ggpicrust2 (version 2.1.2)

pathway_daa: 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,
  ...
)

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.

Arguments

abundance

A data frame or matrix containing predicted functional pathway abundance, with pathways/features as rows and samples as columns. The column names should match the sample names in metadata. Values should be counts or abundance measurements.

metadata

A data frame or tibble containing sample information. Must include a 'sample' column with sample identifiers matching the column names in abundance data.

group

Character string specifying the column name in metadata that contains group information for differential abundance analysis.

daa_method

Character string specifying the method for differential abundance analysis. Available choices are:

• "ALDEx2": ANOVA-Like Differential Expression tool

• "DESeq2": Differential expression analysis based on negative binomial distribution

• "edgeR": Exact test for differences between groups using negative binomial model

• "limma voom": Limma-voom framework for RNA-seq analysis

• "metagenomeSeq": Zero-inflated Gaussian mixture model

• "LinDA": Linear models for differential abundance analysis

• "Maaslin2": Multivariate Association with Linear Models

• "Lefser": Linear discriminant analysis effect size

Default is "ALDEx2".

select

Vector of sample names to include in the analysis. If NULL (default), all samples are included.

p.adjust

Character string specifying the method for p-value adjustment. Choices are:

• "BH": Benjamini-Hochberg procedure (default)

• "holm": Holm's step-down method

• "bonferroni": Bonferroni correction

• "hochberg": Hochberg's step-up method

• "fdr": False Discovery Rate

• "none": No adjustment

reference

Character string specifying the reference level for the group comparison. If NULL (default), the first level is used as reference.

...

Additional arguments passed to the specific DAA method

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

# \donttest{
# 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"))
# }