MapperPlus: Agnostic clustering of high-dimension data for precision medicine

• Published in: PLOS digital health Vol. 2; no. 8; p. e0000307
• Main Authors: Datta, Esha, Ballal, Aditya, López, Javier E, Izu, Leighton T
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.08.2023; Public Library of Science (PLoS)
• Subjects: Algorithms; Archives & records; Clustering; Datasets; Disease; Ejection fraction; Machine learning; Medical research; Medicine and Health Sciences; Patients; Physical Sciences; Precision medicine; Research and Analysis Methods; Stem cells; Survival
• ISSN: 2767-3170; 2767-3170
• DOI: 10.1371/journal.pdig.0000307

One of the goals of precision medicine is to classify patients into subgroups that differ in their susceptibility and response to a disease, thereby enabling tailored treatments for each subgroup. Therefore, there is a great need to identify distinctive clusters of patients from patient data. There are three key challenges to three key challenges of patient stratification: 1) the unknown number of clusters, 2) the need for assessing cluster validity, and 3) the clinical interpretability. We developed MapperPlus, a novel unsupervised clustering pipeline, that directly addresses these challenges. It extends the topological Mapper technique and blends it with two random-walk algorithms to automatically detect disjoint subgroups in patient data. We demonstrate that MapperPlus outperforms traditional agnostic clustering methods in key accuracy/performance metrics by testing its performance on publicly available medical and non-medical data set. We also demonstrate the predictive power of MapperPlus in a medical dataset of pediatric stem cell transplant patients where a number of cluster is unknown. Here, MapperPlus stratifies the patient population into clusters with distinctive survival rates. The MapperPlus software is open-source and publicly available.