Hidden Markov Model Enables Robust Chromosome Segmentation in Sparse Single-Cell Sequencing
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Ridgley et al. have introduced a k-segment constrained hidden Markov model that caps the maximum number of state transitions, producing robust segmentation for loss of heterozygosity (LOH) in sparse single-cell whole-genome sequencing. Sparse data has made reliable LOH detection difficult, and constraining transitions is intended to suppress spurious segment boundaries. Benchmarked against sci-L3-Strand-seq segmentation, which resolves all seven mitotic crossover outcomes, the method achieved 86% LOH recall and 88% precision. The preprint was posted on bioRxiv Genomics on October 24, 2026. Open questions include performance across additional tumor types, platforms and coverage depths, and whether the approach transfers to clinical single-cell sequencing workflows.
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- biorxiv.orgPickRobust Chromosome Segmentation in Sparse Single-Cell Whole-Genome Sequencing Using a k-Segment Constrained Hidden Markov Model
Ridgley et al. propose a k-segment constrained hidden Markov model that limits the maximum number of state transitions, enabling robust segmentation for loss of heterozygosity (LOH) in sparse single-cell whole-genome sequencing. Benchmarked against sci-L3-Strand-seq segmentation, which resolves all seven mitotic crossover outcomes, the method achieved 86% LOH recall and 88% precision.
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