Identifying homogeneous subgroups of patients and important features: a topological machine learning approach

• Main Authors: Ewan Carr, Mathieu Carrière, Bertrand Michel, Frédéric Chazal, Raquel Iniesta
• Format: Article
• Language: English
• Published: BMC 2021-09-01
• Series: BMC Bioinformatics
• Subjects: Topological data analysis; Clustering; Machine learning
• Online Access: https://doi.org/10.1186/s12859-021-04360-9
• ISSN: 1471-2105

Abstract Background This paper exploits recent developments in topological data analysis to present a pipeline for clustering based on Mapper, an algorithm that reduces complex data into a one-dimensional graph. Results We present a pipeline to identify and summarise clusters based on statistically significant topological features from a point cloud using Mapper. Conclusions Key strengths of this pipeline include the integration of prior knowledge to inform the clustering process and the selection of optimal clusters; the use of the bootstrap to restrict the search to robust topological features; the use of machine learning to inspect clusters; and the ability to incorporate mixed data types. Our pipeline can be downloaded under the GNU GPLv3 license at https://github.com/kcl-bhi/mapper-pipeline .