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arXiv · 2011.11070

Topological Data Analysis of copy number alterations in cancer

Stefan Groha·Caroline Weis·Alexander Gusev·Bastian Rieck
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Abstract

Identifying subgroups and properties of cancer biopsy samples is a crucial step towards obtaining precise diagnoses and being able to perform personalized treatment of cancer patients. Recent data collections provide a comprehensive characterization of cancer cell data, including genetic data on copy number alterations (CNAs). We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach that encodes each cancer sample as a persistence diagram of topological features, i.e., high-dimensional voids represented in the data. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data and demonstrate the viability of some applications on finding substructures in cancer data as well as comparing similarity of cancer types.

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Stefan Groha, Caroline Weis, Alexander Gusev, Bastian Rieck. 2020-11-22. Topological Data Analysis of copy number alterations in cancer. https://arxiv.org/abs/2011.11070

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