ArticleNucleic acids research2022
CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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52 citing papers in PubMed, 78 citations in OpenAlex.
- Amplification bias in sequencing-based spatial transcriptomics: sources, mechanisms, impacts, and mitigation strategies.Briefings in bioinformatics · 2026Review
- SlotDeconv: spatial transcriptomics deconvolution via diversity-constrained prototype learning and spatial refinement.Bioinformatics (Oxford, England) · 2026Article
- AddaGCN: Spatial transcriptomics deconvolution using graph convolutional networks with adversarial discriminative domain adaptation.PLoS computational biology · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Robust integration and annotation of single-cell and spatial omics data using interpretable gene programs.Cell genomics · 2026Article
- S3RL: Enhancing Spatial Single-Cell Transcriptomics With Separable Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SpaJoint: a transfer learning method for spatial transcriptomics deconvolution.Briefings in bioinformatics · 2026Article
- DANST enables cell-type deconvolution in spatial transcriptomics using deep domain adversarial neural networks.Communications biology · 2026Article
- A spectral dimension reduction technique that improves pattern detection in multivariate spatial data.Bioinformatics (Oxford, England) · 2026Article
- Engineering and evaluation of precision-glycosylated clickable albumin nanoplatform for targeting the tumor microenvironment.Theranostics · 2026Article
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- stTransfer enables transfer of single-cell annotations to spatial transcriptomics with single-cell resolution.Cell reports methods · 2025Article
- Tumor microenvironment remodeling across thyroid cancer differentiation states revealed by spatial transcriptomics.Cancer immunology, immunotherapy : CII · 2025Article
- Deciphering Cell Type Abundance in Proteomics Data Through Graph Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Out of distribution learning in bioinformatics: advancements and challenges.Briefings in bioinformatics · 2025Review
- Article
- STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data.Genome medicine · 2025Article
- LETSmix: a spatially informed and learning-based domain adaptation method for cell-type deconvolution in spatial transcriptomics.Genome medicine · 2025Article
- Dual-mode action of scalable, high-quality engineered stem cell-derived SIRPα-extracellular vesicles for treating acute liver failure.Nature communications · 2025Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks and applied it to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. First, CellDART was applied to a mouse brain and a human dorsolateral prefrontal cortex tissue to identify cell types with a layer-specific spatial distribution. Overall, the proposed approach showed more stable and higher accuracy with short execution time compared to other computational methods to predict the spatial location of excitatory neurons. CellDART was capable of decomposing cellular proportion in mouse hippocampus Slide-seq data. Furthermore, CellDART elucidated the cell type predominance defined by the human lung cell atlas across the lung tissue compartments and it corresponded to the known prevalent cell types. CellDART is expected to help to elucidate the spatial heterogeneity of cells and their close interactions in various tissues.
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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.