CN-SBM: Categorical Block Modelling For Primary and Residual Copy Number Variation
This provides an interpretable, scalable method for analyzing tumor heterogeneity in cancer genomics, though it appears incremental as an extension of stochastic block models to a specific domain.
The authors tackled the problem of analyzing noisy copy number variation data in cancer genomics by developing CN-SBM, a probabilistic framework that jointly clusters samples and genomic regions using a bipartite categorical block model, which revealed clinically relevant subtypes and improved patient stratification in survival analysis on TCGA low-grade glioma data.
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.