A distribution-free and analytic method for power and sample size calculation in single-cell differential expression.

Area of Science:

• Single-cell transcriptomics
• Computational biology
• Statistical genetics

Background:

• Differential expression analysis in single-cell RNA sequencing (scRNA-seq) is crucial for understanding cell-type specific responses.
• Robust experimental design, including adequate statistical power and sample size, is essential for reliable scRNA-seq analysis.
• Current methods for power and sample size calculation often rely on distribution assumptions and neglect cell-cell correlations, limiting their accuracy.

Purpose of the Study:

• To develop a novel, accurate, and efficient method for calculating statistical power and sample size in single-cell differential expression analysis.
• To address limitations of existing methods by not assuming data distribution and incorporating cell-cell correlations.

Main Methods:

• Proposed scPS, an analytic-based method utilizing generalized estimating equations.
• scPS makes no assumptions regarding the distribution of single-cell transcriptomics data.
• The method accounts for inherent cell-cell correlations within individual samples.

Main Results:

• scPS provides accurate power and sample size calculations for single-cell differential expression studies.
• The method is distribution-agnostic, enhancing its applicability to diverse scRNA-seq datasets.
• scPS effectively incorporates cell-cell correlations, leading to more biologically relevant experimental designs.
• The approach is computationally efficient compared to simulation-based methods.

Conclusions:

• scPS offers a rapid, powerful, and reliable approach for designing single-cell differential expression experiments.
• This method improves the robustness of experimental design by considering crucial data characteristics.
• scPS facilitates more accurate and efficient research in single-cell transcriptomics.