Phylogenetic analyses of large and diverse data sets generally result in large sets of competing phylogenetic trees. Consensus tree methods used to summarize sets of competing trees discard important information regarding the similarity and distribution of competing trees. A more fine grain approach is to use a dimensionality reduction method to project tree-to-tree distances in 2D or 3D space. In this study, we systematically evaluate the performance of several nonlinear dimensionality reduction (NLDR) methods on tree-to-tree distances obtained from independent nonparametric bootstrap analyses of genes from three mid- to large-sized mitochondrial genome alignments.

