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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