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.
@article{arxiv.2011.11070,
title = {Topological Data Analysis of copy number alterations in cancer},
author = {Stefan Groha and Caroline Weis and Alexander Gusev and Bastian Rieck},
journal= {arXiv preprint arXiv:2011.11070},
year = {2021}
}