Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Abstract

Due to high mutation rates, COVID-19 evolved rapidly, and several variants such as Alpha, Gamma, Delta, Beta, and Omicron emerged with altered viral properties like the severity of the disease caused, transmission rates, etc. These variants burdened the medical systems worldwide and created a massive impact on the world economy as each had to be studied and dealt with in its specific ways. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. In this paper, we present a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and then compares the results from different dimensionality reduction methods including: Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation Projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for a particular variant (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We conclude that the proposed framework can effectively distinguish between the major variants and hence can be used for the identification of emerging variants in the future.