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

ABSTRACT:

Motivation

Differential expression analysis in single-cell transcriptomics unveils cell type-specific responses to various treatments or biological conditions. To ensure the robustness and reliability of the analysis, it is essential to have a solid experimental design with ample statistical power and sample size. However, existing methods for power and sample size calculation often assume a specific distribution for single-cell transcriptomics data, potentially deviating from the true data distribution. Moreover, they commonly overlook cell-cell correlations within individual samples, posing challenges in accurately representing biological phenomena. Additionally, due to the complexity of deriving an analytic formula, most methods employ time-consuming simulation-based strategies.

SUBMITTER: Hsu CY 

PROVIDER: S-EPMC11407695 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature