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majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bio Experimental Design Power Analysis by majiayu000

Name: Bio Experimental Design Power Analysis
Rating: 4.2 (5 reviews)
Author: majiayu000

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.

majiayu000 77d333f 2 files · 2.6 KB Updated Sep 9, 2026 567 repo stars

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 Concept Power = probability of detecting a true effect. Underpowered studies waste resources; overpowered studies are inefficient. ## RNA-seq Power Analysis ```r 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) ```r 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

Frequently asked questions