Letter to the Editor: on the stability and internal consistency of component-wise sparse mixture regression-based clustering. Issue 1 (26th December 2021)

• Record Type: Journal Article
• Authors: Zhang, Bo; He, Jianghua; Hu, Jinxiang; Koestler, Devin C; Chalise, Prabhakar
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 1(2022)
• Publication Date: 2021-12-26
• Subjects: supervised learning -- mixture modeling -- disease heterogeneity; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab532
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Understanding the relationship between molecular markers and a phenotype of interest is often obfuscated by patient-level heterogeneity. To address this challenge, Chang et al. recently published a novel method called Component-wise Sparse Mixture Regression (CSMR), a regression-based clustering method that promises to detect heterogeneous relationships between molecular markers and a phenotype of interest under high-dimensional settings. In this Letter to the Editor, we raise awareness to several issues concerning the assessment of CSMR in Chang et al., particularly its assessment in settings where the number of features, P, exceeds the study sample size, N, and advocate for additional metrics/approaches when assessing the performance of regression-based clustering methodologies.