ArticleBioinformatics (Oxford, England)2024
PCA-based spatial domain identification with state-of-the-art performance.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- BioMaster: Multi-agent system for automated bioinformatics analysis workflow.Patterns (New York, N.Y.) · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- SECTOR: structural entropy-based learning of spatiotemporal organisation in spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- GatorDuo: Global-Consistency Dual-Graph Refinement With Pseudo-Label Agreement for Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- SCALE: unsupervised multiscale domain identification in spatial omics data.Nucleic acids research · 2026Article
- jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.Bioinformatics advances · 2026Article
- Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.PLoS computational biology · 2025Article
- Spatiotemporal interaction of immune and renal cells controls glomerular crescent formation in autoimmune kidney disease.Nature immunology · 2025Article
- Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
8 authors.
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Abstract
motivationThe identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data.
resultsFollowing Occam's razor, we show that a simple PCA-based algorithm for unsupervised spatial domain identification rivals the performance of ten competing state-of-the-art methods across six single-cell spatial transcriptomic datasets. Our reductionist approach, NichePCA, provides researchers with intuitive domain interpretation and excels in execution speed, robustness, and scalability. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/imsb-uke/nichepca.
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