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

Hyeongseon Jeon1,2, Juan Xie1,2,3, Yeseul Jeon1,4,5

  • 1Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.

Biomolecules
|February 25, 2023
PubMed
Summary

This review discusses statistical power analysis for gene expression profiling technologies like bulk RNA-seq and single-cell RNA-seq, highlighting tools and factors for robust transcriptomic research.

Keywords:
RNA-seqgene expression analysishigh-throughput spatial transcriptomicspower analysisscRNA-seqtranscriptomics

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology
  • Statistical Genetics

Background:

  • Gene expression profiling is crucial for target discovery in transcriptomic studies, with technologies like bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics yielding distinct data characteristics.
  • Ensuring biologically meaningful findings from transcriptomic experiments necessitates systematic consideration of experimental factors via statistical power analysis.

Purpose of the Study:

  • To review and practically discuss power analysis for bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics.
  • To describe existing power analysis tools and provide recommendations for bulk and single-cell RNA-seq experiments.
  • To investigate factors influencing power analysis for spatial transcriptomics, given the current lack of dedicated tools.

Main Methods:

  • Literature review and discussion of statistical power analysis principles.
  • Examination of existing power analysis tools and methodologies for bulk and single-cell RNA-seq.
  • Exploration of key factors impacting statistical power in high-throughput spatial transcriptomics.

Main Results:

  • Established power analysis tools and recommendations are available for bulk and single-cell RNA-seq, aiding researchers in experimental design.
  • No specific power analysis tools currently exist for high-throughput spatial transcriptomics, necessitating a focus on influential factors.
  • Key factors affecting power in spatial transcriptomics include data resolution, cell type heterogeneity, and spatial autocorrelation.

Conclusions:

  • Systematic power analysis is essential for reliable transcriptomic research across different profiling technologies.
  • Researchers using bulk and single-cell RNA-seq can leverage existing tools and recommendations for robust experimental design.
  • Further development of power analysis methodologies is needed for high-throughput spatial transcriptomics to ensure statistically sound discoveries.