ReviewBiomolecules2023
Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives.
Review in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed.
- VINE-seq and MultiVINE-seq for single-nucleus and multiome profiling of the brain vasculature.Nature protocols · 2026Review
- Tracing cell communication programs across conditions at single cell resolution with CCC-RISE.bioRxiv : the preprint server for biology · 2026Article
- Integration of Bulk and Single-Cell RNA Sequencing Analyses in Biomedicine.International journal of molecular sciences · 2026Review
- Characterising the Periodontal Granulation Tissue Using scRNAseq.Journal of clinical periodontology · 2026Article
- Sample size requirements for machine learning classification of binary outcomes in bulk RNA-Seq data.BMC bioinformatics · 2026Article
- Mapping safety in space: the emerging role of spatial transcriptomics in safe drug development.Frontiers in toxicology · 2026Review
- Spatial transcriptomics on an expanded dataset at the brain-electrode interface: exploration of variability and identification of novel biomarkers.Frontiers in neuroscience · 2026Article
- Evidence of an allostatic response by intestinal tissues following induction of joint inflammation.PloS one · 2026Article
- Comprehensive molecular characterization of craniopharyngiomas using whole transcriptome and spatial transcriptomics approaches.Brain tumor pathology · 2025Article
- PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.PLoS computational biology · 2025Article
- Review
- Genome-wide transcriptome differences associated with perceived discrimination in an urban, community-dwelling middle-aged cohort.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025Article
- Trajectory Inference for Single Cell Omics.ArXiv · 2025Article
- Rewired chromatin structure and epigenetic gene dysregulation during HTLV-1 infection to leukemogenesis.Cancer science · 2025Article
- Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery.Nature neuroscience · 2024Review
- Implementation and validation of single-cell genomics experiments in neuroscience.Nature neuroscience · 2024Review
- A distribution-free and analytic method for power and sample size calculation in single-cell differential expression.Bioinformatics (Oxford, England) · 2024Article
- TBX3 transfection and nodal signal pathway inhibition promote differentiation of adipose mesenchymal stem cell to cardiac pacemaker-like cells.Stem cell research & therapy · 2024Article
- Lessons learned from phase 3 trials of immunotherapy for glioblastoma: Time for longitudinal sampling?Neuro-oncology · 2024Article
- Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.Genome biology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.