Estimate sample size and power for detecting differential gene expression.
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.
RNA-seq Power
Power depends on effect size × √(n ÷ 2) against the multiplicity-adjusted threshold
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.
Biological replicates rather than sequencing depth drive power, which is why three replicates sequenced deeply is usually worse than six sequenced shallowly.
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.