Two-stage clustering analysis to detect pattern change of biomarker expression between experimental conditions. (21st July 2020)

• Record Type: Journal Article
• Authors: Huh, Iksoo; Choi, Sunghoon; Kim, Youjin; Park, Soo-Yeon; Kwon, Oran; Park, Taesung
• Is Part Of: International journal of data mining and bioinformatics. Volume 23:Number 4(2020)
• Page Start: 299
• Page End: 317
• Publication Date: 2020-07-21
• Subjects: two-stage -- pattern clustering -- biomarker expression -- intervention study -- cross-over design; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

In a crossover design, individuals usually undergo all experimental conditions, and the measurements of biomarkers are repeatedly observed at serial time points for each experimental condition. To analyse time-dependent changing patterns of biomarkers, clustering algorithms are commonly used across time points to group together subjects having similar changing patterns. Among the clustering methods, hierarchical- and K-means clustering have been popularly used. However, since they are originally unsupervised approaches, they do not identify different changing patterns between experimental conditions. Therefore, we propose a new two-stage clustering method focusing on changing patterns. The first stage is to eliminate non-informative biomarkers using Euclidean distances, and the second stage is to allocate the remaining biomarkers to predefined patterns using a correlation-based distance. We demonstrate the advantages of our proposed method by simulation and real data analysis.