bio-experimental-design-power-analysis
Calculates statistical power and minimum sample sizes for RNA-seq, ATAC-seq, and other sequencing experiments. Use when planning experiments, determining how many replicates are needed, or assessing whether a study is adequately powered to detect expected effect sizes.
npx skills add majiayu000/claude-skill-registry --skill power-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Power Analysis for Sequencing Experiments ## Core Concept Power = probability of detecting a true effect. Underpowered studies waste resources; overpowered studies are inefficient. ## RNA-seq Power Analysis ```r library(RNASeqPower) # Typical parameters # - depth: sequencing depth per sample (reads/gene) # - cv: biological coefficient of variation (0.1-0.4 typical) # - effect: fold change to detect (1.5 = 50% change) # - alpha: significance level (0.05 standard) # Calculate power for given sample size rnapower(depth = 20, n = 3, cv = 0.4, effect = 2, alpha = 0.05) # Calculate required samples for target power rnapower(depth = 20, cv = 0.4, effect = 2, alpha = 0.05, power = 0.8) ``` ## 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 | ## ATAC-seq Power (ssizeRNA) ```r library(ssizeRNA) # For differential accessibility size.zhao(m = 10000, m1 = 500, fc = 2, fdr = 0.05, power = 0.8, mu = 10, disp = 0.1) ``` ## Quick Reference | Effect Size | Recommended n (CV=0.4) | |
- Core Concept
- RNA-seq Power Analysis
- CV Guidelines
- ATAC-seq Power (ssizeRNA)
- Quick Reference
- Related Skills
What does the bio-experimental-design-power-analysis skill do?
Calculates statistical power and minimum sample sizes for RNA-seq, ATAC-seq, and other sequencing experiments. Use when planning experiments, determining how many replicates are needed, or assessing whether a study is adequately powered to detect expected effect sizes.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill power-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.