bio-experimental-design-power-analysis — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited bio-experimental-design-power-analysis (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Reference examples tested with: RNASeqPower 1.42+, pwr 1.3+
Before using code patterns, verify installed versions match. If versions differ:
packageVersion("<pkg>") then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"How many replicates do I need for RNA-seq?" → Calculate statistical power or minimum sample size given sequencing depth, biological variability, and expected effect size.
RNASeqPower::rnapower(), pwr::pwr.t.test()Power = probability of detecting a true effect. Underpowered studies waste resources; overpowered studies are inefficient.
Goal: Determine whether a planned RNA-seq experiment has sufficient statistical power to detect biologically meaningful fold changes, or calculate the minimum sample size needed for a target power.
Approach: Provide sequencing depth, biological coefficient of variation, expected fold change, and significance level to rnapower, which uses a negative binomial model to compute power or required sample size.
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)| 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 |
library(ssizeRNA)
# For differential accessibility
size.zhao(m = 10000, m1 = 500, fc = 2, fdr = 0.05, power = 0.8,
mu = 10, disp = 0.1)| 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 |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.