A multi-labeled tree dissimilarity measure for comparing "clonal trees" of tumor progression

Algorithms for Molecular Biology : AMB
Nikolai KarpovS Cenk Sahinalp

Abstract

We introduce a new dissimilarity measure between a pair of "clonal trees", each representing the progression and mutational heterogeneity of a tumor sample, constructed by the use of single cell or bulk high throughput sequencing data. In a clonal tree, each vertex represents a specific tumor clone, and is labeled with one or more mutations in a way that each mutation is assigned to the oldest clone that harbors it. Given two clonal trees, our multi-labeled tree dissimilarity (MLTD) measure is defined as the minimum number of mutation/label deletions, (empty) leaf deletions, and vertex (clonal) expansions, applied in any order, to convert each of the two trees to the maximum common tree. We show that the MLTD measure can be computed efficiently in polynomial time and it captures the similarity between trees of different clonal granularity well.

References

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Nov 27, 2014·Proceedings of the National Academy of Sciences of the United States of America·Charles GawadStephen R Quake
Jan 9, 2015·Bioinformatics·Salem MalikicCenk S Sahinalp
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Jan 6, 2017·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Nilgun DonmezS Cenk Sahinalp

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Citations

Jul 31, 2020·Bioinformatics·Simone CiccolellaGianluca Della Vedova
Oct 30, 2020·IScience·Erfan Sadeqi AzerS Cenk Sahinalp
Jan 1, 2021·Bioinformatics·Sarah ChristensenMohammed El-Kebir
Jun 12, 2021·Algorithms for Molecular Biology : AMB·Katharina JahnLouxin Zhang
Sep 1, 2021·BMC Bioinformatics·Leila BaghaarabaniBahram Goliaei

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Methods Mentioned

BETA
ISA

Software Mentioned

SCITE
SiFit
PMSPD
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