ArticleJournal of computational biology : a journal of computational molecular cell biology2020
Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Single-cell copy number alteration signature analysis reveals masked patterns and potential biomarkers for cancer.Communications biology · 2025Article
- Sc-TUSV-Ext: Single-Cell Clonal Lineage Inference from Single Nucleotide Variants, Copy Number Alterations, and Structural Variants.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Deconvolution and Phylogeny Inference of Diverse Variant Types Integrating Bulk DNA-seq with Single-cell RNA-seq.bioRxiv : the preprint server for biology · 2025Article
- Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data.Cancer pathogenesis and therapy · 2025Review
- Deconvolution and phylogeny inference of diverse variant types integrating bulk DNA-seq with single-cell RNA-seq.Bioinformatics advances · 2025Article
- Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations.Bioinformatics (Oxford, England) · 2022Article
- Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer.Bioinformatics (Oxford, England) · 2022Article
- Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data.Bioinformatics (Oxford, England) · 2021Article
- Joint Clustering of Single-Cell Sequencing and Fluorescence In Situ Hybridization Data for Reconstructing Clonal Heterogeneity in Cancers.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Assessing the contribution of tumor mutational phenotypes to cancer progression risk.PLoS computational biology · 2021Article
- Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis.Bioinformatics (Oxford, England) · 2020Article
- Incorporating biological structure into machine learning models in biomedicine.Current opinion in biotechnology · 2020Review
- Neural Network Deconvolution Method for Resolving Pathway-Level Progression of Tumor Clonal Expression Programs With Application to Breast Cancer Brain Metastases.Frontiers in physiology · 2020Article
- Algorithmic approaches to clonal reconstruction in heterogeneous cell populations.Quantitative biology (Beijing, China) · 2019Article
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Authors and funding
11 authors.
Funding
Abstract
Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but involves the analysis of a mixture of many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing is a promising alternative, but its limitations-for example, high noise, difficulty scaling to large populations, technical artifacts, and large data sets-have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semisimulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.
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