Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives

TL;DR

This paper reviews and discusses statistical power analysis methods for bulk, single-cell, and spatial transcriptomics, providing practical guidance and highlighting the lack of tools for spatial methods.

Contribution

It offers a comprehensive review of power analysis tools for bulk and single-cell RNA-seq and explores factors influencing spatial transcriptomics power analysis, filling a gap in current methodologies.

Findings

Existing power analysis tools for bulk and single-cell RNA-seq are summarized.

Recommendations for experimental design using these tools are provided.

Factors influencing power in spatial transcriptomics are identified and discussed.

Abstract

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.