ArticleNature biotechnology2022
DestVI identifies continuums of cell types in spatial transcriptomics data.
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 164 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
164 citing papers in PubMed, 1 synthesis or guideline pooled it, 226 citations in OpenAlex.
- An integrated single cell and spatial transcriptomic map of human white adipose tissue.Nature communications · 2023Pooled it
- Article
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- Ten quick tips for spatial transcriptomics analysis.PLoS computational biology · 2026Review
- Plant Spatio-Temporal Integration Network (PSTN): a framework for dynamic single-cell and spatial transcriptomics in fungal-plant interactions.Bioinformatics (Oxford, England) · 2026Article
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.Research square · 2026Article
- ICAM1high Neutrophils Sculpt Tumor Evolution and Metastasis Through Symbiotic Adhesion and Reverse Migration.Cancer research · 2026Article
- NicheDeSig: niche-aware deconvolution and adaptive signature analysis for spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Tissue and cellular spatiotemporal dynamics in colon aging.Nature biotechnology · 2026Article
- The Great Escape: the systems biology of endocrine resistance and lineage plasticity.Endocrine-related cancer · 2026Review
- AddaGCN: Spatial transcriptomics deconvolution using graph convolutional networks with adversarial discriminative domain adaptation.PLoS computational biology · 2026Article
- SlotDeconv: spatial transcriptomics deconvolution via diversity-constrained prototype learning and spatial refinement.Bioinformatics (Oxford, England) · 2026Article
- Toward simultaneous pseudo-space reconstruction and cell-type deconvolution of single-cell spatial transcriptome using SpaDicer.Cell reports methods · 2026Article
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- A multi-scale graph frequency network for structural and functional region analysis in spatial transcriptomics.Functional & integrative genomics · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Spatial transcriptomics in ovarian biology technologies: computational challenges, and biological insights.Reproduction (Cambridge, England) · 2026Review
- Multi-task spatial distillation reveals cell-type-resolved programmed cell death landscapes in the human kidney.Briefings in bioinformatics · 2026Article
- Scalable multi-group nonnegative spatial factorization for spatial genomics data with cell-type heterogeneity.bioRxiv : the preprint server for biology · 2026Article
104 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
17 authors at 4 institutions in 3 countries.
Funding
Abstract
Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ).
Indexed as
Identifiers
What OpenQuestion holds
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.