bio-experimental-design-power-analysis

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name: bio-experimental-design-power-analysis description: 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. tool_type: r primary_tool: RNASeqPower measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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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