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

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## Abstract

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## 1. Introduction

## 2. Power Analysis for Bulk RNA-Seq Experiments

#### 2.1. Bulk RNA-Seq Experiment

#### 2.2. Bulk RNA-Seq Power Analysis Tools

#### 2.3. Bulk RNA-Seq Power Analysis Tool Recommendation

## 3. Power Analysis for Single-Cell RNA-Seq (scRNA-Seq) Experiments

#### 3.1. Power Analysis for Cell Subpopulation Detection

#### 3.1.1. Ascertaining Cell Subpopulation Proportions in a Single Tissue

#### 3.1.2. Ascertaining Differential Cell Subpopulation Proportions between Distinct Experimental Conditions

#### 3.2. Power Analysis for DEG Detection

#### 3.2.1. DEGs across Different Conditions for a Cell Type

#### 3.2.2. DEGs across Different Cell Types

#### 3.3. scRNA-Seq Power Analysis Tool Recommendations

## 4. Power analysis for Spatial Transcriptomic Experiments

#### 4.1. Introduction of High-Throughput Spatial Transcriptomics (HST) Technology

#### 4.2. Literature Reviews of Power Analysis for HST Data

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Morozova, O.; Hirst, M.; Marra, M.A. Applications of new sequencing technologies for transcriptome analysis. Annu. Rev. Genom. Hum. Genet.
**2009**, 10, 135–151. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Liang, K.-H. ScienceDirect. In Bioinformatics for Biomedical Science and Clinical Applications, 1st ed.; Woodhead Pub: Philadelphia, NY, USA, 2013. [Google Scholar]
- Haque, A.; Engel, J.; Teichmann, S.A.; Lonnberg, T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med.
**2017**, 9, 75. [Google Scholar] [CrossRef] [PubMed] - Hong, M.; Tao, S.; Zhang, L.; Diao, L.T.; Huang, X.; Huang, S.; Xie, S.J.; Xiao, Z.D.; Zhang, H. RNA sequencing: New technologies and applications in cancer research. J. Hematol. Oncol.
**2020**, 13, 166. [Google Scholar] [CrossRef] [PubMed] - Rao, M.S.; Van Vleet, T.R.; Ciurlionis, R.; Buck, W.R.; Mittelstadt, S.W.; Blomme, E.A.G.; Liguori, M.J. Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver from Short-Term Rat Toxicity Studies. Front. Genet.
**2018**, 9, 636. [Google Scholar] [CrossRef] [Green Version] - Burgess, D.J. Spatial transcriptomics coming of age. Nat. Rev. Genet.
**2019**, 20, 317. [Google Scholar] [CrossRef] - Marioni, J.C.; Mason, C.E.; Mane, S.M.; Stephens, M.; Gilad, Y. RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Res.
**2008**, 18, 1509–1517. [Google Scholar] [CrossRef] [Green Version] - Bacher, R.; Kendziorski, C. Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol.
**2016**, 17, 63. [Google Scholar] [CrossRef] [Green Version] - Schurch, N.J.; Schofield, P.; Gierliński, M.; Cole, C.; Sherstnev, A.; Singh, V.; Wrobel, N.; Gharbi, K.; Simpson, G.G.; Owen-Hughes, T.; et al. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA
**2016**, 22, 839–851. [Google Scholar] [CrossRef] [Green Version] - Liu, Y.; Zhou, J.; White, K.P. RNA-seq differential expression studies: More sequence or more replication? Bioinformatics
**2014**, 30, 301–304. [Google Scholar] [CrossRef] [Green Version] - Pollen, A.A.; Nowakowski, T.J.; Shuga, J.; Wang, X.; Leyrat, A.A.; Lui, J.H.; Li, N.; Szpankowski, L.; Fowler, B.; Chen, P.; et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol.
**2014**, 32, 1053–1058. [Google Scholar] [CrossRef] [Green Version] - Cohen, J. The statistical power of abnormal-social psychological research: A review. J. Abnorm. Soc. Psychol.
**1962**, 65, 145. [Google Scholar] [CrossRef] - Cohen, J. Statistical power analysis. Curr. Dir. Psychol. Sci.
**1992**, 1, 98–101. [Google Scholar] [CrossRef] - Thomas, L. Retrospective power analysis. Conserv. Biol.
**1997**, 11, 276–280. [Google Scholar] [CrossRef] [Green Version] - Chuan, C.L.; Penyelidikan, J. Sample size estimation using Krejcie and Morgan and Cohen statistical power analysis: A comparison. J. Penyelid. IPBL
**2006**, 7, 78–86. [Google Scholar] - Sanger, F.; Nicklen, S.; Coulson, A.R. DNA sequencing with chain-terminating inhibitors. Proc. Natl. Acad. Sci. USA
**1977**, 74, 5463–5467. [Google Scholar] [CrossRef] [Green Version] - Stark, R.; Grzelak, M.; Hadfield, J. RNA sequencing: The teenage years. Nat. Rev. Genet.
**2019**, 20, 631–656. [Google Scholar] [CrossRef] - Van den Berge, K.; Hembach, K.M.; Soneson, C.; Tiberi, S.; Clement, L.; Love, M.I.; Patro, R.; Robinson, M.D. RNA sequencing data: Hitchhiker’s guide to expression analysis. Annu. Rev. Biomed. Data Sci.
**2019**, 2, 139–173. [Google Scholar] [CrossRef] [Green Version] - Hart, S.N.; Therneau, T.M.; Zhang, Y.; Poland, G.A.; Kocher, J.P. Calculating sample size estimates for RNA sequencing data. J. Comput. Biol.
**2013**, 20, 970–978. [Google Scholar] [CrossRef] [Green Version] - Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics
**2010**, 26, 139–140. [Google Scholar] [CrossRef] [Green Version] - Anders, S.; Huber, W. Differential expression analysis for sequence count data. Genome Biol.
**2010**, 11, R106. [Google Scholar] [CrossRef] [Green Version] - Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol.
**2014**, 15, 550. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Hardcastle, T.J.; Kelly, K.A. baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinform.
**2010**, 11, 422. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Van den Berge, K.; Perraudeau, F.; Soneson, C.; Love, M.I.; Risso, D.; Vert, J.P.; Robinson, M.D.; Dudoit, S.; Clement, L. Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biol.
**2018**, 19, 24. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Kvam, V.M.; Liu, P.; Si, Y. A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am. J. Bot.
**2012**, 99, 248–256. [Google Scholar] [CrossRef] [Green Version] - Li, D.; Zand, M.S.; Dye, T.D.; Goniewicz, M.L.; Rahman, I.; Xie, Z. An evaluation of RNA-seq differential analysis methods. PLoS ONE
**2022**, 17, e0264246. [Google Scholar] [CrossRef] - Lund, S.P.; Nettleton, D.; McCarthy, D.J.; Smyth, G.K. Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Stat. Appl. Genet. Mol. Biol.
**2012**, 11. [Google Scholar] [CrossRef] [Green Version] - Law, C.W.; Chen, Y.; Shi, W.; Smyth, G.K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol.
**2014**, 15, R29. [Google Scholar] [CrossRef] [Green Version] - Poplawski, A.; Binder, H. Feasibility of sample size calculation for RNA-seq studies. Brief. Bioinform.
**2018**, 19, 713–720. [Google Scholar] [CrossRef] - Li, C.I.; Su, P.F.; Shyr, Y. Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data. BMC Bioinform.
**2013**, 14, 357. [Google Scholar] [CrossRef] [Green Version] - Bi, R.; Liu, P. Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments. BMC Bioinform.
**2016**, 17, 146. [Google Scholar] [CrossRef] [Green Version] - Wu, H.; Wang, C.; Wu, Z. PROPER: Comprehensive power evaluation for differential expression using RNA-seq. Bioinformatics
**2015**, 31, 233–241. [Google Scholar] [CrossRef] [Green Version] - Busby, M.A.; Stewart, C.; Miller, C.A.; Grzeda, K.R.; Marth, G.T. Scotty: A web tool for designing RNA-Seq experiments to measure differential gene expression. Bioinformatics
**2013**, 29, 656–657. [Google Scholar] [CrossRef] [Green Version] - Zhao, S.; Li, C.I.; Guo, Y.; Sheng, Q.; Shyr, Y. RnaSeqSampleSize: Real data based sample size estimation for RNA sequencing. BMC Bioinform.
**2018**, 19, 191. [Google Scholar] [CrossRef] - Ching, T.; Huang, S.; Garmire, L.X. Power analysis and sample size estimation for RNA-Seq differential expression. RNA
**2014**, 20, 1684–1696. [Google Scholar] [CrossRef] [Green Version] - Pierson, E.; Yau, C. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol.
**2015**, 16, 241. [Google Scholar] [CrossRef] [Green Version] - Davis, A.; Gao, R.; Navin, N.E. SCOPIT: Sample size calculations for single-cell sequencing experiments. BMC Bioinform.
**2019**, 20, 566. [Google Scholar] [CrossRef] [Green Version] - Liang, S.; Willis, J.; Dou, J.; Mohanty, V.; Huang, Y.; Vilar, E.; Chen, K. Sensei: How many samples to tell a change in cell type abundance? BMC Bioinform.
**2022**, 23, 2. [Google Scholar] [CrossRef] - Millard, N.; Korsunsky, I.; Weinand, K.; Fonseka, C.Y.; Nathan, A.; Kang, J.B.; Raychaudhuri, S. Maximizing statistical power to detect differentially abundant cell states with scPOST. Cell Rep. Methods
**2021**, 1, 100120. [Google Scholar] [CrossRef] - Schmid, K.T.; Höllbacher, B.; Cruceanu, C.; Böttcher, A.; Lickert, H.; Binder, E.B.; Theis, F.J.; Heinig, M. scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies. Nat. Commun.
**2021**, 12, 6625. [Google Scholar] [CrossRef] - Zimmerman, K.D.; Langefeld, C.D. Hierarchicell: An R-package for estimating power for tests of differential expression with single-cell data. BMC Genom.
**2021**, 22, 319. [Google Scholar] [CrossRef] - Vieth, B.; Ziegenhain, C.; Parekh, S.; Enard, W.; Hellmann, I. powsimR: Power analysis for bulk and single cell RNA-seq experiments. Bioinformatics
**2017**, 33, 3486–3488. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Su, K.; Wu, Z.; Wu, H. Simulation, power evaluation and sample size recommendation for single-cell RNA-seq. Bioinformatics
**2020**, 36, 4860–4868. [Google Scholar] [CrossRef] [PubMed] - Li, W.V.; Li, J.J. A statistical simulator scDesign for rational scRNA-seq experimental design. Bioinformatics
**2019**, 35, i41–i50. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Qi, R.; Ma, A.; Ma, Q.; Zou, Q. Clustering and classification methods for single-cell RNA-sequencing data. Brief. Bioinform.
**2020**, 21, 1196–1208. [Google Scholar] [CrossRef] - Maynard, K.R.; Collado-Torres, L.; Weber, L.M.; Uytingco, C.; Barry, B.K.; Williams, S.R.; Catallini, J.L., 2nd; Tran, M.N.; Besich, Z.; Tippani, M.; et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci.
**2021**, 24, 425–436. [Google Scholar] [CrossRef] - Chen, K.H.; Boettiger, A.N.; Moffitt, J.R.; Wang, S.; Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science
**2015**, 348, aaa6090. [Google Scholar] [CrossRef] [Green Version] - Ståhl, P.L.; Salmén, F.; Vickovic, S.; Lundmark, A.; Navarro, J.F.; Magnusson, J.; Giacomello, S.; Asp, M.; Westholm, J.O.; Huss, M.; et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science
**2016**, 353, 78–82. [Google Scholar] [CrossRef] [Green Version] - Svensson, V.; Teichmann, S.A.; Stegle, O. SpatialDE: Identification of spatially variable genes. Nat. Methods
**2018**, 15, 343–346. [Google Scholar] [CrossRef] - Sun, S.; Zhu, J.; Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods
**2020**, 17, 193–200. [Google Scholar] [CrossRef] - Edsgärd, D.; Johnsson, P.; Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods
**2018**, 15, 339–342. [Google Scholar] [CrossRef] - Li, Q.; Zhang, M.; Xie, Y.; Xiao, G. Bayesian Modeling of Spatial Molecular Profiling Data via Gaussian Process. Bioinformatics
**2021**, 37, 4129–4136. [Google Scholar] [CrossRef] - Jiang, X.; Xiao, G.; Li, Q. A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data. Stat. Med.
**2022**, 41, 4647–4665. [Google Scholar] [CrossRef] - Dries, R.; Zhu, Q.; Dong, R.; Eng, C.L.; Li, H.; Liu, K.; Fu, Y.; Zhao, T.; Sarkar, A.; Bao, F.; et al. Giotto: A toolbox for integrative analysis and visualization of spatial expression data. Genome Biol.
**2021**, 22, 78. [Google Scholar] [CrossRef] - Shi, J.; Luo, Z. Nonlinear dimensionality reduction of gene expression data for visualization and clustering analysis of cancer tissue samples. Comput. Biol. Med.
**2010**, 40, 723–732. [Google Scholar] [CrossRef] - Ben-Dor, A.; Bruhn, L.; Friedman, N.; Nachman, I.; Schummer, M.; Yakhini, Z. Tissue classification with gene expression profiles. J. Comput. Biol.
**2000**, 7, 559–583. [Google Scholar] [CrossRef] - Zhao, E.; Stone, M.R.; Ren, X.; Guenthoer, J.; Smythe, K.S.; Pulliam, T.; Williams, S.R.; Uytingco, C.R.; Taylor, S.E.B.; Nghiem, P.; et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol.
**2021**, 39, 1375–1384. [Google Scholar] [CrossRef] - Allen, C.; Chang, Y.; Neelon, B.; Chang, W.; Kim, H.J.; Li, Z.; Ma, Q.; Chung, D. A Bayesian multivariate mixture model for high throughput spatial transcriptomics. Biometrics
**2022**. [Google Scholar] [CrossRef] - Browaeys, R.; Saelens, W.; Saeys, Y. NicheNet: Modeling intercellular communication by linking ligands to target genes. Nat. Methods
**2020**, 17, 159–162. [Google Scholar] [CrossRef] - Chen, Z.; Yang, X.; Bi, G.; Liang, J.; Hu, Z.; Zhao, M.; Li, M.; Lu, T.; Zheng, Y.; Sui, Q.; et al. Ligand-receptor interaction atlas within and between tumor cells and T cells in lung adenocarcinoma. Int. J. Biol. Sci.
**2020**, 16, 2205–2219. [Google Scholar] [CrossRef] - Jin, S.; Guerrero-Juarez, C.F.; Zhang, L.; Chang, I.; Ramos, R.; Kuan, C.H.; Myung, P.; Plikus, M.V.; Nie, Q. Inference and analysis of cell-cell communication using CellChat. Nat. Commun.
**2021**, 12, 1088. [Google Scholar] [CrossRef] - Williams, C.G.; Lee, H.J.; Asatsuma, T.; Vento-Tormo, R.; Haque, A. An introduction to spatial transcriptomics for biomedical research. Genome Med.
**2022**, 14, 68. [Google Scholar] [CrossRef] [PubMed] - Lohoff, T.; Ghazanfar, S.; Missarova, A.; Koulena, N.; Pierson, N.; Griffiths, J.A.; Bardot, E.S.; Eng, C.L.; Tyser, R.C.V.; Argelaguet, R.; et al. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nat. Biotechnol.
**2022**, 40, 74–85. [Google Scholar] [CrossRef] [PubMed] - Bost, P.; Schulz, D.; Engler, S.; Wasserfall, C.; Bodenmiller, B. Optimizing multiplexed imaging experimental design through tissue spatial segregation estimation. bioRxiv
**2021**. [Google Scholar] [CrossRef] - Baker, E.A.G.; Schapiro, D.; Dumitrascu, B.; Vickovic, S.; Regev, A. Power analysis for spatial omics. bioRxiv
**2022**. [Google Scholar] - Li, Z.; Zhou, X. BASS: Multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies. Genome Biol.
**2022**, 23, 168. [Google Scholar] [CrossRef] - Allen, C.; Chang, Y.; Ma, Q.; Chung, D. MAPLE: A Hybrid Framework for Multi-Sample Spatial Transcriptomics Data. bioRxiv
**2022**, 2022, 482296. [Google Scholar] [CrossRef] - Hu, J.; Li, X.; Coleman, K.; Schroeder, A.; Ma, N.; Irwin, D.J.; Lee, E.B.; Shinohara, R.T.; Li, M. SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods
**2021**, 18, 1342–1351. [Google Scholar] [CrossRef] - Tu, J.J.; Li, H.S.; Yan, H.; Zhang, X.F. EnDecon: Cell type deconvolution of spatially resolved transcriptomics data via ensemble learning. Bioinformatics
**2023**, 39, btac825. [Google Scholar] [CrossRef] - Ma, Y.; Zhou, X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat. Biotechnol.
**2022**, 40, 1349–1359. [Google Scholar] [CrossRef] - Cable, D.M.; Murray, E.; Zou, L.S.; Goeva, A.; Macosko, E.Z.; Chen, F.; Irizarry, R.A. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol.
**2022**, 40, 517–526. [Google Scholar] [CrossRef]

**Figure 1.**Comparison of bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics technologies in terms of the profiling resolution (level), data structure, and target discoveries.

**Figure 2.**The figure depicts three representative research questions for the analysis of HST data. SVG denotes the identification of a gene with a spatial pattern of gene expression. Tissue architecture refers to the identification of a tissue’s structure through the clustering of similar gene expression patterns. Cell-cell communication, on the other hand, detects the interaction between cells using their spatial information and gene expression data.

**Figure 3.**Depending on the type of HST data, it can be considered as either marked point process data or areal data. First, imaging-based HST data can be regarded as marked point process data. For example, cell locations are analogous to the spatial coordinates of birds’ habitats in the US. Its spatial information is modeled through the distance among habitats. Sequencing-based HST data, on the other hand, can be regarded as areal data on a regular grid. Here the spot, which is a group of cells, can be compared to the states’ aggregated bird habitats. Its spatial information is modeled through the adjacency or neighborhood structure.

**Figure 4.**Key experimental factors in designing HST experiments include: (1) the choice of tissue area, (2) the number and sizes of fields of view (FoVs), and (3) the number of cells and spots. These experimental factors can affect the statistical power needed to achieve the research goals, e.g., those mentioned in Figure 2. For example, the choice of tissue area, along with the number and sizes of FoVs, can determine the degree to which the biological aspects of our interest (e.g., interesting cell subpopulations, or cell-cell communications) are captured in the generated HST data. Likewise, the number of cells and spots can affect the signal-to-noise ratios (effect sizes) of the generated HST data.

**Table 1.**A table shows six software tools for statistical power analysis for bulk RNA-seq experiments. Each tool is presented along with the citation and the software environments that have been implemented.

Tool Name [Citation] (Implementation) | |||
---|---|---|---|

Pilot Data | Pilot Data with Stored Data | ||

Type 1 Error | Poisson Log-normal | - | ‘Scotty’ [33] (Web Interface) |

Negative Binomial | ‘RNASeqPower’ [19] (R package) | - | |

FDR | ‘ssizeRNA’ [31] (R package) | ‘RnaSeqSampleSize’ [34] (R package) | |

‘RNASeqPowerCalculator’ [35] (R package) | ‘PROPER’ [32] (R package) |

**Table 2.**A table with information about different software tools for scRNA-seq power analysis with two distinct detection targets. Experimental Factors: cell number (1), individual number (2), Sequencing depth (3).

Detection Target | # of Samples | Tool Name | Experimental Factor | Software | Model | Power Assessment |
---|---|---|---|---|---|---|

Cell sub- population | Single sample | ‘SCOPIT’ [37] | (1) | R package & Web application | Multinomial | Analytical |

‘howmanycells’ | Web application | Negative Binomial | ||||

Multi sample | ‘Sensei‘ [38] | (1), (2) | Beta Binomial | |||

‘scPOST’ [39] | R package | Linear mixed model | Simulation- based | |||

DEG | ‘scPower’ [40] | (1), (2), (3) | R package & Web server | Negative Binomial | Pseudobulk | |

‘hierarchicell’ [41] | R package | Simulation- based | ||||

Single sample | ‘powsimR’ [42] | (1) | ||||

‘POWSC’ [43] | (1), (3) | A mixture of zero-inflated Poisson and log-normal Poisson distributions | ||||

‘scDesign’ [44] | Gamma-Normal mixture model |

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## 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