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Branch-recombinant Gaussian processes for analysis of perturbations in biological time series.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Penfold, Christopher A 
Sybirna, Anastasiya  ORCID logo  https://orcid.org/0000-0002-8665-7112
Reid, John E 
Huang, Yun 
Wernisch, Lorenz 

Abstract

MOTIVATION: A common class of behaviour encountered in the biological sciences involves branching and recombination. During branching, a statistical process bifurcates resulting in two or more potentially correlated processes that may undergo further branching; the contrary is true during recombination, where two or more statistical processes converge. A key objective is to identify the time of this bifurcation (branch or recombination time) from time series measurements, e.g. by comparing a control time series with perturbed time series. Gaussian processes (GPs) represent an ideal framework for such analysis, allowing for nonlinear regression that includes a rigorous treatment of uncertainty. Currently, however, GP models only exist for two-branch systems. Here, we highlight how arbitrarily complex branching processes can be built using the correct composition of covariance functions within a GP framework, thus outlining a general framework for the treatment of branching and recombination in the form of branch-recombinant Gaussian processes (B-RGPs). RESULTS: We first benchmark the performance of B-RGPs compared to a variety of existing regression approaches, and demonstrate robustness to model misspecification. B-RGPs are then used to investigate the branching patterns of Arabidopsis thaliana gene expression following inoculation with the hemibotrophic bacteria, Pseudomonas syringae DC3000, and a disarmed mutant strain, hrpA. By grouping genes according to the number of branches, we could naturally separate out genes involved in basal immune response from those subverted by the virulent strain, and show enrichment for targets of pathogen protein effectors. Finally, we identify two early branching genes WRKY11 and WRKY17, and show that genes that branched at similar times to WRKY11/17 were enriched for W-box binding motifs, and overrepresented for genes differentially expressed in WRKY11/17 knockouts, suggesting that branch time could be used for identifying direct and indirect binding targets of key transcription factors. AVAILABILITY AND IMPLEMENTATION: https://github.com/cap76/BranchingGPs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Description

Keywords

Arabidopsis, Arabidopsis Proteins, Computational Biology, Pseudomonas syringae, Transcription Factors

Journal Title

Bioinformatics

Conference Name

Journal ISSN

1367-4803
1367-4811

Volume Title

34

Publisher

Oxford University Press (OUP)
Sponsorship
MRC (unknown)
Engineering and Physical Sciences Research Council (EP/I036575/1)
Alan Turing Institute (AT/I00009/16)
Wellcome Trust (083089/Z/07/Z)
Biotechnology and Biological Sciences Research Council (BB/L014130/1)