PT  - JOURNAL ARTICLE
AU  - Christopher A. Penfold
AU  - Anastasiya Sybirna
AU  - John Reid
AU  - Yun Huang
AU  - Lorenz Wernisch
AU  - Zoubin Ghahramani
AU  - Murray Grant
AU  - M. Azim Surani
TI  - Branch-recombinant Gaussian processes for analysis of perturbations in biological time series
AID  - 10.1101/291757
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 291757
4099  - http://biorxiv.org/content/early/2018/03/29/291757.short
4100  - http://biorxiv.org/content/early/2018/03/29/291757.full
AB  - 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 under-go further branching; the contrary is true during recombination, where two or more statistical processes converge into one. A key objective is to identify the time of this bifurcation (branch time) from time series measurements e.g., comparing a control time series with a perturbed time series. Whilst statistical treatments for the two branch (control versus treatment) case exists, the ability to infer more complex branching structure from time series data remains open. Gaussian processes (GPs) represents 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). We first demonstrate 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 showed that groups of 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. Software is available from: https://github.com/cap76/BranchingGPs.