ArticlePhysical biology2021
STARCH: copy number and clone inference from spatial transcriptomics data.
Article in Physical biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Benchmarking copy number alteration inference methods for spatial transcriptomics.Nature communications · 2026Article
- HisToSpatialCNV: an interpretable deep learning method predicting spatial copy number variations from histopathology images.Nature biomedical engineering · 2026Article
- Spatial ecology of breast cancer reveals co-evolution of proliferative and dormant niches.Genome medicine · 2026Article
- Spatial-aware detection of copy number alterations from spatial transcriptomics using SpaCNA.Nature communications · 2026Article
- Single-cell omics data-driven decoding of tumor clonal evolution through reinforcement learning.Genome medicine · 2026Article
- Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications.Cancer cell · 2026Review
- CNAScope: pan-cancer copy number aberration database with functional annotation and interactive visualization.Nucleic acids research · 2026Article
- Pathogenomic analysis reveals clinically relevant epithelial-mesenchymal plasticity in esophageal squamous cell carcinoma.Theranostics · 2026Article
- Benchmarking scRNA-seq copy number variation callers.Nature communications · 2025Article
- Hybrid BAG-seq: DNA and RNA from the same single nucleus reveals interactions between genomic and transcriptomic landscapes in human tumor samples.Genome biology · 2025Article
- Cancer subclone detection based on DNA copy number in single-cell and spatial omic sequencing data.Nature methods · 2025Article
- Mitochondrial clone tracing within spatially intact human tissues.bioRxiv : the preprint server for biology · 2025Article
- Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma.Cell reports. Medicine · 2025Article
- Clonal phylogenies inferred from bulk, single cell, and spatial transcriptomic analysis of epithelial cancers.PloS one · 2025Article
- Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics.Nature methods · 2024Article
- SpottedPy quantifies relationships between spatial transcriptomic hotspots and uncovers environmental cues of epithelial-mesenchymal plasticity in breast cancer.Genome biology · 2024Article
- Integrative spatial and genomic analysis of tumor heterogeneity with Tumoroscope.Nature communications · 2024Article
- Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology.MedComm · 2024Review
- Answering open questions in biology using spatial genomics and structured methods.BMC bioinformatics · 2024Review
- A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data.Genome research · 2024Article
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Authors and funding
4 authors.
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Abstract
Tumors are highly heterogeneous, consisting of cell populations with both transcriptional and genetic diversity. These diverse cell populations are spatially organized within a tumor, creating a distinct tumor microenvironment. A new technology called spatial transcriptomics can measure spatial patterns of gene expression within a tissue by sequencing RNA transcripts from a grid of spots, each containing a small number of cells. In tumor cells, these gene expression patterns represent the combined contribution of regulatory mechanisms, which alter the rate at which a gene is transcribed, and genetic diversity, particularly copy number aberrations (CNAs) which alter the number of copies of a gene in the genome. CNAs are common in tumors and often promote cancer growth through upregulation of oncogenes or downregulation of tumor-suppressor genes. We introduce a new method STARCH (spatial transcriptomics algorithm reconstructing copy-number heterogeneity) to infer CNAs from spatial transcriptomics data. STARCH overcomes challenges in inferring CNAs from RNA-sequencing data by leveraging the observation that cells located nearby in a tumor are likely to share similar CNAs. We find that STARCH outperforms existing methods for inferring CNAs from RNA-sequencing data without incorporating spatial information.
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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.