Orchard: Building large cancer phylogenies using stochastic combinatorial search
Fig 2
Example of Orchard’s mutation tree search with k = 2, f = ∞.
Mutation trees are depicted using genotype matrices. Search begins with a genotype matrix B(1) containing the first mutation in π. During each iteration, the best tree t(ℓ) is popped from the queue and extended. The extensions are scored and reintroduced into the queue. Only the k trees with the highest scores in the queue are kept, while others are discarded. The bars next to each genotype matrix indicate its perturbed log probability, Gϕ. Bars with grey fill correspond to the top-k trees that are retained and extended. Genotype matrices within dashed boxes denote parts of the search space that are not explored further. Orchard’s best reconstructed tree can then be input into the phylogeny-aware clustering algorithm. This algorithm conducts agglomerative clustering on the mutation trees to produce a set of clone trees. Each clone tree’s set of clones is scored, and the algorithm yields the clone tree that minimizes the Generalized Information Criterion (GIC). See Section 2.6 and Section A4 in S1 Appendix for complete details.