pathway_daa | R Documentation |
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.
pathway_daa(
abundance,
metadata,
group,
daa_method = "ALDEx2",
select = NULL,
p.adjust = "BH",
reference = NULL,
...
)
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:
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:
|
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 |
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.
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.
# 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"))
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