Bio Microbiome Differential Abundance

biotender-max/bio-microbiome-differential-abundance

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.

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the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

132

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill bio-microbiome-differential-abundance

What comes with it

5 377 bytes besides the instruction

examples/aldex2_analysis.R

usage-guide.md

The instruction itself

10 sections, as written by the author

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('<pkg>') 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.

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

|--------|---------|------------|-------|-------|

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

How to use it

Copy the folder

Take biotender-max/bio-microbiome-differential-abundance from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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