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BI
bio-experimental-design-power-analysis
Maintainer FreedomIntelligence · Last updated April 1, 2026
Statistical power analysis for detecting differential signals.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-experimental-design-power-analysis
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- rnapower(depth = 20, n = 3, cv = 0.4, effect = 2, alpha = 0.05).
- rnapower(depth = 20, cv = 0.4, effect = 2, alpha = 0.05, power = 0.8).
Source Doc
Excerpt From SKILL.md
Core Concept
Power = probability of detecting a true effect. Underpowered studies waste resources; overpowered studies are inefficient.
RNA-seq Power Analysis
library(RNASeqPower)
## CV Guidelines
| Experiment Type | Typical CV | Notes |
|-----------------|------------|-------|
| Cell lines | 0.1-0.2 | Low variability |
| Inbred mice | 0.2-0.3 | Moderate |
| Human samples | 0.3-0.5 | High variability |
| Primary cells | 0.3-0.4 | Donor-dependent |
Use cases
- Use bio-experimental-design-power-analysis for statistical testing and quantitative analysis.
- Apply bio-experimental-design-power-analysis to research datasets that require rigorous inference.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- experimental-design/sample-size - Detailed sample size calculations
- experimental-design/batch-design - Accounting for batch effects in design
- differential-expression/deseq2-basics - Running the actual DE analysis
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