--- 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 --- ## Version Compatibility Reference examples tested with: DESeq2 1.42+, ggplot2 3.5+, phyloseq 1.46+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Differential Abundance Testing **"Find which taxa differ between my groups"** → Identify differentially abundant taxa between experimental conditions using compositionally-aware methods that account for the relative nature of microbiome data. - R: `ALDEx2::aldex()` for CLR-transformed Welch's t-test - R: `ANCOMBC::ancombc2()` for bias-corrected log-linear models - R: `Maaslin2::Maaslin2()` for multivariable association ## The Compositionality Problem Microbiome data is compositional - abundances are relative, not absolute. Standard tests (t-test, DESeq2) can give false positives. ## ALDEx2 (Recommended) **Goal:** Identify differentially abundant taxa between groups using a compositionally-aware statistical framework. **Approach:** Apply CLR transformation with Monte Carlo sampling on the OTU table, run Welch's t-test per taxon, and filter by FDR-corrected p-value and effect size. ```r 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$effect) > 1, ] # Volcano-like plot aldex.plot(aldex_results, type = 'MW', test = 'welch') ``` ## ANCOM-BC2 (Recommended) ```r library(ANCOMBC) # Run ANCOM-BC2 with sensitivity analysis ancom_result <- ancombc2(data = ps, fix_formula = 'Group', p_adj_method = 'BH', pseudo_sens = TRUE, prv_cut = 0.1, lib_cut = 1000, group = 'Group', struc_zero = TRUE) # Extract results (includes sensitivity analysis) res_df <- ancom_result$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$passed_ss_Group == TRUE, ] ``` ## MaAsLin2 ```r library(Maaslin2) # Prepare data features <- as.data.frame(t(otu_table(ps))) metadata <- as.data.frame(sample_data(ps)) # Run MaAsLin2 maaslin_results <- Maaslin2( input_data = features, input_metadata = metadata, output = 'maaslin2_output', fixed_effects = 'Group', normalization = 'CLR', transform = 'NONE', analysis_method = 'LM' ) # Results in maaslin2_output/all_results.tsv sig_maaslin <- maaslin_results$results[maaslin_results$results$qval < 0.05, ] ``` ## DESeq2 (with caution) ```r 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 ```r 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

bio-microbiome-differential-abundance

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