ReviewBriefings in bioinformatics2022
A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 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.
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.
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Who cites it
57 citing papers in PubMed, 72 citations in OpenAlex.
- Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.Cell reports methods · 2026Article
- Tissue and cellular spatiotemporal dynamics in colon aging.Nature biotechnology · 2026Article
- AddaGCN: Spatial transcriptomics deconvolution using graph convolutional networks with adversarial discriminative domain adaptation.PLoS computational biology · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Host-Directed Antiviral Strategies Against Influenza Viruses: Host Targets, Multi-Omics Approaches and AI-Assisted Discovery.Veterinary sciences · 2026Review
- SpatialCell AI achieves reference-free single-cell resolution from spot-based spatial transcriptomics through morphology-guided enhancement.Scientific reports · 2026Article
- Pan-cancer analysis of spatial transcriptomics reveals heterogeneous tumor spatial microenvironment.Cell reports. Medicine · 2026Article
- Spatial transcriptomics implicates the thalamus and cortex in autism and schizophrenia.bioRxiv : the preprint server for biology · 2026Article
- PFDN5 synergizes with anti-PD1 therapy to promote triple-negative breast cancer cell death through JAK2/STAT3/c-Myc signaling axis.Journal of translational medicine · 2026Article
- DSG2+ Cancer Stem Cells Co-Located With FAP+ Myofibroblasts in the Tumor Boundary That Determines the Efficacy of Immunotherapy in Non-Small Cell Lung Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Shared immune perturbations in hepatocellular carcinoma-depression comorbidity and synergistic immunotherapeutic effects of fluoxetine combined with anti-PD-1.Frontiers in immunology · 2026Article
- Integrated Single-Cell and Bulk RNA Sequencing Reveals Inflammatory Microenvironment and Glycolysis-Driven Epithelial Proliferation in Benign Prostatic Hyperplasia.Journal of inflammation research · 2026Article
- MIF-expressing tumor cells mediate immunotherapeutic resistance in esophageal squamous cell carcinoma.Theranostics · 2026Article
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- From pixels to cell types: a comprehensive review of computational methods for spatial transcriptomics deconvolution.Genomics & informatics · 2025Review
- ZipAEr: A compressive convolutional autoencoder for high-dimensional spatial omics data at subcellular resolution.Research square · 2025Article
- Spatial pattern enhanced cellular and tissue recognition for spatial transcriptomics.NAR genomics and bioinformatics · 2025Article
- Mitigation of multi-scale biases in cell-type deconvolution for spatially resolved transcriptomics using HarmoDecon.Bioinformatics (Oxford, England) · 2025Article
- SpaLinker identifies phenotype-associated spatial tumor microenvironment features by integrating bulk and spatial sequencing data.Cell genomics · 2025Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
Spatial transcriptomics (ST) technologies allow researchers to examine transcriptional profiles along with maintained positional information. Such spatially resolved transcriptional characterization of intact tissue samples provides an integrated view of gene expression in its natural spatial and functional context. However, high-throughput sequencing-based ST technologies cannot yet reach single cell resolution. Thus, similar to bulk RNA-seq data, gene expression data at ST spot-level reflect transcriptional profiles of multiple cells and entail the inference of cell-type composition within each ST spot for valid and powerful subsequent analyses. Realizing the critical importance of cell-type decomposition, multiple groups have developed ST deconvolution methods. The aim of this work is to review state-of-the-art methods for ST deconvolution, comparing their strengths and weaknesses. In particular, we construct ST spots from single-cell level ST data to assess the performance of 10 methods, with either ideal reference or non-ideal reference. Furthermore, we examine the performance of these methods on spot- and bead-level ST data by comparing estimated cell-type proportions to carefully matched single-cell ST data. In comparing the performance on various tissues and technological platforms, we concluded that RCTD and stereoscope achieve more robust and accurate inferences.
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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.