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 journalArticle

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 languageEnglish (US)
Article number70
JournalGenome Biology
Volume22
Issue number1
DOIs
StatePublished - Dec 2021

Keywords

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

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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