ArticleNature biotechnology2022
Spatially informed cell-type deconvolution for spatial transcriptomics.
Article in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 354 papers.
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Who cites it
354 citing papers in PubMed.
- Spatial multi‑omics identifies an SPP1+ macrophage‑driven anti‑apoptotic niche in osteoarthritis synovium via the SPP1‑CD44 axis.International journal of molecular medicine · 2026Article
- Unveiling the role of spatial transcriptomics in the analysis of the tumor immune microenvironment (Review).International journal of molecular medicine · 2026Review
- Characterizing intra- and intertumor heterogeneity in ovarian high-grade serous carcinoma subtypes using single-cell and spatial transcriptomics.Genome research · 2026Article
- Mapping Embryonic Mouse Lung Development Using Enhanced Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Single-cell and spatial transcriptomics reveals the spatiotemporal trajectory of the small peptide TAP4 in delaying postharvest fruit senescence.Plant communications · 2026Article
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- Cancer-associated fibroblast-derived POSTN as a metabolic "switch" shapes lipid-stressed macrophage polarization in gastric cancer.Redox biology · 2026Article
- Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.Nature genetics · 2026Article
- Stepwise multi-scale reconstruction of cell spatial organization from single-cell RNA sequencing data with Cell2space.Briefings in bioinformatics · 2026Article
- Spatial Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Tissue by spRandom-seq.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.Research square · 2026Article
- Spatial transcriptomic analysis reveals spatial features in human Stanford type A aortic dissection.iScience · 2026Article
- Benchmarking cell-type deconvolution in cross-platform transcriptomic data.Genome biology · 2026Article
- Immune niche composed of C1QNature communications · 2026Article
- SlotDeconv: spatial transcriptomics deconvolution via diversity-constrained prototype learning and spatial refinement.Bioinformatics (Oxford, England) · 2026Article
- Article
- Advances in deciphering intratumoral versus peritumoral heterogeneity of infiltrating lymphocytes in pancreatic cancer: a spatial perspective.Translational oncology · 2026Review
- Deciphering the Leading-Edge Spatiotemporal Microenvironment of Hepatocellular Carcinoma for Targeted Drug Discovery Using SpaPred.International journal of molecular sciences · 2026Article
- RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.Nature communications · 2026Article
294 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types. Here, we introduce a deconvolution method, conditional autoregressive-based deconvolution (CARD), that combines cell-type-specific expression information from single-cell RNA sequencing (scRNA-seq) with correlation in cell-type composition across tissue locations. Modeling spatial correlation allows us to borrow the cell-type composition information across locations, improving accuracy of deconvolution even with a mismatched scRNA-seq reference. CARD can also impute cell-type compositions and gene expression levels at unmeasured tissue locations to enable the construction of a refined spatial tissue map with a resolution arbitrarily higher than that measured in the original study and can perform deconvolution without an scRNA-seq reference. Applications to four datasets, including a pancreatic cancer dataset, identified multiple cell types and molecular markers with distinct spatial localization that define the progression, heterogeneity and compartmentalization of pancreatic cancer.
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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 Socratic graph.