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bio-microbiome-differential-abundance

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

OpenClawNanoClawAnalysisReproductionbio-microbiome-differential-abundance🧬 bioinformatics (gptomics bio-* suite)bioinformatics — metagenomics & microbiomedifferential

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-microbiome-differential-abundance

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

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.
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.
groups <- sample_data(ps)$Group.

Source Doc

Excerpt From SKILL.md

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)

## Run ALDEx2 (CLR transformation + Welch's t-test)

aldex_results <- aldex(otu, groups, mc.samples = 128, test = 'welch',
                       effect = TRUE, include.sample.summary = FALSE)