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Who published Bio Experimental Design Power Analysis?

bg-szy (@bg-szy) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bio Experimental Design Power Analysis by bg-szy

Name: Bio Experimental Design Power Analysis
Author: bg-szy

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.

bg-szy Updated Sep 17, 2026

File contents

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

---name: bio-experimental-design-power-analysisdescription: 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.---# Power Analysis for Sequencing Experiments## Core ConceptPower = probability of detecting a true effect. Underpowered studies waste resources; overpowered studies are inefficient.## RNA-seq Power Analysis```rlibrary(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 sizernapower(depth = 20, n = 3, cv = 0.4, effect = 2, alpha = 0.05)# Calculate required samples for target powerrnapower(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)```rlibrary(ssizeRNA)# For differential accessibilitysize.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

Frequently asked questions