K-Means Clustering for Features Arrangement in Metagenomic Data Visualization

Abstract

Personalized medicine is one of the most concern of the scientists to propose successful treatments for diseases. This approach considers patients’ genetic make-up and attention to their preferences, beliefs, attitudes, knowledge and social context. Deep learning techniques hold important roles and obtain achievements in bioinformatics tasks. Metagenomic data analysis is very important to develop and evaluate methods and tools applying to Personalized medicine. Metagenomic data is usually characterized by high-dimensional spaces where humans meet difficulties to interpret data. Visualizing metagenomic data is crucial to provide insights in data which can help researchers to explore patterns in data. Moreover, these visualizations can be fetched into deep learning such as Convolutional Neural Networks to do prediction tasks. In this study, we propose a visualization method for metagenomic data where features are arranged in the visualization based on K-means clustering algorithms. We show by experiments on metagenomic datasets of three diseases (Colorectal Cancer, Obesity and Type 2 Diabetes) that the proposed approach not only provides a robust method for visualization where we can observe clusters in the images but also enables us to improve the performance in disease prediction with deep learning algorithms.