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

被引：0
作者：
Fang Wang
Qihan Wang
Vakul Mohanty
Shaoheng Liang
Jinzhuang Dou
Jincheng Han
Darlan Conterno Minussi
Ruli Gao
Li Ding
Nicholas Navin
Ken Chen
机构：
[1] The University of Texas MD Anderson Cancer Center,Department of Bioinformatics and Computational Biology
[2] Sun Yat-sen University,Present Address: Precision Medicine Institute, The First Affiliated Hospital
[3] Rice University,Department of Computer Science
[4] The University of Texas MD Anderson Cancer Center,Department of Cancer Biology
[5] The University of Texas MD Anderson Cancer Center,Department of Genetics
[6] Houston Methodist Research Institute,Department of Cardiovascular Sciences, Center for Bioinformatics and Computational Biology
[7] McDonnell Genome Institute Washington University School of Medicine,Department of Medicine
来源：
Genome Biology | / 22卷
关键词：
Single-cell; scDNA-seq; scRNA-seq; Copy number alteration; Tumor evolution; Lineage tracing; Driver discovery;
D O I：
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中图分类号：
学科分类号：
摘要：
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.
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