Two-stage clustering analysis to detect pattern change of biomarker expression between experimental conditions
by Iksoo Huh; Sunghoon Choi; Youjin Kim; Soo-Yeon Park; Oran Kwon; Taesung Park
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 23, No. 4, 2020

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.

Online publication date: Mon, 27-Jul-2020

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