ReviewExperimental & molecular medicine2020
Single-cell transcriptomics in cancer: computational challenges and opportunities.
Review in Experimental & molecular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
112 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Systematic benchmarking and optimal strategy selection of cross-species integration methods.Briefings in bioinformatics · 2026Article
- Systematic characterization of neurotransmitter receptor dysregulation identifies a neural-related prognostic signature associated with biochemical recurrence in prostate cancer.Translational andrology and urology · 2026Article
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- Spatial Omics Technologies in Glioblastoma Research: Principles, Applications, and Best Practices.Genes · 2026Review
- CancerOmicsStudio (CoS): a web server for integrative and interpretable analysis of multi-omics cancer data.Bioinformatics (Oxford, England) · 2026Article
- Predicting targeted- and immunotherapeutic response outcomes in melanoma with single-cell Raman spectroscopy and AI.bioRxiv : the preprint server for biology · 2026Article
- Analysis of microsatellite instability intensity in single-cell resolution with scMnT reveals tumor heterogeneity in colorectal cancer.Briefings in bioinformatics · 2026Article
- Characterizing malignant prognostic signatures in primary glioma based on single-cell and bulk transcriptome sequencing.PloS one · 2026Article
- Tumor prognostic risk stratification based on pseudo-time analysis of single-cell sequencing for patients with lung adenocarcinoma.Journal of thoracic disease · 2025Article
- Prostate cancer cells converge to an inflammatory-like state upon metastatic dissemination.Nature communications · 2025Article
- AI-derived five-gene signature predicts risk in multiple myeloma under bortezomib-based therapy.Scientific reports · 2025Article
- Multi-omics strategies for biomarker discovery and application in personalized oncology.Molecular biomedicine · 2025Review
- Cell state dynamics during early stages of serous endometrial carcinoma.The Journal of pathology · 2025Article
- Benchmarking cell type and gene set annotation by large language models with AnnDictionary.Nature communications · 2025Article
- Barcodes based on nucleic acid sequences: Applications and challenges (Review).Molecular medicine reports · 2025Review
- Comprehensive Integrated Analysis Reveals the Spatiotemporal Microevolution of Cancer Cells in Patients with Bone-Metastatic Prostate Cancer.Biomedicines · 2025Article
- Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.Journal of proteome research · 2025Review
- scMalignantFinder distinguishes malignant cells in single-cell and spatial transcriptomics by leveraging cancer signatures.Communications biology · 2025Article
- CITEgeist: Cellular Indexing of Transcriptomes and Epitopes for Guided Exploration of Intrinsic Spatial Trends.bioRxiv : the preprint server for biology · 2025Article
52 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Intratumor heterogeneity is a common characteristic across diverse cancer types and presents challenges to current standards of treatment. Advancements in high-throughput sequencing and imaging technologies provide opportunities to identify and characterize these aspects of heterogeneity. Notably, transcriptomic profiling at a single-cell resolution enables quantitative measurements of the molecular activity that underlies the phenotypic diversity of cells within a tumor. Such high-dimensional data require computational analysis to extract relevant biological insights about the cell types and states that drive cancer development, pathogenesis, and clinical outcomes. In this review, we highlight emerging themes in the computational analysis of single-cell transcriptomics data and their applications to cancer research. We focus on downstream analytical challenges relevant to cancer research, including how to computationally perform unified analysis across many patients and disease states, distinguish neoplastic from nonneoplastic cells, infer communication with the tumor microenvironment, and delineate tumoral and microenvironmental evolution with trajectory and RNA velocity analysis. We include discussions of challenges and opportunities for future computational methodological advancements necessary to realize the translational potential of single-cell transcriptomic profiling in cancer.
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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.