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Andrew C. Tapia, Jerzy W. Jaromczyk, Neil Moore, and Christopher L. Schardl (329 Rose St., Lexington, KY 40508)
We show how two kinds of data visualization have aided us in understanding genetic distance matrices produced by our recent RNA-clique method. Genetic distance matrices quantify the extent to which the genomes of two or more samples differ. Usually, each sample represents an individual. The matrix provides a distance value for each pair of samples. Since the number of distances in the matrix grows quadratically with the number of samples, it can be difficult to discern patterns simply by inspecting the table of values, even when relatively few samples are considered. Visualization has become a necessary first step for interpretation in our analyses involving dozens of plant or animal samples. We show how a Principal Coordinates Analysis (PCoA) plot has revealed genetic structure in Brachyelytrum erectum populations. We also show two cases in which heatmaps have allowed us to identify outliers and other deviations from patterns in distance matrices. In the first, we detected a likely error in which sample labels might have been swapped. In the second, we discovered that a collection of plant samples, ostensibly from Bromus laevipes, might include samples from some related species.