Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives
Creators
- 1. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, U.S.A.
- 2. The Interdisciplinary PhD program in Biostatistics, The Ohio State University, Columbus, Ohio, U.S.A.
- 3. Department of Statistics and Data Science, Yonsei University, Seoul, South Korea
- 4. Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, U.S.A.
- 5. Division of Statistics and Data Science, University of Cincinnati, Cincinnati, Ohio, U.S.A.
Description
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.
Files
Supplementary Material.zip
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