create network visualization of GSEA results

Usage

create_network_plot(
  gsea_results,
  similarity_measure = "jaccard",
  similarity_cutoff = 0.3,
  n_pathways = 20,
  layout = "fruchterman",
  node_color_by = "NES",
  edge_width_by = "similarity"
)

Arguments

Value

A ggplot2 object

Differentially Abundant Analysis Results with Annotation

Description

This is a result dataset after processing 'kegg_abundance' through the 'pathway_daa' with the LinDA method and further annotation with 'pathway_annotation'.

Usage

daa_annotated_results_df

Format

A data frame with 10 variables:

adj_method

Method used for adjusting p-values.

feature

Feature being tested.

group1

One group in the comparison.

group2

The other group in the comparison.

method

Statistical test used.

p_adjust

Adjusted p-value.

p_values

P-values from the statistical test.

pathway_class

Class of the pathway.

pathway_description

Description of the pathway.

pathway_map

Map of the pathway.

pathway_name

Name of the pathway.

Source

From ggpicrust2 package demonstration.

References

Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020.

DAA Results Dataset

Description

This dataset is the result of processing 'kegg_abundance' through the 'LinDA' method in the 'pathway_daa' function. It includes information about the feature, groups compared, p values, and method used.

Usage

daa_results_df

Format

A data frame with columns:

adj_method

Method used for p-value adjustment.

feature

The feature (pathway) being compared.

group1

The first group in the comparison.

group2

The second group in the comparison.

method

The method used for the comparison.

p_adjust

The adjusted p-value from the comparison.

p_values

The raw p-value from the comparison.

Source

From ggpicrust2 package demonstration.

References

Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020.

This function integrates pathway name/description annotations, ten of the most advanced differential abundance (DA) methods, and visualization of DA results.

Description

This function integrates pathway name/description annotations, ten of the most advanced differential abundance (DA) methods, and visualization of DA results.

Usage

ggpicrust2(
  file = NULL,
  data = NULL,
  metadata,
  group,
  pathway,
  daa_method = "ALDEx2",
  ko_to_kegg = FALSE,
  p.adjust = "BH",
  order = "group",
  p_values_bar = TRUE,
  x_lab = NULL,
  select = NULL,
  reference = NULL,
  colors = NULL
)

Arguments

Value

daa.results.df, a dataframe of DA results A list of sub-lists, each containing a ggplot2 plot ('plot') and a dataframe of differential abundance results ('results') for a specific DA method. Each plot visualizes the differential abundance results of a specific DA method, and the corresponding dataframe contains the results used to create the plot.

Examples

## Not run: 
# Load necessary data: abundance data and metadata
abundance_file <- "path/to/your/abundance_file.tsv"
metadata <- read.csv("path/to/your/metadata.csv")

# Run ggpicrust2 with input file path
results_file_input <- ggpicrust2(file = abundance_file,
                                 metadata = metadata,
                                 group = "your_group_column",
                                 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",
                                 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")

## End(Not run)

Integrated analysis with ggpicrust2 including GSEA

Description

This function extends the ggpicrust2 functionality to include Gene Set Enrichment Analysis (GSEA).

Usage

ggpicrust2_extended(..., run_gsea = FALSE, gsea_params = list())

Arguments

Value

A list containing ggpicrust2 and GSEA results

Examples

## Not run: 
# Load example data
data(ko_abundance)
data(metadata)

# Run integrated analysis
integrated_results <- ggpicrust2_extended(
  data = ko_abundance,
  metadata = metadata,
  group = "Environment",
  pathway = "KO",
  daa_method = "LinDA",
  ko_to_kegg = TRUE,
  run_gsea = TRUE,
  gsea_params = list(
    method = "fgsea",
    rank_method = "signal2noise",
    nperm = 1000
  )
)

# Access DAA results
daa_results <- integrated_results$daa_results

# Access GSEA results
gsea_results <- integrated_results$gsea_results

# Access plots
daa_plot <- integrated_results$daa_plot
gsea_plot <- integrated_results$gsea_plot

## End(Not run)

Annotate GSEA results with pathway information

Description

This function adds pathway annotations to GSEA results, including pathway names, descriptions, and classifications.

Usage

gsea_pathway_annotation(gsea_results, pathway_type = "KEGG")

Arguments

Value

A data frame with annotated GSEA results

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]

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

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

## End(Not run)

Import Differential Abundance Analysis (DAA) results from MicrobiomeAnalyst

Description

This function imports DAA results from an external platform such as MicrobiomeAnalyst. It can be used to compare the results obtained from different platforms.

Arguments

Value

a data frame containing the DAA results from MicrobiomeAnalyst with additional columns for the method and group levels.

Examples

## Not run: 
# Assuming you have a CSV file named "DAA_results.csv" in your current directory
daa_results <- import_MicrobiomeAnalyst_daa_results(file_path = "DAA_results.csv")

## End(Not run)

KEGG Abundance Dataset

Description

A dataset derived from 'ko_abundance' by the function 'ko2kegg_abundance' in the ggpicrust2 package. Each row corresponds to a KEGG pathway, and each column corresponds to a sample.

Usage

kegg_abundance

Format

A data frame where rownames are KEGG pathways and column names are individual sample names, including: "SRR11393730", "SRR11393731", "SRR11393732", "SRR11393733", "SRR11393734", "SRR11393735", "SRR11393736", "SRR11393737", "SRR11393738", "SRR11393739", "SRR11393740", "SRR11393741", "SRR11393742", "SRR11393743", "SRR11393744", "SRR11393745", "SRR11393746", "SRR11393747", "SRR11393748", "SRR11393749", "SRR11393750", "SRR11393751", "SRR11393752", "SRR11393753", "SRR11393754", "SRR11393755", "SRR11393756", "SRR11393757", "SRR11393758", "SRR11393759", "SRR11393760", "SRR11393761", "SRR11393762", "SRR11393763", "SRR11393764", "SRR11393765", "SRR11393766", "SRR11393767", "SRR11393768", "SRR11393769", "SRR11393770", "SRR11393771", "SRR11393772", "SRR11393773", "SRR11393774", "SRR11393775", "SRR11393776", "SRR11393777", "SRR11393778", "SRR11393779"

Source

From ggpicrust2 package demonstration.

References

Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020.

Convert KO abundance in picrust2 export files to KEGG pathway abundance

Description

This function takes a file containing KO (KEGG Orthology) abundance data in picrust2 export format and converts it to KEGG pathway abundance data. The input file should be in .tsv, .txt, or .csv format.

Usage

ko2kegg_abundance(file = NULL, data = NULL)

Arguments

Value

A data frame with KEGG pathway abundance values. Rows represent KEGG pathways, identified by their KEGG pathway IDs. Columns represent samples, identified by their sample IDs from the input file. Each cell contains the abundance of a specific KEGG pathway in a given sample, calculated by summing the abundances of the corresponding KOs in the input file.

Examples

## Not run: 
library(ggpicrust2)
library(readr)

# Example 1: Demonstration with a hypothetical input file

# Prepare an input file path
input_file <- "path/to/your/picrust2/results/pred_metagenome_unstrat.tsv"

# Run ko2kegg_abundance function
kegg_abundance <- ko2kegg_abundance(file = input_file)

# Alternatively, read the data from a file and use the data argument
file_path <- "path/to/your/picrust2/results/pred_metagenome_unstrat.tsv"
ko_abundance <- read_delim(file_path, delim = "\t")
kegg_abundance <- ko2kegg_abundance(data = ko_abundance)

# Example 2: Working with real data
# In this case, we're using an existing dataset from the ggpicrust2 package.

# Load the data
data(ko_abundance)

# Apply the ko2kegg_abundance function to our real dataset
kegg_abundance <- ko2kegg_abundance(data = ko_abundance)

## End(Not run)

KO Abundance Dataset

Description

This is a demonstration dataset from the ggpicrust2 package, representing the output of PICRUSt2. Each row represents a KO (KEGG Orthology) group, and each column corresponds to a sample.

Usage

ko_abundance

Format

A data frame where rownames are KO groups and column names include #NAME and individual sample names, such as: "#NAME", "SRR11393730", "SRR11393731", "SRR11393732", "SRR11393733", "SRR11393734", "SRR11393735", "SRR11393736", "SRR11393737", "SRR11393738", "SRR11393739", "SRR11393740", "SRR11393741", "SRR11393742", "SRR11393743", "SRR11393744", "SRR11393745", "SRR11393746", "SRR11393747", "SRR11393748", "SRR11393749", "SRR11393750", "SRR11393751", "SRR11393752", "SRR11393753", "SRR11393754", "SRR11393755", "SRR11393756", "SRR11393757", "SRR11393758", "SRR11393759", "SRR11393760", "SRR11393761", "SRR11393762", "SRR11393763", "SRR11393764", "SRR11393765", "SRR11393766", "SRR11393767", "SRR11393768", "SRR11393769", "SRR11393770", "SRR11393771", "SRR11393772", "SRR11393773", "SRR11393774", "SRR11393775", "SRR11393776", "SRR11393777", "SRR11393778", "SRR11393779"

Source

From ggpicrust2 package demonstration.

References

Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020.

MetaCyc Abundance Dataset

Description

This is a demonstration dataset from the ggpicrust2 package, representing the output of PICRUSt2. Each row represents a MetaCyc pathway, and each column corresponds to a sample.

Usage

metacyc_abundance

Format

A data frame where rownames are MetaCyc pathways and column names include "pathway" and individual sample names, such as: "pathway", "SRR11393730", "SRR11393731", "SRR11393732", "SRR11393733", "SRR11393734", "SRR11393735", "SRR11393736", "SRR11393737", "SRR11393738", "SRR11393739", "SRR11393740", "SRR11393741", "SRR11393742", "SRR11393743", "SRR11393744", "SRR11393745", "SRR11393746", "SRR11393747", "SRR11393748", "SRR11393749", "SRR11393750", "SRR11393751", "SRR11393752", "SRR11393753", "SRR11393754", "SRR11393755", "SRR11393756", "SRR11393757", "SRR11393758", "SRR11393759", "SRR11393760", "SRR11393761", "SRR11393762", "SRR11393763", "SRR11393764", "SRR11393765", "SRR11393766", "SRR11393767", "SRR11393768", "SRR11393769", "SRR11393770", "SRR11393771", "SRR11393772", "SRR11393773", "SRR11393774", "SRR11393775", "SRR11393776", "SRR11393777", "SRR11393778", "SRR11393779"

Source

From ggpicrust2 package demonstration.

References

Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020.

Metadata for ggpicrust2 Demonstration

Description

This is a demonstration dataset from the ggpicrust2 package. It provides the metadata required for the demonstration functions in the package. The dataset includes environmental information for each sample.

Usage

metadata

Format

A tibble with each row representing metadata for a sample.

Sample1

Metadata for Sample1, including Environment

Sample2

Metadata for Sample2, including Environment

...

...

Source

ggpicrust2 package demonstration.

References

Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020.

Pathway information annotation

Description

This function serves two main purposes: 1. Annotating pathway information from PICRUSt2 output files. 2. Annotating pathway information from the output of 'pathway_daa' function, and converting KO abundance to KEGG pathway abundance when 'ko_to_kegg' is set to TRUE.

Usage

pathway_annotation(
  file = NULL,
  pathway = NULL,
  daa_results_df = NULL,
  ko_to_kegg = FALSE,
  organism = NULL
)

Arguments

Value

A data frame with annotated pathway information. If using the function for the first use case, the output data frame will include the following columns:

• id: The pathway ID.

• description: The description of the pathway.

• sample1, sample2, ...: Abundance values for each sample.

If ko_to_kegg is set to TRUE, the output data frame will also include the following columns:

• pathway_name: The name of the KEGG pathway.

• pathway_description: The description of the KEGG pathway.

• pathway_class: The class of the KEGG pathway.

• pathway_map: The KEGG pathway map ID.

When ko_to_kegg is TRUE, the function queries the KEGG database for pathway information. By default (organism = NULL), it retrieves generic KO information that is not specific to any organism. If you are interested in organism-specific pathway information, you can specify the KEGG organism code using the organism parameter.

Examples

## Not run: 
# Example 1: Annotate pathways from PICRUSt2 output file
pathway_annotation(file = "path/to/picrust2_output.tsv",
                              pathway = "KO")

## End(Not run)

## Not run: 
# Example 2: Annotate pathways from pathway_daa output
# Assuming you have daa_results from pathway_daa function
daa_results <- pathway_daa(abundance, metadata, group = "Group")
annotated_results <- pathway_annotation(pathway = "KO",
                              daa_results_df = daa_results,
                              ko_to_kegg = FALSE)

## End(Not run)

Differential Abundance Analysis for Predicted Functional Pathways

Description

Performs differential abundance analysis on predicted functional pathway data using various statistical methods. This function supports multiple methods for analyzing differences in pathway abundance between groups, including popular approaches like ALDEx2, DESeq2, edgeR, and others.

Usage

pathway_daa(
  abundance,
  metadata,
  group,
  daa_method = "ALDEx2",
  select = NULL,
  p.adjust = "BH",
  reference = NULL,
  ...
)

Arguments

Value

A data frame containing the differential abundance analysis results. The structure of the results depends on the chosen DAA method. For methods that support multi-group comparisons (like LinDA), when there are more than two groups, the results will contain separate rows for each feature in each pairwise comparison between the reference group and each non-reference group. The data frame includes the following columns:

• feature: Feature/pathway identifier

• method: The DAA method used

• group1: Reference group

• group2: Comparison group

• p_values: P-values for the comparison

• p_adjust: Adjusted p-values

• adj_method: Method used for p-value adjustment

Some methods may provide additional columns, such as log2FoldChange for effect size information.

References

• ALDEx2: Fernandes et al. (2014) Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome.

• DESeq2: Love et al. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology.

• edgeR: Robinson et al. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics.

• limma-voom: Law et al. (2014) voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology.

• metagenomeSeq: Paulson et al. (2013) Differential abundance analysis for microbial marker-gene surveys. Nature Methods.

• Maaslin2: Mallick et al. (2021) Multivariable Association Discovery in Population-scale Meta-omics Studies.

Examples

# 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 differential abundance analysis using ALDEx2
results <- pathway_daa(
  abundance = abundance_data,
  metadata = metadata,
  group = "Environment"
)

# Using a different method (DESeq2)
deseq_results <- pathway_daa(
  abundance = abundance_data,
  metadata = metadata,
  group = "Environment",
  daa_method = "DESeq2"
)

# Create example data with more samples
abundance <- data.frame(
  sample1 = c(10, 20, 30),
  sample2 = c(20, 30, 40),
  sample3 = c(30, 40, 50),
  sample4 = c(40, 50, 60),
  sample5 = c(50, 60, 70),
  row.names = c("pathway1", "pathway2", "pathway3")
)

metadata <- data.frame(
  sample = c("sample1", "sample2", "sample3", "sample4", "sample5"),
  group = c("control", "control", "treatment", "treatment", "treatment")
)

# Run differential abundance analysis using ALDEx2
results <- pathway_daa(abundance, metadata, "group")

# Using a different method (limma voom instead of DESeq2 for this small example)
limma_results <- pathway_daa(abundance, metadata, "group",
                            daa_method = "limma voom")

# Analyze specific samples only
subset_results <- pathway_daa(abundance, metadata, "group",
                             select = c("sample1", "sample2", "sample3", "sample4"))

The function pathway_errorbar() is used to visualize the results of functional pathway differential abundance analysis as error bar plots.

Description

The function pathway_errorbar() is used to visualize the results of functional pathway differential abundance analysis as error bar plots.

Arguments

Value

A ggplot2 plot showing the error bar plot of the differential abundance analysis results for the functional pathways. The plot visualizes the differential abundance results of a specific differential abundance analysis method. The corresponding dataframe contains the results used to create the plot.

Examples

## Not run: 
# Example 1: Analyzing KEGG pathway abundance
metadata <- read_delim(
  "path/to/your/metadata.txt",
  delim = "\t",
  escape_double = FALSE,
  trim_ws = TRUE
)

# data(metadata)

kegg_abundance <- ko2kegg_abundance(
  "path/to/your/pred_metagenome_unstrat.tsv"
)

# data(kegg_abundance)

# Please change group to "your_group_column" if you are not using example dataset
group <- "Environment"

daa_results_df <- pathway_daa(
  abundance = kegg_abundance,
  metadata = metadata,
  group = group,
  daa_method = "ALDEx2",
  select = NULL,
  reference = NULL
)

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

daa_annotated_sub_method_results_df <- pathway_annotation(
  pathway = "KO",
  daa_results_df = daa_sub_method_results_df,
  ko_to_kegg = TRUE
)

# Please change Group to metadata$your_group_column if you are not using example dataset
Group <- metadata$Environment

p <- pathway_errorbar(
  abundance = kegg_abundance,
  daa_results_df = daa_annotated_sub_method_results_df,
  Group = Group,
  p_values_threshold = 0.05,
  order = "pathway_class",
  select = daa_annotated_sub_method_results_df %>%
  arrange(p_adjust) %>%
  slice(1:20) %>%
  select("feature") %>% pull(),
  ko_to_kegg = TRUE,
  p_value_bar = TRUE,
  colors = NULL,
  x_lab = "pathway_name",
  log2_fold_change_color = "#FF5733" # Custom color for log2 fold change bars
)

# Example 2: Analyzing EC, MetaCyc, KO without conversions
metadata <- read_delim(
  "path/to/your/metadata.txt",
  delim = "\t",
  escape_double = FALSE,
  trim_ws = TRUE
)
# data(metadata)

metacyc_abundance <- read.delim("path/to/your/metacyc_abundance.tsv")

# data(metacyc_abundance)

group <- "Environment"

daa_results_df <- pathway_daa(
  abundance = metacyc_abundance %>% column_to_rownames("pathway"),
  metadata = metadata,
  group = group,
  daa_method = "LinDA",
  select = NULL,
  reference = NULL
)


daa_annotated_results_df <- pathway_annotation(
  pathway = "MetaCyc",
  daa_results_df = daa_results_df,
  ko_to_kegg = FALSE
)

Group <- metadata$Environment

p <- pathway_errorbar(
  abundance = metacyc_abundance %>% column_to_rownames("pathway"),
  daa_results_df = daa_annotated_results_df,
  Group = Group,
  p_values_threshold = 0.05,
  order = "group",
  select = NULL,
  ko_to_kegg = FALSE,
  p_value_bar = TRUE,
  colors = NULL,
  x_lab = "description",
  log2_fold_change_color = "#006400" # Dark green for log2 fold change bars
)

## End(Not run)

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
)

Arguments

Value

A data frame containing GSEA results

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]

# 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)

## End(Not run)

Create pathway heatmap

Description

This function creates a heatmap of the predicted functional pathway abundance data. The function first makes the abundance data relative, then converts the abundance data to a long format and orders the samples based on the environment information. The heatmap is then created using the 'ggplot2' library.

Arguments

Value

A ggplot heatmap object representing the heatmap of the predicted functional pathway abundance data.

Examples

library(ggpicrust2)
library(ggh4x)
library(dplyr)
library(tidyr)
library(tibble)
library(magrittr)

# Create example functional pathway abundance data
kegg_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))
)

# Custom colors for facet strips
custom_colors <- c("skyblue", "salmon")

# Create a heatmap using custom colors for facet strips
pathway_heatmap(kegg_abundance_example, metadata_example, "group", colors = custom_colors)

# Use real dataset
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"
)
annotated_metacyc_daa_results_df <- pathway_annotation(
  pathway = "MetaCyc",
  daa_results_df = metacyc_daa_results_df,
  ko_to_kegg = FALSE
)
feature_with_p_0.05 <- metacyc_daa_results_df %>% filter(p_adjust < 0.05)
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",
  colors = custom_colors,
  low_color = "#2166ac",  # Custom blue for low values
  mid_color = "#f7f7f7",  # Light gray for mid values
  high_color = "#b2182b"   # Custom red for high values
)

Perform Principal Component Analysis (PCA) on functional pathway abundance data

Description

This function performs PCA analysis on pathway abundance data and creates an informative visualization that includes a scatter plot of the first two principal components (PC1 vs PC2) with density plots for both PCs. The plot helps to visualize the clustering patterns and distribution of samples across different groups.

Usage

pathway_pca(abundance, metadata, group, colors = NULL)

Arguments

Details

The function performs several validations on input data:

• Abundance matrix must have at least 2 pathways and 3 samples

• All values in abundance matrix must be numeric

• Sample names must match between abundance and metadata

• Group column must exist in metadata

• If custom colors are provided, they must be valid color names or codes

Value

A ggplot object showing:

• Center: PCA scatter plot with confidence ellipses (95

• Top: Density plot for PC1

• Right: Density plot for PC2

Examples

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

# Create example metadata
metadata <- data.frame(
  sample_name = paste0("Sample", 1:10),
  group = factor(rep(c("Control", "Treatment"), each = 5))
)

# Basic PCA plot with default colors
pca_plot <- pathway_pca(abundance_data, metadata, "group")

# PCA plot with custom colors
pca_plot <- pathway_pca(
  abundance_data,
  metadata,
  "group",
  colors = c("blue", "red")  # One color per group
)


# Example with real data
data("metacyc_abundance")  # Load example pathway abundance data
data("metadata")          # Load example metadata

# Generate PCA plot
# Prepare abundance data
abundance_data <- as.data.frame(metacyc_abundance)
rownames(abundance_data) <- abundance_data$pathway
abundance_data <- abundance_data[, -which(names(abundance_data) == "pathway")]

# Create PCA plot
pathway_pca(
  abundance_data,
  metadata,
  "Environment",
  colors = c("green", "purple")
)

Prepare gene sets for GSEA

Description

Prepare gene sets for GSEA

Usage

prepare_gene_sets(pathway_type = "KEGG", organism = "ko")

Arguments

Value

A list of pathway gene sets

Run fast GSEA implementation

Description

Run fast GSEA implementation

Usage

run_fgsea(ranked_list, gene_sets, nperm = 1000, min_size = 10, max_size = 500)

Arguments

Value

A data frame of fgsea results

Safely Extract Elements from a List

Description

Safely extracts elements from a list, returning NA if the extraction fails

Usage

safe_extract(list, field, index = 1)

Arguments

Value

The extracted element if successful, NA if extraction fails

Examples

# Create a sample list
my_list <- list(
  a = list(x = 1:3),
  b = list(y = 4:6)
)

# Extract existing element
safe_extract(my_list, "a", 1)

# Extract non-existing element (returns NA)
safe_extract(my_list, "c", 1)

Visualize GSEA results

Description

This function creates various visualizations for Gene Set Enrichment Analysis (GSEA) results.

Usage

visualize_gsea(
  gsea_results,
  plot_type = "enrichment_plot",
  n_pathways = 20,
  sort_by = "p.adjust",
  colors = NULL,
  abundance = NULL,
  metadata = NULL,
  group = NULL,
  network_params = list(),
  heatmap_params = list()
)

Arguments

Value

A ggplot2 object or ComplexHeatmap object

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]

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

# Create enrichment plot
visualize_gsea(gsea_results, plot_type = "enrichment_plot", n_pathways = 10)

# Create dotplot
visualize_gsea(gsea_results, plot_type = "dotplot", n_pathways = 20)

# Create barplot
visualize_gsea(gsea_results, plot_type = "barplot", n_pathways = 15)

# Create network plot
visualize_gsea(gsea_results, plot_type = "network", n_pathways = 15)

# Create heatmap
visualize_gsea(
  gsea_results,
  plot_type = "heatmap",
  n_pathways = 15,
  abundance = abundance_data,
  metadata = metadata,
  group = "Environment"
)

## End(Not run)