Hierarchical Maximum Likelihood Clustering Approach

Objective: In this paper, we focused on devel- oping 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 includ- ing biological sciences. There are, however, several alter- native clustering algorithms that can be applied, including support vector clustering. In this paper, taking into consid- eration the nature of biological data, we propose a maxi- mum 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 pro- posed scheme is free from selecting initial settings to begin the search process. In addition, it does not require the com- putation of the first and second derivative of likelihood func- tions, as is required by many other maximum likelihood- based methods. Significance: This algorithm uses distri- bution and centroid information to cluster a sample and was applied to biological data. A MATLAB implementa- tion of this method can be downloaded from the web link http://www.riken.jp/en/research/labs/ims/med_sci_math/.