ArticleNature methods2023
Alignment of spatial genomics data using deep Gaussian processes.
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 59 papers.
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Who cites it
59 citing papers in PubMed.
- ST-ConMa: a multimodal foundation framework for spatial transcriptomics via image-gene contrastive and matching learning.Briefings in bioinformatics · 2026Article
- OT-knn: A neighborhood-aware optimal transport framework for aligning spatial transcriptomics data.PLoS computational biology · 2026Article
- STcompare: comparative spatial transcriptomics data analysis of structurally matched tissues to characterize differentially spatially patterned genes.Bioinformatics (Oxford, England) · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Spatiotemporal cell type deconvolution leveraging tissue structure.Research square · 2026Article
- SPOmiAlign: a modality-agnostic computational framework for multimodal spatial omics alignment enabled by a feature matching foundation model.Briefings in bioinformatics · 2026Article
- Alignment of spatial transcriptomics slices across diseases, platforms and conditions.Genome medicine · 2026Article
- Multimodal spatial alignment and morphology mapping with MOSAICField.bioRxiv : the preprint server for biology · 2026Article
- Dynamic Factor Analysis for Sparse and Irregular Longitudinal Data: An Application to Metabolite Measurements in a COVID-19 Study.Statistics in medicine · 2026Article
- GALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Reconstructing Coherent Functional Landscape From Multi-Modal Multi-Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data.Genome biology · 2026Article
- Quantifying human-environment interactions through Bayesian modeling of species-resolved microbial transfer signatures: an exploratory proof-of-concept study.Frontiers in microbiology · 2026Article
- Unified integration of spatial transcriptomics across platforms with LLOKI.Genome research · 2025Article
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.Advances in neural information processing systems · 2025Article
- MaskGraphene: an advanced framework for interpretable joint representation for multi-slice, multi-condition spatial transcriptomics.Genome biology · 2025Article
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- Integrating cross-sample and cross-modal data for spatial transcriptomics and metabolomics with SpatialMETA.Nature communications · 2025Article
- The xIV-LDDMM toolkit of image-varifold based technologies for mapping 3D images and spatial-omics across scales.Communications biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Spatially resolved genomic technologies have allowed us to study the physical organization of cells and tissues, and promise an understanding of local interactions between cells. However, it remains difficult to precisely align spatial observations across slices, samples, scales, individuals and technologies. Here, we propose a probabilistic model that aligns spatially-resolved samples onto a known or unknown common coordinate system (CCS) with respect to phenotypic readouts (for example, gene expression). Our method, Gaussian Process Spatial Alignment (GPSA), consists of a two-layer Gaussian process: the first layer maps observed samples' spatial locations onto a CCS, and the second layer maps from the CCS to the observed readouts. Our approach enables complex downstream spatially aware analyses that are impossible or inaccurate with unaligned data, including an analysis of variance, creation of a dense three-dimensional (3D) atlas from sparse two-dimensional (2D) slices or association tests across data modalities.
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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.