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A comparison of single-cell trajectory inference methods: towards more accurate and robust tools
View ORCID ProfileWouter Saelens, View ORCID ProfileRobrecht Cannoodt, View ORCID ProfileHelena Todorov, View ORCID ProfileYvan Saeys
doi: https://doi.org/10.1101/276907
Wouter Saelens
Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Robrecht Cannoodt
Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium.
Helena Todorov
Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.Centre International de Recherche en Infectiologie, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon, F-69007, Lyon, France
Yvan Saeys
Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.

Article usage
Posted March 05, 2018.
A comparison of single-cell trajectory inference methods: towards more accurate and robust tools
Wouter Saelens, Robrecht Cannoodt, Helena Todorov, Yvan Saeys
bioRxiv 276907; doi: https://doi.org/10.1101/276907
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