| Literature DB >> 33622385 |
Fang Wang1,2, Qihan Wang1,3, Vakul Mohanty1, Shaoheng Liang1, Jinzhuang Dou1, Jincheng Han4, Darlan Conterno Minussi5, Ruli Gao6, Li Ding7, Nicholas Navin5, Ken Chen8.
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 .Entities:
Keywords: Copy number alteration; Driver discovery; Lineage tracing; Single-cell; Tumor evolution; scDNA-seq; scRNA-seq
Mesh:
Year: 2021 PMID: 33622385 PMCID: PMC7901082 DOI: 10.1186/s13059-021-02291-5
Source DB: PubMed Journal: Genome Biol ISSN: 1474-7596 Impact factor: 13.583