Bio experimental design power analysis
Skill bg-szy/TOP-SKILLS/skills/awesome-skills/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.From its SKILL.md
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SKILL.md
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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
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)
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) |
|---|---|
| 4-fold | 3 per group |
| 2-fold | 5-6 per group |
| 1.5-fold | 10-12 per group |
| 1.25-fold | 20+ per group |
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
What ships with it: 1 file
2.1 KB alongside SKILL.md
- usage-guide.md2.1 KB