ArticleNature communications2023
A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 166 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
166 citing papers in PubMed.
- SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Article
- Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions.Briefings in bioinformatics · 2026Review
- WDGTA: a wavelet-denoised graph attention framework for spatial transcriptomics deconvolution.Briefings in bioinformatics · 2026Article
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.Research square · 2026Article
- Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.Cell reports methods · 2026Article
- Benchmarking cell-type deconvolution in cross-platform transcriptomic data.Genome biology · 2026Article
- Binary-SPA: a reference-free method for cell annotation in high-resolution spatial transcriptomics.Nucleic acids research · 2026Article
- NicheDeSig: niche-aware deconvolution and adaptive signature analysis for spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- PRISM: Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping.Bioinformatics (Oxford, England) · 2026Article
- Same-Slide Spatial Multiomics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.Cancer discovery · 2026Article
- DESpace2: detection of differential spatial patterns in spatial omics data.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
- RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics.Nature communications · 2026Article
- Review
- The Single-Cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors.Cell genomics · 2026Article
- CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images.Nature protocols · 2026Review
- Spatial transcriptomics in ovarian biology technologies: computational challenges, and biological insights.Reproduction (Cambridge, England) · 2026Review
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
106 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and reconstruction of tissue architecture. Due to the existence of low-resolution spots in recent spatial transcriptomics technologies, uncovering cellular heterogeneity is crucial for disentangling the spatial patterns of cell types, and many related methods have been proposed. Here, we benchmark 18 existing methods resolving a cellular deconvolution task with 50 real-world and simulated datasets by evaluating the accuracy, robustness, and usability of the methods. We compare these methods comprehensively using different metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. In terms of performance, CARD, Cell2location, and Tangram are the best methods for conducting the cellular deconvolution task. To refine our comparative results, we provide decision-tree-style guidelines and recommendations for method selection and their additional features, which will help users easily choose the best method for fulfilling their concerns.
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.