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daa-diagnose

Diagnose microbiome count data sparsity, library size, and study design to recommend differential abundance analysis methods.

1|1|Updated Jan 30, 2026
Search Tags:#data-analysis#diagnose#differential abundance#microbiome#method-recommendation
One-click install
npx skills add https://github.com/shandley/composable-daa --skill daa-diagnose
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: daa-diagnose
Source: https://github.com/shandley/composable-daa/tree/main/.claude/skills/daa-diagnose
Command: npx skills add https://github.com/shandley/composable-daa --skill daa-diagnose

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Diagnose microbiome/virome count data and identify the most suitable differential abundance analysis method.

Core Features & Use Cases

  • Diagnoses data characteristics (sparsity, library size, and study design) to guide method choice.
  • Recommends appropriate differential abundance analysis methods (LinDA, ZINB, Hurdle, LMM) based on data.
  • Use case: A researcher provides counts and metadata and receives a tailored method recommendation.

Quick Start

Provide counts and metadata files to receive an automated diagnosis and recommended DAA method.

Frequently Asked Questions about daa-diagnose

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I choose the right differential abundance analysis method for microbiome data?▼

Differential abundance analysis for microbiome data requires evaluating sparsity, library size, and study design. Diagnosing these characteristics recommends tailored methods like LinDA, ZINB, Hurdle, or LMM for accurate cross-sectional or longitudinal results.

What differential abundance method should I use for longitudinal microbiome studies?▼

Longitudinal microbiome studies often utilize Linear Mixed Models (LMM). Diagnosing your specific data characteristics confirms if LMM suits your study design compared to cross-sectional alternatives like LinDA or ZINB.

How do I diagnose microbiome count data before selecting a DAA method?▼

Diagnosing microbiome count data involves empirically profiling sparsity, library size, and study design. Providing counts and metadata files outputs a tailored differential abundance method recommendation for your dataset.

Does data sparsity affect which differential abundance method I should use?▼

Data sparsity directly impacts differential abundance method selection. High sparsity in microbiome count data often guides the recommendation toward zero-inflated methods like ZINB or Hurdle to handle excessive zeros accurately.

What is the best way to analyze differential abundance in highly sparse virome data?▼

Analyzing differential abundance in highly sparse virome data requires diagnosing sparsity and library size. This profiling typically recommends zero-inflated methods like ZINB or Hurdle to handle excessive zeros effectively.

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