Chow K-HK

Chung V

Elowitz MB

Garry DJ

Gong W

Granados AA

Guan Y

Han L

Hu J

Jones MG

Joseph T

Khodaverdian A

Kwak I-Y

Liu Z

Mason M

Meyer P

Peng J

Prusokas A

Rao A

Rao S

Raz O

Rennert P

Retkute R

Saipradeep VG

Salvador-Martinez I

Shang X

Shapiro E

Shendure J

Sivadasan N

Srinivasan R

Telford MJ

Wang R

Yosef N

Yu T

Zhang H

Zhang R

\ua9 2021 The AuthorsThe 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

Newcastle University E-Prints

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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

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

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