ArticleNature biotechnology2025
The covariance environment defines cellular niches for spatial inference.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
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Who cites it
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cross-expression meta-analysis of mouse brain slices reveals coordinated gene expression across spatially adjacent cells.Genome biology · 2025Pooled it
- Pan-tumor spatial transcriptomics reveals conserved properties of tumor organization.Cell reports. Medicine · 2026Article
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- Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data.Nature communications · 2026Article
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- MuSpAn: a toolbox for multiscale spatial analysis.Nature communications · 2026Article
- Mapping mesenchymal diversity in the human small intestine and organoids.Nature cell biology · 2026Article
- ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.Bioinformatics (Oxford, England) · 2026Article
- Spatiotemporal cell type deconvolution leveraging tissue structure.Research square · 2026Article
- Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities.bioRxiv : the preprint server for biology · 2026Article
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- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data.bioRxiv : the preprint server for biology · 2026Article
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- Uncovering Immune Niches in Health and Disease Using Spatial Transcriptomics.European journal of immunology · 2026Review
- Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP.bioRxiv : the preprint server for biology · 2026Article
- Spatiotemporal cell type deconvolution leveraging tissue structure.bioRxiv : the preprint server for biology · 2026Article
- SCALPEL: A pipeline for processing large-scale spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Tumor-infiltrating bacteria disrupt cancer epithelial cell interactions and induce cell-cycle arrest.Cancer cell · 2026Article
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Abstract
A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.
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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.