BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process

Genome Biology
Alexis BoukouvalasMagnus Rattray

Abstract

High-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through pseudotime methods. We develop the branching Gaussian process (BGP), a non-parametric model that is able to identify branching dynamics for individual genes and provide an estimate of branching times for each gene with an associated credible region. We demonstrate the effectiveness of our method on simulated data, a single-cell RNA-seq haematopoiesis study and mouse embryonic stem cells generated using droplet barcoding. The method is robust to high levels of technical variation and dropout, which are common in single-cell data.

References

Mar 29, 2006·IEEE Transactions on Pattern Analysis and Machine Intelligence·Shihao JiLawrence Carin
May 3, 2016·Nature Biotechnology·Manu SettyDana Pe'er
Jun 12, 2016·Bioinformatics·Jing YangMagnus Rattray
Jun 19, 2016·Bioinformatics·John E Reid, Lorenz Wernisch
Aug 30, 2016·Nature Methods·Laleh HaghverdiFabian J Theis
Jan 17, 2017·Nature Communications·Grace X Y ZhengJason H Bielas
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Sep 14, 2017·Genome Biology·Luke ZappiaAlicia Oshlack
Feb 8, 2018·Genome Biology·F Alexander WolfFabian J Theis

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Citations

Feb 8, 2019·Biophysics Reviews·Christopher Yau, Kieran Campbell
Mar 7, 2020·Nature Communications·Koen Van den BergeLieven Clement
Aug 2, 2020·Scientific Reports·Thomas G MinchingtonNancy Papalopulu
Jan 16, 2021·Science·T StadlerM P H Stumpf
Jul 1, 2021·Communications Biology·Siqi FangOliver M Crook
Jul 3, 2021·Bioinformatics·Nuha BinTayyashMagnus Rattray
Dec 8, 2021·Proceedings of the National Academy of Sciences of the United States of America·Chen Qiao, Yuanhua Huang

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Methods Mentioned

BETA
RNA-seq

Software Mentioned

GPflow
BEAM
Monocle
DDRTree
MFA
ScanPy
OMGP

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