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Review

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

by 1,2,†, 1,2,3,†, 1,4,5,†, 6, 1,2,3, 7 and 1,2,3,*
1
Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
2
Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
3
The Interdisciplinary PhD program in Biostatistics, The Ohio State University, Columbus, OH 43210, USA
4
Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea
5
Department of Applied Statistics, Yonsei University, Seoul 03722, Republic of Korea
6
Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
7
Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH 45221, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Biomolecules 2023, 13(2), 221; https://doi.org/10.3390/biom13020221
Received: 26 December 2022 / Revised: 20 January 2023 / Accepted: 21 January 2023 / Published: 24 January 2023
(This article belongs to the Special Issue Single-Cell and Spatial Multi-Omics Technologies in Human Health)

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.
Keywords: transcriptomics; gene expression analysis; power analysis; RNA-seq; scRNA-seq; high-throughput spatial transcriptomics transcriptomics; gene expression analysis; power analysis; RNA-seq; scRNA-seq; high-throughput spatial transcriptomics

Share and Cite

MDPI and ACS Style

Jeon, H.; Xie, J.; Jeon, Y.; Jung, K.J.; Gupta, A.; Chang, W.; Chung, D. Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives. Biomolecules 2023, 13, 221. https://doi.org/10.3390/biom13020221

AMA Style

Jeon H, Xie J, Jeon Y, Jung KJ, Gupta A, Chang W, Chung D. Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives. Biomolecules. 2023; 13(2):221. https://doi.org/10.3390/biom13020221

Chicago/Turabian Style

Jeon, Hyeongseon, Juan Xie, Yeseul Jeon, Kyeong Joo Jung, Arkobrato Gupta, Won Chang, and Dongjun Chung. 2023. "Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives" Biomolecules 13, no. 2: 221. https://doi.org/10.3390/biom13020221

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