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name: bio-microbiome-differential-abundance description: Differential abundance testing for microbiome data using compositionally-aware methods like ALDEx2, ANCOM-BC2, and MaAsLin2. Use when identifying taxa that differ between experimental groups while accounting for the compositional nature of microbiome data. tool_type: r primary_tool: ALDEx2 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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Differential Abundance Testing

The Compositionality Problem

Microbiome data is compositional - abundances are relative, not absolute. Standard tests (t-test, DESeq2) can give false positives.

ALDEx2 (Recommended)

library(ALDEx2)
library(phyloseq)

ps <- readRDS('phyloseq_object.rds')
otu <- as.data.frame(otu_table(ps))
if (!taxa_are_rows(ps)) otu <- t(otu)

# Define groups
groups <- sample_data(ps)$Group

# Run ALDEx2 (CLR transformation + Welch's t-test)
aldex_results <- aldex(otu, groups, mc.samples = 128, test = 'welch',
                       effect = TRUE, include.sample.summary = FALSE)

# Filter significant
sig_aldex <- aldex_results[aldex_results$we.eBH < 0.05 & abs(aldex_results$effect) > 1, ]

# Volcano-like plot
aldex.plot(aldex_results, type = 'MW', test = 'welch')

ANCOM-BC2 (Recommended)

library(ANCOMBC)

# Run ANCOM-BC2 with sensitivity analysis
ancom_result <- ancombc2(data = ps, fix_formula = 'Group',
                         p_adj_method = 'BH', pseudo_sens = TRUE,
                         prv_cut = 0.1, lib_cut = 1000,
                         group = 'Group', struc_zero = TRUE)

# Extract results (includes sensitivity analysis)
res_df <- ancom_result$res

# Primary results
sig_ancom <- res_df[res_df$diff_Group == TRUE, ]

# Check sensitivity (passed_ss = passed sensitivity analysis)
robust_hits <- res_df[res_df$diff_Group == TRUE & res_df$passed_ss_Group == TRUE, ]

MaAsLin2

library(Maaslin2)

# Prepare data
features <- as.data.frame(t(otu_table(ps)))
metadata <- as.data.frame(sample_data(ps))

# Run MaAsLin2
maaslin_results <- Maaslin2(
    input_data = features,
    input_metadata = metadata,
    output = 'maaslin2_output',
    fixed_effects = 'Group',
    normalization = 'CLR',
    transform = 'NONE',
    analysis_method = 'LM'
)

# Results in maaslin2_output/all_results.tsv
sig_maaslin <- maaslin_results$results[maaslin_results$results$qval < 0.05, ]

DESeq2 (with caution)

library(DESeq2)
library(phyloseq)

# Convert to DESeq2 (use geometric mean of poscounts)
ps_deseq <- ps
ps_deseq <- prune_samples(sample_sums(ps_deseq) > 1000, ps_deseq)

dds <- phyloseq_to_deseq2(ps_deseq, ~ Group)
dds <- DESeq(dds, test = 'Wald', fitType = 'parametric', sfType = 'poscounts')

res <- results(dds, alpha = 0.05)
sig_deseq <- res[which(res$padj < 0.05 & abs(res$log2FoldChange) > 1), ]

Visualization

library(ggplot2)

# Volcano plot from ALDEx2
ggplot(aldex_results, aes(x = effect, y = -log10(we.eBH))) +
    geom_point(aes(color = we.eBH < 0.05 & abs(effect) > 1), alpha = 0.6) +
    geom_hline(yintercept = -log10(0.05), linetype = 'dashed') +
    geom_vline(xintercept = c(-1, 1), linetype = 'dashed') +
    scale_color_manual(values = c('grey', 'red')) +
    theme_minimal() +
    labs(x = 'Effect Size', y = '-log10(Adjusted P-value)')

Method Comparison

Method	Handles	Covariates	Speed	Notes
ALDEx2	Compositionality	Limited	Slow	Best for simple designs
ANCOM-BC2	Compositionality, zeros, sensitivity	Yes	Medium	Recommended for complex designs
MaAsLin2	Compositionality	Yes	Fast	Good for longitudinal
DESeq2	Sparsity (less ideal)	Yes	Fast	Use with caution for microbiome

Related Skills

• diversity-analysis - Identify overall differences first
• differential-expression/deseq2-basics - Similar concepts
• pathway-analysis/go-enrichment - Enrichment of differential taxa

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