ArticleCommunications biology2024
STEM enables mapping of single-cell and spatial transcriptomics data with transfer learning.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Stepwise multi-scale reconstruction of cell spatial organization from single-cell RNA sequencing data with Cell2space.Briefings in bioinformatics · 2026Article
- Translational insights and clinical challenges of targeting cancer stem cells.Signal transduction and targeted therapy · 2026Review
- CTMAP: an adversarial cross-modal learning framework for accurate and robust cell-type annotation in single-cell resolution spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Identification of Markers on the Basis of Transcriptomic Analysis for Molecular Assignment of Medulloblastoma.International journal of molecular sciences · 2026Article
- Inference of spatial chromatin accessibility via integration of spatial transcriptomics and single-cell multi-omics data.Nature communications · 2026Article
- Spatial Mapping of Single Cells via Correlation and Importance Between Cells and Spots.Interdisciplinary sciences, computational life sciences · 2026Article
- Topography-aware optimal transport for alignment of spatial omics data.Cell reports methods · 2026Article
- stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data.Innovation (Cambridge (Mass.)) · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- Inference of cell-type composition and single-cell spatial maps from spatial transcriptomics data with SWOT.Communications biology · 2025Article
- Refinement strategies for Tangram for reliable single-cell to spatial mapping.Bioinformatics (Oxford, England) · 2025Article
- Transfer learning of multicellular organization via single-cell and spatial transcriptomics.PLoS computational biology · 2025Article
- Long Non-Coding RNA Function in Smooth Muscle Cell Plasticity and Atherosclerosis.Arteriosclerosis, thrombosis, and vascular biology · 2025Review
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Deep learning in integrating spatial transcriptomics with other modalities.Briefings in bioinformatics · 2024Review
- SELF-Former: multi-scale gene filtration transformer for single-cell spatial reconstruction.Briefings in bioinformatics · 2024Article
- Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model.Communications biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Profiling spatial variations of cellular composition and transcriptomic characteristics is important for understanding the physiology and pathology of tissues. Spatial transcriptomics (ST) data depict spatial gene expression but the currently dominating high-throughput technology is yet not at single-cell resolution. Single-cell RNA-sequencing (SC) data provide high-throughput transcriptomic information at the single-cell level but lack spatial information. Integrating these two types of data would be ideal for revealing transcriptomic landscapes at single-cell resolution. We develop the method STEM (SpaTially aware EMbedding) for this purpose. It uses deep transfer learning to encode both ST and SC data into a unified spatially aware embedding space, and then uses the embeddings to infer SC-ST mapping and predict pseudo-spatial adjacency between cells in SC data. Semi-simulation and real data experiments verify that the embeddings preserved spatial information and eliminated technical biases between SC and ST data. We apply STEM to human squamous cell carcinoma and hepatic lobule datasets to uncover the localization of rare cell types and reveal cell-type-specific gene expression variation along a spatial axis. STEM is powerful for mapping SC and ST data to build single-cell level spatial transcriptomic landscapes, and can provide mechanistic insights into the spatial heterogeneity and microenvironments of tissues.
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.