ReviewCommunications biology2022
Deciphering tissue structure and function using spatial transcriptomics.
Review in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 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
76 citing papers in PubMed, 112 citations in OpenAlex.
- Review
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- MCFST: spatial domain identification method based on multi-view graph convolutional network and graph fusion network.Bioinformatics (Oxford, England) · 2026Article
- Chromogenic in situ HCR as a targeted translational interface for routine molecular histology.Histochemistry and cell biology · 2026Article
- Decoding cellular communication networks and signaling pathways in bone, skeletal muscle, and bone-muscle crosstalk through spatial transcriptomics in a young male mouse.Bone research · 2026Article
- Advancing single-cell omics and cell-based therapeutics with quantum computing.Nature reviews. Molecular cell biology · 2026Review
- UNLOCKING MULTI-SAMPLE DIFFERENTIAL EXPRESSION FOR SPATIAL TRANSCRIPTOMICS DATA WITH TESSERA.bioRxiv : the preprint server for biology · 2026Article
- Comparison and optimization of cellular neighbor preference methods for quantitative tissue analysis.Nature communications · 2026Article
- Spatial Transcriptomics of Human Decidua Identifies Molecular Signatures in Recurrent Pregnancy Loss.Genomics, proteomics & bioinformatics · 2026Article
- Spatially structured inflammatory response in the presence of a uniform stimulus.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Benchmarking cell-type-specific spatially variable gene detection methods.Briefings in bioinformatics · 2026Article
- Toward next-generation machine learning and deep learning for spatial omics.Briefings in bioinformatics · 2026Review
- Advances in Spatial Transcriptomics in Bone.Current osteoporosis reports · 2026Review
- spammR: an R package designed for analysis and integration of spatial multi-omic measurements.Bioinformatics advances · 2026Article
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- A multimodal framework to identify molecular mechanisms driving patient group-associated morphology through the integration of spatial transcriptomics and whole slide imaging.NPJ artificial intelligence · 2026Article
- MMSpa is a deep learning-based tool that enhances the identification of spatial domains in spatial transcriptomics studies.PLoS biology · 2026Article
- Addressing the mean-variance relationship in spatially resolved transcriptomics data with spoon.Biostatistics (Oxford, England) · 2025Article
- Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.PLoS computational biology · 2025Article
- Article
16 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
5 authors at 1 institution in 1 country.
Funding
Abstract
The rapid development of spatial transcriptomics (ST) techniques has allowed the measurement of transcriptional levels across many genes together with the spatial positions of cells. This has led to an explosion of interest in computational methods and techniques for harnessing both spatial and transcriptional information in analysis of ST datasets. The wide diversity of approaches in aim, methodology and technology for ST provides great challenges in dissecting cellular functions in spatial contexts. Here, we synthesize and review the key problems in analysis of ST data and methods that are currently applied, while also expanding on open questions and areas of future development.
Indexed as
Identifiers
What Socratic 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 Socratic graph.