# Daa Guide > Guide for differential abundance analysis method selection, threshold choices, and result interpretation. Use when user asks about LinDA, ZINB, Hurdle, NB methods, q-value thresholds, effect sizes, sensitivity, FDR, or how to interpret DAA results. Also use when user runs an analysis and gets unexpected results (like 0 significant features). Use when this capability is needed. - Skill: `tomevault-io/daa-guide` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/daa-guide` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/daa-guide/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/daa-guide --- # Differential Abundance Analysis Guide This skill provides evidence-based guidance for method selection, threshold choices, and result interpretation based on comprehensive benchmarking. ## Quick Method Selection | Sparsity | Method | Threshold | Sensitivity | FDR | Use Case | |----------|--------|-----------|-------------|-----|----------| | >70% | **Hurdle** | q < 0.05 | 83% | 25% | Sparse data with structural zeros | | 50-70% | **ZINB** | q < 0.05 | 83% | 29% | Moderate sparsity, excess zeros | | <30% | **LinDA** | q < 0.10 | 39% | 12.5% | Low sparsity, high-confidence findings | | Longitudinal | **LMM** | q < 0.05 | - | - | Repeated measures | ## CRITICAL: LinDA Threshold **LinDA requires q < 0.10, NOT q < 0.05** LinDA uses CLR (Centered Log-Ratio) transformation which attenuates effect sizes by ~75%: - True 4.0 log2FC (16x fold change) becomes ~1.0 observed - At q < 0.05: **0% sensitivity** (nothing detected) - At q < 0.10: **39% sensitivity** with excellent **12.5% FDR** This is by design, not a bug. CLR centers by geometric mean to handle compositional data. ### When User Gets 0 Significant Results with LinDA 1. First, check if they used q < 0.05 → suggest q < 0.10 2. If still nothing at q < 0.10, the effects may be too small 3. LinDA needs >8x fold changes to detect anything reliably 4. Suggest trying Hurdle if FDR control is less critical ## Effect Size Requirements | Method | Minimum Detectable Effect | Notes | |--------|---------------------------|-------| | LinDA | >8x fold change (3 log2FC) | Due to CLR attenuation | | ZINB | >2x fold change (1 log2FC) | Good sensitivity | | Hurdle | >2x fold change (1 log2FC) | Best for sparse data | | NB | >16x fold change (4 log2FC) | Conservative | ## Method Details ### LinDA (CLR + Linear Model) - **Best for**: High-confidence findings, FDR-controlled discovery - **Threshold**: q < 0.10 (NOT 0.05) - **Pros**: Excellent FDR control (12.5%), handles compositionality - **Cons**: Low sensitivity, only detects very large effects - **Effect sizes**: NOT directly interpretable as fold changes (attenuated by ~75%) ### ZINB (Zero-Inflated Negative Binomial) - **Best for**: Discovery, count data with excess zeros - **Threshold**: q < 0.05 - **Pros**: High sensitivity (83%), models zero-inflation - **Cons**: Moderate FDR (29%), assumes NB distribution ### Hurdle Model - **Best for**: Sparse data with structural zeros, two-part analysis - **Threshold**: q < 0.05 - **Pros**: High sensitivity (83%), good FDR (25%), separates presence/abundance - **Cons**: More complex interpretation (binary + count components) ### NB (Negative Binomial) - **Best for**: Low-sparsity data, simple overdispersion - **Threshold**: q < 0.05 - **Pros**: Simple, well-understood - **Cons**: Low sensitivity (6%), doesn't handle excess zeros ### Permutation Test - **Best for**: Unknown distributions, non-parametric inference - **Threshold**: p < 0.05 - **Pros**: Distribution-free, robust - **Cons**: Computationally intensive, may be conservative ### LMM (Linear Mixed Model) - **Best for**: Longitudinal data, repeated measures - **Threshold**: q < 0.05 - **Pros**: Handles within-subject correlation, auto-detected by `recommend` - **Cons**: Requires CLR transformation, same attenuation as LinDA ## Interpreting Results ### LinDA Results - Effect sizes are CLR-transformed, NOT fold changes - Observed estimate of 1.0 may represent true 4x fold change - Focus on significance (q-value), not effect magnitude - Use q < 0.10 threshold ### ZINB/Hurdle Results - Effect sizes are on log scale (interpretable as log fold change) - `fold_change = exp(estimate)` - Higher sensitivity means more discoveries but also more false positives - Consider biological plausibility of findings ### Sample Size Considerations - n=10 per group: Only huge effects (>8x) reliably detectable - n=20 per group: Large effects (>4x) detectable - n=50 per group: Moderate effects (>2x) become detectable - Power analysis recommended before study ## Decision Tree ``` Is your data longitudinal/repeated measures? ├── YES → recommend auto-detects and runs LMM └── NO → Continue... What is your sparsity level? ├── >70% zeros → Hurdle (q < 0.05) ├── 50-70% zeros → ZINB (q < 0.05) ├── 30-50% zeros → ZINB or LinDA └── <30% zeros → LinDA (q < 0.10) Unsure about distributional assumptions? └── Permutation test (p < 0.05) ``` ## CLI Quick Reference The unified workflow handles method selection automatically: ```bash # Let the tool choose the best method for your data daa recommend -c counts.tsv -m metadata.tsv -g group -t treatment --run -o results.tsv # Just see the recommendation (no execution) daa recommend -c counts.tsv -m metadata.tsv -g group -t treatment # Generate editable YAML for custom configurations daa recommend -c counts.tsv -m metadata.tsv -g group -t treatment --yaml -o pipeline.yaml # Non-parametric alternative daa permutation -c counts.tsv -m metadata.tsv -f "~ group" -t grouptreatment -o results.tsv ``` ### Longitudinal/Repeated Measures The `recommend` command auto-detects these designs: ```bash # Metadata with subject + timepoint columns → LMM automatically daa recommend -c counts.tsv -m metadata.tsv -g group -t treatment --run -o results.tsv # For custom formulas (interactions, random slopes): daa recommend -c counts.tsv -m metadata.tsv -g group -t treatment --yaml -o pipeline.yaml # Edit pipeline.yaml, then: daa run -c counts.tsv -m metadata.tsv --config pipeline.yaml -o results.tsv ``` ## Troubleshooting: 0 Significant Features 1. **Check threshold**: Did you use LinDA with q < 0.05? Try q < 0.10 2. **Check sample size**: n < 20/group has very limited power 3. **Check sparsity**: >70% sparsity with LinDA? Try Hurdle instead 4. **Run validation**: `daa validate` to see if method works on your data structure 5. **Check raw p-values**: Are any features close (p < 0.1)? ## For More Details - Method comparison benchmarks: see [method-comparison.md](method-comparison.md) - Q-value threshold analysis: see [thresholds.md](thresholds.md) - Result interpretation guide: see [interpretation.md](interpretation.md) --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/shandley) — claim your Tome and manage your conversions.