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MEDALT: single-cell copy number lineage tracing enabling gene discovery.

ABSTRACT: We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT .

SUBMITTER: Wang F

PROVIDER: S-EPMC7901082 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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MEDALT: single-cell copy number lineage tracing enabling gene discovery.

Wang Fang F Wang Qihan Q Mohanty Vakul V Liang Shaoheng S Dou Jinzhuang J Han Jincheng J Minussi Darlan Conterno DC Gao Ruli R Ding Li L Navin Nicholas N Chen Ken K

Genome biology 20210223 1

We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer pati ...[more]

PMID: 33622385

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