  We analyze 14,651 HIV1 reverse transcriptase (HIV RT) sequences from the
Stanford HIV Drug Resistance Database labeled with treatment regimen in order
to study the evolution this enzyme under drug selection in the clinic. Our goal
is to identify distinct sectors of HIV RT's sequence space that are undergoing
evolution as a way to identify individual selections and/or evolutionary
solutions. We utilize Uniform Manifold Approximation and Projection (UMAP), a
graph-based dimensionality reduction technique uniquely suited for the
detection of non-linear dependencies and visualize the results using an
unsupervised clustering algorithm based on density analysis. Our analysis
produced 21 distinct clusters of sequences. Supporting the biological
significance of these clusters, they tend to represent phylogenetically related
sequences with strong correspondence to distinct treatment regimens. Thus, this
method for visualization of areas of HIV RT undergoing evolution can help infer
information about selective pressures, although it is correlative. The mutation
signatures associated with each cluster may represent the higher-order
epistatic context facilitating these evolutionary pathways, information that is
generally not accessible by other types of mutational co-dependence analyses.
