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GPfates specifications


Unique identifier OMICS_16720
Name GPfates
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
License MIT License
Computer skills Advanced
Version 1.0.0
Stability Stable
Numpy, Pandas, tqdm
Maintained Yes



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  • person_outline Sarah Teichmann <>

Publication for GPfates

GPfates in publication

PMCID: 5575496
PMID: 28524227
DOI: 10.1002/1873-3468.12684

[…] between cells using a random‐walk‐based distance., more principled model based approaches have been presented with scuba, which considers transition of cells clusters over time . as well as with gpfates/omgp , where multiple smooth trajectories are explicitly modelled. after inference, each cell gets assigned a posterior probability of having been sampled from a particular trajectory. […]

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GPfates institution(s)
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK; QIMR Berghofer Medical Research Institute, Herston, Brisbane, Queensland, Australia; Department of Microbiology and Immunology, Peter Doherty Institute, University of Melbourne, Parkville, Victoria, Australia; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK; National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, UK; Department of Computer Science, University of Sheffield, Sheffield, UK; Australian Research Council Centre of Excellence in Advanced Molecular Imaging, University of Melbourne, Parkville, Victoria, Australia
GPfates funding source(s)
This work was supported by Wellcome Trust (no. WT098051), European Research Council grant ThSWITCH (no. 260507), Australian National Health and Medical Research Council Project grant (number 1028641), a Career Development Fellowship (no. 1028643), University of Queensland, Australian Infectious Diseases Research Centre grants, the Lister Institute for Preventive Medicine, European Molecular Biology Laboratory Australia and OzEMalaR, the Lundbeck Foundation, and the Marie Curie Initial Training Networks grant “Machine Learning for Personalized Medicine” (EU FP7-PEOPLE Project Ref 316861, MLPM2012).

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