RNA-seq Power Calculator
Estimate sample size and power for detecting differential gene expression.
About RNA-seq Power Calculator
Effect size is the log fold change divided by the coefficient of variation, so biological variability matters as much as the fold change itself. Testing fifteen thousand genes raises the effective significance threshold by four orders of magnitude. Biological replicates rather than sequencing depth drive power, which is why three replicates sequenced deeply is usually worse than six sequenced shallowly.
Formula
RNA-seq Power
Power depends on effect size × √(n ÷ 2) against the multiplicity-adjusted threshold
- biological CV
- Variation between biological replicates, the dominant noise source
- multiplicity adjustment
- Testing thousands of genes raises the significance bar sharply
Worked example
With the default values below, RNA-seq Power Calculator returns Statistical Power of 6.70%. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Minimum fold change to detectminimum 1.01 x
- 2 x
- Biological coefficient of variationminimum 0.01
- 0.4
- Samples per groupminimum 2
- 6
- False discovery rateaccepts 0.01–50 %
- 5 %
- Genes testedminimum 1
- 15,000
Results
- Statistical Power
- 6.70%
- Effect Size in Log Space
- 1.7329
- Samples per Group for 80 Per Cent Power
- 20
- Multiplicity-Adjusted Significance Level
- 0.00033333
- Verdict
- Severely underpowered — increase replicates before sequencing
Step by step
- 1Values used
- 2RNA-seq Power
- 3Statistical Power= 6.70
- 4Effect Size in Log Space= 1.7329
- 5Samples per Group for 80 Per Cent Power= 20
- 6Multiplicity-Adjusted Significance Level= 0.00033333
- 7Verdict= Severely underpowered — increase replicates before sequencing
Assumptions and limits
- The result is derived only from the 5 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 8 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
How is RNA-seq Power calculated?
Power depends on effect size × √(n ÷ 2) against the multiplicity-adjusted threshold Effect size is the log fold change divided by the coefficient of variation, so biological variability matters as much as the fold change itself. Testing fifteen thousand genes raises the effective significance threshold by four orders of magnitude.
Why does RNA-seq Power matter?
Biological replicates rather than sequencing depth drive power, which is why three replicates sequenced deeply is usually worse than six sequenced shallowly.
What values do I need to enter?
This calculator takes 5 inputs: Minimum fold change to detect, Biological coefficient of variation, Samples per group, False discovery rate, Genes tested. The pre-filled defaults are a realistic starting point — replace them with figures from your own environment for a result you can act on.