In interventional studies, biomarkers such as metabolites, are usually measured across serial time points. And when the interest lies in comparing expression levels between different experimental conditions, summary measures such as area under curve (AUC), have been widely used. Although the summary measure based approaches have been successful in identifying novel biomarkers, they do not reveal anything about time-dependent changing patterns of biomarkers which can demonstrate the reactivity of biomarkers to various physiological conditions. To account for such patterns, all measurements across time points need to be used, and clustering analysis with the measurements can group together biomarkers having similar changing patterns. Some such popularly used clustering methods include hierarchical- and K-means clustering. While these may provide some well-clustered results, their patterns are quite dependent on input data sets, making it difficult to obtain consistent patterns across different interventional studies. In addition, it is problematic for these methods to discriminate biomarkers with weakly active patterns that need to be grouped as static, compared to those having strongly active patterns, when their patterns are highly similar. To address these issues, we propose a new clustering method for improving identification of changing patterns. Our approach is based on a two-stage process: the first is elimination of stable markers using Euclidean distances, while the second stage assigns the remaining biomarkers to predefined patterns using 1-correlation distance measure. By simulation studies, we showed that our proposed method had superior classification performances, compared to other unsupervised clustering methods. We expect that this approach can complement the existing summary measure based approaches.
|Title of host publication||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Editors||Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Nov 2019|
|Event||2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States|
Duration: 18 Nov 2019 → 21 Nov 2019
|Name||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Conference||2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Period||18/11/19 → 21/11/19|
Bibliographical noteFunding Information:
This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (2013M3A9C4078158) results of these AUC measurements do not reflect dynamic patterns of biomarkers across time points; such patterns can be an important piece of information, with respect to reactivity to experimental conditions.
© 2019 IEEE.
- Biomarker expression
- Pattern Clustering