ArticleNature communications2024
Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Clinical translation of senescence-related pan-cancer multi-omics: tools for assessment and immunotherapy prediction.Cancer gene therapy · 2026Article
- Multi-modality Graph Representation Learning for Malignant Cell Identification from scRNA-seq using DeepMalignant.bioRxiv : the preprint server for biology · 2026Article
- Nuclear factor of activated T cells as a driver of tumor progression and a target for precision therapy.Animal models and experimental medicine · 2026Review
- SP-printer: reconstruction of tumor stage-specific microenvironments via phenotype-integrated spatial transcriptomics.Journal of translational medicine · 2026Article
- A Multi-Omics Framework Reveals Tumor Heterogeneity and Predicts Therapeutic Targets in Renal Cell Carcinoma.International journal of molecular sciences · 2026Article
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Reinvigorating COTL1Nature cell biology · 2026Article
- Lysosome-dependent cell death reveals a prognostic signature in colorectal cancer via integrated analysis of scRNA-seq and bulk RNA-seq data.Translational cancer research · 2026Article
- RUNX2 drives adenoma-to-carcinoma transition in colon cancer.Cell death & disease · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Article
- scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.Communications biology · 2026Article
- So3D: a comprehensive three-dimensional spatial omics resource for decoding tissue architecture in physiology and disease.Nucleic acids research · 2026Article
- Identification of programmed cell death-related subtypes reveals immune heterogeneity and therapeutic divergence in colon cancer.Theranostics · 2026Article
- Spatial Transcriptomics Unveils Landscape of Resistance to Concurrent Chemo-Radiotherapy in Hypopharyngeal Squamous Cell Carcinoma: The Role of SPP1Cancer medicine · 2026Article
- scCirclehunter delineates ecDNA-containing cells using single-cell ATAC-seq, with a focus on glioblastoma.Cell discovery · 2025Article
- Immunoregulatory Roles of Tumor-Originated Pericytes Identified by Single-Cell Analysis in Glioblastoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Integrating Single-Cell and Spatial Transcriptomics Reveals NK Cell Subpopulations Associated With Immunotherapy for Melanoma.Smart medicine · 2025Article
- Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics.World journal of gastrointestinal oncology · 2025Review
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11 authors.
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Abstract
Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation.
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