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

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-associat...

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Autores principales: Wang, Fang, Wang, Qihan, Mohanty, Vakul, Liang, Shaoheng, Dou, Jinzhuang, Han, Jincheng, Minussi, Darlan Conterno, Gao, Ruli, Ding, Li, Navin, Nicholas, Chen, Ken
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901082/
https://www.ncbi.nlm.nih.gov/pubmed/33622385
http://dx.doi.org/10.1186/s13059-021-02291-5
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author Wang, Fang
Wang, Qihan
Mohanty, Vakul
Liang, Shaoheng
Dou, Jinzhuang
Han, Jincheng
Minussi, Darlan Conterno
Gao, Ruli
Ding, Li
Navin, Nicholas
Chen, Ken
author_facet Wang, Fang
Wang, Qihan
Mohanty, Vakul
Liang, Shaoheng
Dou, Jinzhuang
Han, Jincheng
Minussi, Darlan Conterno
Gao, Ruli
Ding, Li
Navin, Nicholas
Chen, Ken
author_sort Wang, Fang
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02291-5.
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spelling pubmed-79010822021-02-23 MEDALT: single-cell copy number lineage tracing enabling gene discovery Wang, Fang Wang, Qihan Mohanty, Vakul Liang, Shaoheng Dou, Jinzhuang Han, Jincheng Minussi, Darlan Conterno Gao, Ruli Ding, Li Navin, Nicholas Chen, Ken Genome Biol Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02291-5. BioMed Central 2021-02-23 /pmc/articles/PMC7901082/ /pubmed/33622385 http://dx.doi.org/10.1186/s13059-021-02291-5 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Fang
Wang, Qihan
Mohanty, Vakul
Liang, Shaoheng
Dou, Jinzhuang
Han, Jincheng
Minussi, Darlan Conterno
Gao, Ruli
Ding, Li
Navin, Nicholas
Chen, Ken
MEDALT: single-cell copy number lineage tracing enabling gene discovery
title MEDALT: single-cell copy number lineage tracing enabling gene discovery
title_full MEDALT: single-cell copy number lineage tracing enabling gene discovery
title_fullStr MEDALT: single-cell copy number lineage tracing enabling gene discovery
title_full_unstemmed MEDALT: single-cell copy number lineage tracing enabling gene discovery
title_short MEDALT: single-cell copy number lineage tracing enabling gene discovery
title_sort medalt: single-cell copy number lineage tracing enabling gene discovery
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901082/
https://www.ncbi.nlm.nih.gov/pubmed/33622385
http://dx.doi.org/10.1186/s13059-021-02291-5
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