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Get Started Free →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.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -9% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 4% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -57% | 0% |
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Power = probability of detecting a true effect. Underpowered studies waste resources; overpowered studies are inefficient.
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 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 |
rlibrary(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 |
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