CN-SBM: Categorical Block Modelling For Primary and Residual Copy Number Variation

• URL: http://arxiv.org/abs/2506.22963v1
• Date: Sat, 28 Jun 2025 17:45:45 GMT
• Title: CN-SBM: Categorical Block Modelling For Primary and Residual Copy Number Variation
• Authors: Kevin Lam, William Daniels, J Maxwell Douglas, Daniel Lai, Samuel Aparicio, Benjamin Bloem-Reddy, Yongjin Park,
• Abstract summary: We introduce the Copy Number Block Model (CN-SBM), a probabilistic framework that jointly clusters samples and genomic regions based on discrete copy number states.<n> CN-SBM respects the discrete nature of CNV calls and captures subpopulation-specific patterns through block-wise structure.<n>We derive a scalable variational inference algorithm for application to large cohorts and high-resolution data.
• Score: 1.7590081165362783
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cancer is a genetic disorder whose clonal evolution can be monitored by tracking noisy genome-wide copy number variants. We introduce the Copy Number Stochastic Block Model (CN-SBM), a probabilistic framework that jointly clusters samples and genomic regions based on discrete copy number states using a bipartite categorical block model. Unlike models relying on Gaussian or Poisson assumptions, CN-SBM respects the discrete nature of CNV calls and captures subpopulation-specific patterns through block-wise structure. Using a two-stage approach, CN-SBM decomposes CNV data into primary and residual components, enabling detection of both large-scale chromosomal alterations and finer aberrations. We derive a scalable variational inference algorithm for application to large cohorts and high-resolution data. Benchmarks on simulated and real datasets show improved model fit over existing methods. Applied to TCGA low-grade glioma data, CN-SBM reveals clinically relevant subtypes and structured residual variation, aiding patient stratification in survival analysis. These results establish CN-SBM as an interpretable, scalable framework for CNV analysis with direct relevance for tumor heterogeneity and prognosis.

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