Abstract
BackgroundMutation trees are rooted trees in which nodes are of arbitrary degree and labeled with a mutation set. These trees, also referred to as clonal trees, are used in computational oncology to represent the mutational history of tumours. Classical tree metrics such as the popular Robinson–Foulds distance are of limited use for the comparison of mutation trees. One reason is that mutation trees inferred with different methods or for different patients often contain different sets of mutation labels.ResultsWe generalize the Robinson–Foulds distance into a set of distance metrics called Bourque distances for comparing mutation trees. We show the basic version of the Bourque distance for mutation trees can be computed in linear time. We also make a connection between the Robinson–Foulds distance and the nearest neighbor interchange distance.
Highlights
Mutation trees are rooted trees in which nodes are of arbitrary degree and labeled with a mutation set
A number of tree metrics have been proposed for comparisons, including the Robinson–Foulds (RF) [6,7,8], nearestneighbor interchange (NNI) [7, 9] and triple(t) distances [10] for phylogenetic trees; gene duplication, gene loss and reconciliation costs [11, 12] for gene and species trees; and the tree-edit distances [5, 13, 14] for tree models of secondary RNA structures, etc. [15,16,17,18,19]
In "Metrics for labeled trees", we present a connection between the NNI distance and the RF distance for both phylogenetic and arbitrary trees that are unrooted and labeled
Summary
We generalize the Robinson–Foulds distance into a set of distance metrics called Bourque distances for comparing mutation trees. We show the basic version of the Bourque distance for mutation trees can be computed in linear time. We make a connection between the Robinson–Foulds distance and the nearest neighbor interchange distance
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