scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies.

Area of Science:

• Genomics
• Computational Biology
• Statistical Genetics

Background:

• Single-cell RNA sequencing (scRNA-seq) enables cell-type-resolved transcriptomic analyses.
• Existing power analysis methods are insufficient for scRNA-seq and inter-individual comparisons.
• Designing efficient scRNA-seq experiments requires careful consideration of multiple parameters.

Purpose of the Study:

• To develop a statistical framework, scPower, for power analysis in multi-sample scRNA-seq studies.
• To provide guidance on optimizing experimental design for detecting differentially expressed genes within cell types.
• To enable researchers to compare various experimental designs and optimize for cost-effectiveness.

Main Methods:

• Developed a statistical model relating sample size, cells per individual, and sequencing depth to statistical power.
• Evaluated optimal parameter combinations across different scRNA-seq platforms.
• Implemented the model as an R package and a web tool.

Main Results:

• Demonstrated that shallow sequencing of more cells generally yields higher power than deep sequencing of fewer cells.
• Identified optimal parameter combinations for various experimental scenarios.
• Provided general recommendations for scRNA-seq experimental design.

Conclusions:

• scPower provides a flexible and customizable tool for experimental design and power analysis in scRNA-seq.
• The framework facilitates informed decisions for optimizing experiments under budget constraints.
• This work addresses a critical need for efficient power analysis in single-cell transcriptomics.