  Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) has a worldwide devastating effect. The
understanding of evolution and transmission of SARS-CoV-2 is of paramount
importance for the COVID-19 control, combating, and prevention. Due to the
rapid growth of both the number of SARS-CoV-2 genome sequences and the number
of unique mutations, the phylogenetic analysis of SARS-CoV-2 genome isolates
faces an emergent large-data challenge. We introduce a dimension-reduced
$k$-means clustering strategy to tackle this challenge. We examine the
performance and effectiveness of three dimension-reduction algorithms:
principal component analysis (PCA), t-distributed stochastic neighbor embedding
(t-SNE), and uniform manifold approximation and projection (UMAP). By using
four benchmark datasets, we found that UMAP is the best-suited technique due to
its stable, reliable, and efficient performance, its ability to improve
clustering accuracy, especially for large Jaccard distanced-based datasets, and
its superior clustering visualization. The UMAP-assisted $k$-means clustering
enables us to shed light on increasingly large datasets from SARS-CoV-2 genome
isolates.
