ArticleNucleic acids research2022
STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 87 papers.
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
87 citing papers in PubMed, 116 citations in OpenAlex.
- KGLAR: Deconvoluting Spatial Transcriptomics Data with Single-cell Transcriptomes through Knowledge-guided NMF and Least Angle Regression.Interdisciplinary sciences, computational life sciences · 2026Article
- Inferring gene expression profiles of tumor clones.Bioinformatics (Oxford, England) · 2026Article
- WDGTA: a wavelet-denoised graph attention framework for spatial transcriptomics deconvolution.Briefings in bioinformatics · 2026Article
- Binary-SPA: a reference-free method for cell annotation in high-resolution spatial transcriptomics.Nucleic acids research · 2026Article
- AddaGCN: Spatial transcriptomics deconvolution using graph convolutional networks with adversarial discriminative domain adaptation.PLoS computational biology · 2026Article
- Review
- 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
- Single-cell and spatial transcriptomics reveal mTOR-driven cellular fate of spindle cells and immune evasion in classic Kaposi's sarcoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Spatiotemporal cell type deconvolution leveraging tissue structure.Research square · 2026Article
- Review
- Ultra-precision deconvolution of spatial transcriptomics decodes immune heterogeneity and fate-defining programs in tissues.Nature communications · 2026Article
- Binary-SPA: A Reference-Free Method for Cell Annotation in High-Resolution Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- SpaJoint: a transfer learning method for spatial transcriptomics deconvolution.Briefings in bioinformatics · 2026Article
- Spatiotemporal cell type deconvolution leveraging tissue structure.bioRxiv : the preprint server for biology · 2026Article
- CellMap: precision mapping of cellular landscape in spatial transcriptomics.Nucleic acids research · 2026Article
- Spider: a flexible and unified framework for simulating spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Comprehensive analysis of the TGF-β signaling pathway: molecular mechanisms, disease drivers, and frontiers in clinical translation.Frontiers in immunology · 2026Review
- Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries.Molecular neurodegeneration advances · 2026Review
27 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrated STRIDE's utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell types to spatial locations but also improved the identification of spatially localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/wanglabtongji/STRIDE.
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.