2020
DOI: 10.1093/bioinformatics/btaa464
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PhISCS-BnB: a fast branch and bound algorithm for the perfect tumor phylogeny reconstruction problem

Abstract: Motivation Recent advances in single-cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program, or a constraint satisfaction program, which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain or alternative heuristics not only offer no su… Show more

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Cited by 21 publications
(18 citation statements)
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“…Time-efficient analysis of such datasets will require a design of more efficient computational methods that scale well to the larger inputs. Recently developed PhISCS-BnB (Sadeqi Azer et al, 2020b) is one of the methods designed to fill this gap. As demonstrated on the simulated data, PhISCS-BnB finds a solution with the optimality guarantee typically 10 to 100 times faster than PhISCS and can successfully find the optimal solution (within 24 hours) on most of the inputs as large as 300 × 300, whereas PhISCS convergence is usually limited to matrices of size 100 × 100 or smaller.…”
Section: The Existing Methods For Tree Inference Based On the Search mentioning
confidence: 99%
“…Time-efficient analysis of such datasets will require a design of more efficient computational methods that scale well to the larger inputs. Recently developed PhISCS-BnB (Sadeqi Azer et al, 2020b) is one of the methods designed to fill this gap. As demonstrated on the simulated data, PhISCS-BnB finds a solution with the optimality guarantee typically 10 to 100 times faster than PhISCS and can successfully find the optimal solution (within 24 hours) on most of the inputs as large as 300 × 300, whereas PhISCS convergence is usually limited to matrices of size 100 × 100 or smaller.…”
Section: The Existing Methods For Tree Inference Based On the Search mentioning
confidence: 99%
“…The problem of denoising a binary matrix obtained from SCS data to obtain a conflict-free matrix has recently attracted attention in the field of tumor phylogenetics ( Ciccolella et al., 2018 ; Edrisi et al, 2019 ; El-Kebir, 2018 ; Malikic et al., 2019b ; Sadeqi Azer et al., 2020 ). The general denoising problem, which we call the noise _ elimination problem, is defined as follows:…”
Section: Preliminariesmentioning
confidence: 99%
“…More recently, SPhyR ( El-Kebir, 2018 ), a combinatorial optimization approach based on “Dollo” parsimony, and SiCloneFit ( Zafar et al., 2019 ), which is an improved version of SiFit, were proposed. Finally, PhISCS-BnB ( Sadeqi Azer et al., 2020 ), another combinatorially optimal tool utilizing a branch-and-bound strategy, and ScisTree ( Wu, 2020 ), a neighbor joining–based heuristic, are among the most recently developed methods that offer significant improvements in running time.…”
Section: Introductionmentioning
confidence: 99%
“…Finally, a local clustering of cells was performed for reducing the sparsity of the data - as a result each leaf depicted in the tree represents a combination of multiple cells with similar mutational profiles. The final phylogeny on these cell clusters was obtained by applying PhISCS [15, 2, 16].…”
Section: Single Cell Methylation Phylogeny Reconstructionmentioning
confidence: 99%
“…Before we show the methylation phylogenies constructed using our proposed framework, for comparison, we provide the tumor phylogeny we obtained by applying tools such as PhISCS [15, 2, 16] from mutation calls through the use of scRNA-seq data. Even though mutation data is, in principle, better suited for cellular lineage identification and phylogeny reconstruction, the sparsity of scRNA-seq data makes it very difficult to infer a reliable tumor phylogeny.…”
Section: Single Cell Methylation Phylogeny Reconstructionmentioning
confidence: 99%