ArticleNature methods2023
In silico tissue generation and power analysis for spatial omics.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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41 citing papers in PubMed.
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- dbverse scales spatial omics analysis with embedded analytical databases.bioRxiv : the preprint server for biology · 2026Article
- Same-Slide Spatial Multiomics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.Cancer discovery · 2026Article
- SOFisher: reinforcement learning-guided experiment designs for spatial omics.Nature communications · 2026Article
- The Role that Biobanks Can Play in Driving Animal-Free Biomedical Research.Expert reviews in molecular medicine · 2026Review
- Spatially resolved transcriptomic and proteomic profiling reveals cell interaction programs that predict Barrett's esophagus progression.bioRxiv : the preprint server for biology · 2026Article
- Comparison and optimization of cellular neighbor preference methods for quantitative tissue analysis.Nature communications · 2026Article
- MultiCell: geometric learning in multicellular development.Nature methods · 2026Article
- Targeting the PRMT1 axis in cancer: from epigenetic plasticity to contextual therapeutic interventions.Frontiers in oncology · 2026Review
- Discovering paracrine regulators of cell type composition from spatial transcriptomics using SPER.Bioinformatics advances · 2026Article
- From cells to pixels: A decision tree for designing bioimage analysis pipelines.Journal of microscopy · 2025Article
- MIMYR: Generative modeling of missing tissue in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- sCCIgen: a high-fidelity spatially resolved transcriptomics data simulator for cell-cell interaction studies.Genome biology · 2025Article
- MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology.Genome medicine · 2025Article
- Multi-modal image analysis for large-scale cancer tissue studies within IMMUcan.Cell reports methods · 2025Article
- PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.PLoS computational biology · 2025Article
- Single-cell and spatial transcriptomics of stricturing Crohn's disease highlights a fibrosis-associated network.Nature genetics · 2025Article
- POPARI: Modeling multisample variation in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- From surfing to diving into the tumor microenvironment through multiparametric imaging mass cytometry.Frontiers in immunology · 2025Review
- Decoding immune low-response states in sepsis: single-cell and 3D spatial transcriptomic insights into immunoparalysis.Frontiers in immunology · 2025Review
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Abstract
As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis make this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics study. We introduce a method for tunable in silico tissue (IST) generation and use it with spatial profiling data sets to construct an exploratory computational framework for spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest. While we demonstrate ISTs in the context of spatial power analysis, these simulated tissues have other potential use cases, including spatial method benchmarking and optimization.
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