ArticleNucleic acids research2022
scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing 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 16 papers.
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Who cites it
16 citing papers in PubMed.
- PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Methylation-Associated Differentiation Features Define Biological and Prognostic Heterogeneity in CMS4 Colorectal Cancer.International journal of molecular sciences · 2026Article
- Single-cell phenotype-associated subpopulation identification via transfer foundation model and statistical ensemble learning.BMC biology · 2026Article
- Construction of a prognostic model and multidimensional analysis of hepatocellular carcinoma based on palmitoylation-related genes.Discover oncology · 2026Article
- A scissor-guided single-cell framework defines a macrophage-derived risk score for prognostic and immunotherapy stratification in lung adenocarcinoma.Frontiers in immunology · 2026Article
- Identification and experimental validation of CCL26 as a core comorbid gene and biomarker for atopic dermatitis and allergic asthma via machine learning and multi-omics analysis.Frontiers in immunology · 2026Article
- SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation.Nature communications · 2025Article
- Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning.eLife · 2025Article
- Graph-based deep learning for integrating single-cell and bulk transcriptomic data to identify clinical cancer subtypes.Briefings in bioinformatics · 2025Article
- Digital twins as global learning health and disease models for preventive and personalized medicine.Genome medicine · 2025Review
- The molecular subtypes of autoimmune diseases.Computational and structural biotechnology journal · 2024Review
- scPAS: single-cell phenotype-associated subpopulation identifier.Briefings in bioinformatics · 2024Article
- PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data.Briefings in bioinformatics · 2024Article
- Identifying phenotype-associated subpopulations through LP_SGL.Briefings in bioinformatics · 2023Article
- Cancer-Associated Fibroblast-Induced Remodeling of Tumor Microenvironment in Recurrent Bladder Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Robust joint clustering of multi-omics single-cell data via multi-modal high-order neighborhood Laplacian matrix optimization.Bioinformatics (Oxford, England) · 2023Article
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3 authors.
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Abstract
Although single-cell sequencing has provided a powerful tool to deconvolute cellular heterogeneity of diseases like cancer, extrapolating clinical significance or identifying clinically-relevant cells remains challenging. Here, we propose a novel computational method scAB, which integrates single-cell genomics data with clinically annotated bulk sequencing data via a knowledge- and graph-guided matrix factorization model. Once combined, scAB provides a coarse- and fine-grain multiresolution perspective of phenotype-associated cell states and prognostic signatures previously not visible by single-cell genomics. We use scAB to enhance live cancer single-cell RNA-seq data, identifying clinically-relevant previously unrecognized cancer and stromal cell subsets whose signatures show a stronger poor-survival association. The identified fine-grain cell subsets are associated with distinct cancer hallmarks and prognosis power. Furthermore, scAB demonstrates its utility as a biomarker identification tool, with the ability to predict immunotherapy, drug responses and survival when applied to melanoma single-cell RNA-seq datasets and glioma single-cell ATAC-seq datasets. Across multiple single-cell and bulk datasets from different cancer types, we also demonstrate the superior performance of scAB in generating prognosis signatures and survival predictions over existing models. Overall, scAB provides an efficient tool for prioritizing clinically-relevant cell subsets and predictive signatures, utilizing large publicly available databases to improve prognosis and treatments.
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