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

Carr, Ewan; Carrière, Mathieu; Michel, Bertrand; Chazal, Frédéric; Iniesta, Raquel

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.