Hierarchical Maximum Likelihood Clustering Approach

Alok Sharma, Keith A. Boroevich, Daichi Shigemizu, Yoichiro Kamatani, Michiaki Kubo, Tatsuhiko Tsunoda

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Objective: In this paper, we focused on developing a clustering approach for biological data. In many biological analyses, such as multiomics data analysis and genome-wide association studies analysis, it is crucial to find groups of data belonging to subtypes of diseases or tumors. Methods: Conventionally, the k-means clustering algorithm is overwhelmingly applied in many areas including biological sciences. There are, however, several alternative clustering algorithms that can be applied, including support vector clustering. In this paper, taking into consideration the nature of biological data, we propose a maximum likelihood clustering scheme based on a hierarchical framework. Results: This method can perform clustering even when the data belonging to different groups overlap. It can also perform clustering when the number of samples is lower than the data dimensionality. Conclusion: The proposed scheme is free from selecting initial settings to begin the search process. In addition, it does not require the computation of the first and second derivative of likelihood functions, as is required by many other maximum likelihood-based methods. Significance: This algorithm uses distribution and centroid information to cluster a sample and was applied to biological data. A MATLAB implementation of this method can be downloaded from the web link http://www.riken.jp/en/research/labs/ims/med-sci-math/.

Original languageEnglish
Article number7440832
Pages (from-to)112-122
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Volume64
Issue number1
DOIs
Publication statusPublished - 01-2017

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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  • Cite this

    Sharma, A., Boroevich, K. A., Shigemizu, D., Kamatani, Y., Kubo, M., & Tsunoda, T. (2017). Hierarchical Maximum Likelihood Clustering Approach. IEEE Transactions on Biomedical Engineering, 64(1), 112-122. [7440832]. https://doi.org/10.1109/TBME.2016.2542212