RNASeqPower

RNASeqPower computes sample size and statistical power for RNA-Seq experiments to optimize sequencing depth and numbers of biological replicates for detecting differentially expressed genes.

Key Features:

• Sequencing Depth Estimation: Leverages data from 127 RNA-Seq experiments to estimate that approximately 91% ± 4% of annotated genes are typically sequenced at a frequency of 0.1 times per million bases mapped, irrespective of sample source.
• Biological Replicates Determination: Estimates the number of biological replicates required to detect significant changes in gene expression while accounting for observed biological variation.
• Power Estimation Model: Integrates empirical estimates of biological and technical variations from extensive datasets to model statistical power for identifying differentially expressed genes.
• Computational Resources: Provides R code and an Excel worksheet to perform custom power and sample-size calculations for specific experimental conditions.

Scientific Applications:

• Experimental Design Planning: Determine required sequencing depth and numbers of replicates during the planning of RNA-Seq studies.
• Study Power Assessment: Estimate sample sizes and sequencing depth to avoid underpowered experiments or excessive resource allocation.
• Reproducible Differential Expression Analysis: Support reliable and reproducible detection of differentially expressed genes across biological research fields.

Methodology:

Uses empirical estimates of biological and technical variations derived from 127 RNA-Seq experiments and models sequencing depth via gene detection frequency (≈91% ± 4% at 0.1 per million bases mapped) to estimate statistical power, with R code and an Excel worksheet provided for computations.