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

Fang Wang; Qihan Wang; Vakul Mohanty; Shaoheng Liang; Jinzhuang Dou; Jincheng Han; Darlan Conterno Minussi; Ruli Gao; Li Ding; Nicholas Navin; Ken Chen

doi:10.1186/s13059-021-02291-5

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

Fang Wang, Qihan Wang, Vakul Mohanty, Shaoheng Liang, Jinzhuang Dou, Jincheng Han, Darlan Conterno Minussi, Ruli Gao, Li Ding, Nicholas Navin, Ken Chen

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

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.

Original language	English
Article number	70
Journal	Genome biology
Volume	22
Issue number	1
DOIs	https://doi.org/10.1186/s13059-021-02291-5
State	Published - Dec 2021

Keywords

Copy number alteration
Driver discovery
Lineage tracing
Single-cell
Tumor evolution
scDNA-seq
scRNA-seq

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10.1186/s13059-021-02291-5

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title = "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.",

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AU - Wang, Fang

AU - Wang, Qihan

AU - Mohanty, Vakul

AU - Liang, Shaoheng

AU - Dou, Jinzhuang

AU - Han, Jincheng

AU - Minussi, Darlan Conterno

AU - Gao, Ruli

AU - Ding, Li

AU - Navin, Nicholas

AU - Chen, Ken

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KW - Driver discovery

KW - Lineage tracing

KW - Single-cell

KW - Tumor evolution

KW - scDNA-seq

KW - scRNA-seq

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

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Keywords

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