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Single-cell sequencing provides a powerful approach for elucidating intratumor heterogeneity by resolving cell-to-cell variability. However, it also poses additional challenges including elevated error rates, allelic dropout and non-uniform coverage. A recently introduced single-cell-specific mutation detection algorithm leverages the evolutionary relationship between cells for denoising the data. However, due to its probabilistic nature, this method does not scale well with the number of cells. Here, we develop a novel combinatorial approach for utilizing the genealogical relationship of cells in detecting mutations from noisy single-cell sequencing data. Our method, called scVILP, jointly detects mutations in individual cells and reconstructs a perfect phylogeny among these cells. We employ a novel Integer Linear Program algorithm for deterministically and efficiently solving the joint inference problem. We show that scVILP achieves similar or better accuracy but significantly better runtime over existing methods on simulated data. We also applied scVILP to an empirical human cancer dataset from a high grade serous ovarian cancer patient.
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This content will become publicly available on December 1, 2025
Unveiling cellular morphology: statistical analysis using a Riemannian elastic metric in cancer cell image datasets
Elastic metrics can provide a powerful tool to study the heterogeneity arising from cellular morphology. To assess their potential application (e.g. classifying cancer treated cells), we consider a specific instance of the elastic metric, the Square Root Velocity (SRV) metric and evaluate its performance against the linear metric for two datasets of osteosarcoma (bone cancer) cells including pharmacological treatments, and normal and cancerous breast cells. Our comparative statistical analysis shows superior performance of the SRV at capturing cell shape heterogeneity when comparing distance to the mean shapes, with better separation and interpretation between different cell groups. Secondly, when using multidimensional scaling (MDS) to find a low-dimensional embedding for unrescaled contours, we observe that while the linear metric better preserves original pairwise distances, the SRV yields better classification.
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- Award ID(s):
- 2227605
- PAR ID:
- 10562670
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Information Geometry
- Volume:
- 7
- Issue:
- S2
- ISSN:
- 2511-2481
- Page Range / eLocation ID:
- 845 to 859
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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