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

Bio Experimental Design Power Analysis by tomevault-io

Name: Bio Experimental Design Power Analysis
Author: tomevault-io

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. Use when this capability is needed.

tomevault-io Updated Sep 17, 2026

File contents

Version Compatibility

Reference examples tested with: RNASeqPower 1.42+, pwr 1.3+

Before using code patterns, verify installed versions match. If versions differ:

R: packageVersion("<pkg>") then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Power Analysis for Sequencing Experiments

"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.

R: RNASeqPower::rnapower(), pwr::pwr.t.test()

Core Concept

Power = probability of detecting a true effect. Underpowered studies waste resources; overpowered studies are inefficient.

RNA-seq Power Analysis

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)

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

Converted and distributed by TomeVault — claim your Tome and manage your conversions.

---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. Use when this capability is needed.---## Version CompatibilityReference examples tested with: RNASeqPower 1.42+, pwr 1.3+Before using code patterns, verify installed versions match. If versions differ:- R: `packageVersion("<pkg>")` then `?function_name` to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installedpackage and adapt the example to match the actual API rather than retrying.# Power Analysis for Sequencing Experiments**"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.- R: `RNASeqPower::rnapower()`, `pwr::pwr.t.test()`## Core ConceptPower = probability of detecting a true effect. Underpowered studies waste resources; overpowered studies are inefficient.## RNA-seq Power Analysis**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.```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---> Converted and distributed by [TomeVault](https://tomevault.io/claim/gptomics) — claim your Tome and manage your conversions.

Frequently asked questions