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Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees

  • Wuming Gong
  • , Alejandro A. Granados
  • , Jingyuan Hu
  • , Matthew G. Jones
  • , Ofir Raz
  • , Irepan Salvador-Martínez
  • , Hanrui Zhang
  • , Ke Huan K. Chow
  • , Il Youp Kwak
  • , Renata Retkute
  • , Alisa Prusokiene
  • , Augustinas Prusokas
  • , Alex Khodaverdian
  • , Richard Zhang
  • , Suhas Rao
  • , Robert Wang
  • , Phil Rennert
  • , Vangala G. Saipradeep
  • , Naveen Sivadasan
  • , Aditya Rao
  • Thomas Joseph, Rajgopal Srinivasan, Jiajie Peng, Lu Han, Xuequn Shang, Daniel J. Garry, Thomas Yu, Verena Chung, Michael Mason, Zhandong Liu, Yuanfang Guan, Nir Yosef, Jay Shendure, Maximilian J. Telford, Ehud Shapiro, Michael B. Elowitz, Pablo Meyer
  • University of Minnesota Twin Cities
  • California Institute of Technology
  • Baylor College of Medicine
  • University of California at Berkeley
  • University of California at San Francisco
  • Weizmann Institute of Science
  • University College London
  • University of Michigan, Ann Arbor
  • Chung-Ang University
  • University of Cambridge
  • Newcastle University
  • Imperial College London
  • EC Wise Inc.
  • Tata Group India
  • Northwestern Polytechnical University Xian
  • Sage Bionetworks
  • University of Washington
  • Allen Discovery Center for Cell Lineage Tracing
  • Brotman Baty Institute for Precision Medicine
  • Howard Hughes Medical Institute
  • IBM

科研成果: 期刊稿件文章同行评审

39 引用 (Scopus)

摘要

The recent advent of CRISPR and other molecular tools enabled the reconstruction of cell lineages based on induced DNA mutations and promises to solve the ones of more complex organisms. To date, no lineage reconstruction algorithms have been rigorously examined for their performance and robustness across dataset types and number of cells. To benchmark such methods, we decided to organize a DREAM challenge using in vitro experimental intMEMOIR recordings and in silico data for a C. elegans lineage tree of about 1,000 cells and a Mus musculus tree of 10,000 cells. Some of the 22 approaches submitted had excellent performance, but structural features of the trees prevented optimal reconstructions. Using smaller sub-trees as training sets proved to be a good approach for tuning algorithms to reconstruct larger trees. The simulation and reconstruction methods here generated delineate a potential way forward for solving larger cell lineage trees such as in mouse.

源语言英语
页(从-至)810-826.e4
期刊Cell Systems
12
8
DOI
出版状态已出版 - 18 8月 2021

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