ArticleCell reports2019
Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis.
Article in Cell reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 148 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.
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Who cites it
148 citing papers in PubMed, 234 citations in OpenAlex.
- PlantCCC prioritizes context-specific candidate ligand-receptor communication patterns in plant spatial transcriptomics.Plant molecular biology · 2026Article
- Decoding cell-cell communication in spatial transcriptomics: mechanistic insights, modeling constraints, and analytical caveats.Briefings in bioinformatics · 2026Review
- Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture.bioRxiv : the preprint server for biology · 2026Article
- Unraveling cell-cell communication through spatial transcriptomics: a review of computational methods.Briefings in bioinformatics · 2026Review
- Charting spatial ligand-target activity using Renoir.Nature communications · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Spatially Varying Graphical Models for Cell-Cell Interaction Networks in Multiplexed Tissue Imaging.bioRxiv : the preprint server for biology · 2026Article
- The Evolution of Spatial Omics Technologies Introduces A Novel Avenue for Lung Cancer Research.Genomics, proteomics & bioinformatics · 2026Review
- Impact and correction of segmentation errors in spatial transcriptomics.Nature genetics · 2026Article
- Spider: a flexible and unified framework for simulating spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Spatial omics for profiling the dynamic tumor microenvironment.Clinical & translational immunology · 2026Review
- Roles of Cellular Neighborhoods in Lung Cancer.Journal of Cancer · 2026Review
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer.Nature machine intelligence · 2026Article
- A unified framework for identification of cell-type-specific spatially variable genes in spatial transcriptomic studies.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Using spatial proteomics to enhance cell type assignments in histology images.Cell reports methods · 2025Article
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- sCCIgen: a high-fidelity spatially resolved transcriptomics data simulator for cell-cell interaction studies.Genome biology · 2025Article
- Cell-cell interactions as predictive and prognostic markers for drug responses in cancer.Genome medicine · 2025Review
- Integrating cross-sample and cross-modal data for spatial transcriptomics and metabolomics with SpatialMETA.Nature communications · 2025Article
88 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 4 countries.
Funding
Abstract
Technological advances enable assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing molecular variations in physiological contexts. While these methods are increasingly accessible, computational approaches for studying the interplay of the spatial structure of tissues and cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying different dimensions of spatial variation and in particular quantifies the effect of cell-cell interactions on gene expression. In a breast cancer Imaging Mass Cytometry dataset, our model yields interpretable spatial variance signatures, which reveal cell-cell interactions as a major driver of protein expression heterogeneity. Applied to high-dimensional imaging-derived RNA data, SVCA identifies plausible gene families that are linked to cell-cell interactions. SVCA is available as a free software tool that can be widely applied to spatial data from different technologies.
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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.