Integrating Multidimensional Data for Clustering Analysis With Applications to Cancer Patient Data

• Published in: Journal of the American Statistical Association Vol. 116; no. 533; pp. 14 - 26
• Main Authors: Park, Seyoung, Xu, Hao, Zhao, Hongyu
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 02.01.2021; Taylor & Francis Ltd
• Subjects: Algorithms; Cancer; Cluster analysis; Clustering; Convergence; Etiology; Gaussian kernel; genome; Genomics; Multi-omics data; Multidimensional data; Optimization; Patients; Regression analysis; Robustness; Spectral clustering; Statistical methods; Statistics; Subtypes

Advances in high-throughput genomic technologies coupled with large-scale studies including The Cancer Genome Atlas (TCGA) project have generated rich resources of diverse types of omics data to better understand cancer etiology and treatment responses. Clustering patients into subtypes with similar...