ArticleThe FEBS journal2021
Spage2vec: Unsupervised representation of localized spatial gene expression signatures.
Article in The FEBS journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- SANNO: A Graph-Transformer Enhanced Optimal Transport Tool for Spatial Transcriptomic Annotation.Interdisciplinary sciences, computational life sciences · 2026Article
- SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset.Bioinformatics (Oxford, England) · 2026Article
- Multi-omics to study chronic respiratory diseases and viral infections.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Spatial transcriptomics in the adulteLife · 2025Article
- Spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis.Nature genetics · 2025Article
- Spatial Transcriptomics in Human Cardiac Tissue.International journal of molecular sciences · 2025Review
- Machine learning integrative approaches to advance computational immunology.Genome medicine · 2024Review
- Spatial motifs reveal patterns in cellular architecture of complex tissues.bioRxiv : the preprint server for biology · 2024Article
- Review
- Principles and challenges of modeling temporal and spatial omics data.Nature methods · 2023Review
- Article
- FISHFactor: a probabilistic factor model for spatial transcriptomics data with subcellular resolution.Bioinformatics (Oxford, England) · 2023Article
- Article
- Article
- Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH.Nature biotechnology · 2023Article
- Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods.Computational and structural biotechnology journal · 2023Review
- A guidebook of spatial transcriptomic technologies, data resources and analysis approaches.Computational and structural biotechnology journal · 2023Review
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- Annotation of spatially resolved single-cell data with STELLAR.Nature methods · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Investigations of spatial cellular composition of tissue architectures revealed by multiplexed in situ RNA detection often rely on inaccurate cell segmentation or prior biological knowledge from complementary single-cell sequencing experiments. Here, we present spage2vec, an unsupervised segmentation-free approach for decrypting the spatial transcriptomic heterogeneity of complex tissues at subcellular resolution. Spage2vec represents the spatial transcriptomic landscape of tissue samples as a graph and leverages a powerful machine learning graph representation technique to create a lower dimensional representation of local spatial gene expression. We apply spage2vec to mouse brain data from three different in situ transcriptomic assays and to a spatial gene expression dataset consisting of hundreds of individual cells. We show that learned representations encode meaningful biological spatial information of re-occurring localized gene expression signatures involved in cellular and subcellular processes. DATABASE: Spatial gene expression data are available in Zenodo database at https://doi.org/10.5281/zenodo.3897401. Source code for reproducing analysis results and figures is available in Zenodo database at http://www.doi.org/10.5281/zenodo.4030404.
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.