Topological Data Analysis of copy number alterations in cancer
- URL: http://arxiv.org/abs/2011.11070v2
- Date: Thu, 22 Apr 2021 17:28:37 GMT
- Title: Topological Data Analysis of copy number alterations in cancer
- Authors: Stefan Groha, Caroline Weis, Alexander Gusev, Bastian Rieck
- Abstract summary: We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach.
We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data.
- Score: 70.85487611525896
- License: http://creativecommons.org/licenses/by/4.0/
- 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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