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BGP: Branched Gaussian processes for identifying gene-specific branching dynamics in single cell data

View ORCID ProfileAlexis Boukouvalas, View ORCID ProfileJames Hensman, View ORCID ProfileMagnus Rattray
doi: https://doi.org/10.1101/166868
Alexis Boukouvalas
University of Manchester;
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  • For correspondence: alexis.boukouvalas@gmail.com
James Hensman
prowler.io
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Magnus Rattray
University of Manchester;
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Abstract

High-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through use of pseudotime methods. We develop the branching Gaussian process (BGP), a non-parametric model that is able to identify branching dynamics for individual genes and provides an estimate of branching times for each gene with an associated credible region. We demonstrate the effectiveness of our method on both synthetic data and a published single-cell gene expression hematopoiesis study. The method requires prior information about pseudotime and global cellular branching for each cell but the probabilistic nature of the method means that it is robust to errors in these global branch labels and can be used to discover early branching genes which diverge before the inferred global cell branching. The code is open-source and available at https://github.com/ManchesterBioinference/BranchedGP .

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted August 01, 2017.
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BGP: Branched Gaussian processes for identifying gene-specific branching dynamics in single cell data
Alexis Boukouvalas, James Hensman, Magnus Rattray
bioRxiv 166868; doi: https://doi.org/10.1101/166868
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BGP: Branched Gaussian processes for identifying gene-specific branching dynamics in single cell data
Alexis Boukouvalas, James Hensman, Magnus Rattray
bioRxiv 166868; doi: https://doi.org/10.1101/166868

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