Gong, W;
Granados, AA;
Hu, J;
Jones, MG;
Raz, O;
Salvador-Martínez, I;
Zhang, H;
... Meyer, P; + view all
(2021)
Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees.
Cell Systems
10.1016/j.cels.2021.05.008.
(In press).
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.cels.2021.05.008 |
| Publisher version: | http://dx.doi.org/10.1016/j.cels.2021.05.008 |
| Language: | English |
| Additional information: | © 2021 Published by Elsevier Ltd. This is an open access article under the CC BY 4.0 license Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/) |
| Keywords: | C. elegans, CRISPR, M. musculus, benchmarking, cell lineage tracing, crowdsourcing, intmemoir, lineage reconstruction, machine learning, simulation |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10130224 |
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