数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-microbiome-differential-abundance

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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.

OpenClawNanoClaw分析处理复现实验bio-microbiome-differential-abundance🧬 bioinformatics (gptomics bio-* suite)bioinformatics — metagenomics & microbiomedifferential

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

R：ALDEx2：：aldex() ，用于 CLR-transformed Welch's t-test。
R：ANCOMBC：：ancombc2() ，用于 bias-corrected log-linear models。
R：Maaslin2：：Maaslin2() ，用于 multivariable association。
Find which taxa differ between my groups" → Identify differentially abundant taxa between experimental conditions ，使用 compositionally-aware methods that account ，用于 relative nature of microbiome data. R：ALDEx2：：aldex() ，用于 CLR-transformed Welch's t-test R：ANCOMBC：：ancombc2() ，用于 bias-corrected log-linear models R：Maaslin2：：Maaslin2() ，用于 multivariable association。
groups <- sample_data(ps)$Group。

原始文档

SKILL.md 摘录

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)