Branch-recombinant Gaussian processes for analysis of perturbations in biological time series.
Branch-recombinant Gaussian processes for analysis of perturbations in biological time series.
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DOI：
10.1093/bioinformatics/bty603
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发表时间：
2018-09-01
期刊：
影响因子：
--
通讯作者：
Surani MA
中科院分区：
文献类型：
--
作者：
Penfold CA;Sybirna A;Reid JE;Huang Y;Wernisch L;Ghahramani Z;Grant M;Surani MA
在生物科学中遇到的一类常见行为涉及分支和重组。在分支过程中，统计过程发生分支，导致可能经历进一步分支的两个或更多潜在相关的过程；相反，在重组期间，两个或更多统计过程收敛。一个关键的目标是从时间序列测量中识别这种分叉的时间(分支或重组时间)，例如通过比较控制时间序列和扰动时间序列。高斯过程(GP)代表了此类分析的理想框架，允许进行包含对不确定性的严格处理的非线性回归。然而，目前，GP模型仅适用于双分支系统。在这里，我们强调了如何使用GP框架内的协方差函数的正确组合来构建任意复杂的分支过程，从而概述了以分支-重组高斯过程(B-RGPS)的形式处理分支和重组的一般框架。我们首先将B-RGPS的性能与现有的各种回归方法进行了比较，并展示了对模型错误指定的鲁棒性。然后利用B-RGPS研究了半营养细菌紫丁香假单胞菌DC3000和解除武装的突变体hrpA接种后拟南芥基因表达的分枝模式。通过按分支数目对基因进行分组，我们可以自然地将参与基础免疫反应的基因从毒力株颠覆的基因中分离出来，并显示对病原蛋白效应物靶标的丰富。最后，我们鉴定了两个早期分支基因WRKY11和WRKY17，并表明在与WRKY11/17相似的时间分支的基因富含W-box结合基序，并且在WRKY11/17敲除中差异表达的基因过度表达，这表明分支时间可以用于识别关键转录因子的直接和间接结合靶标。Https://github.com/cap76/BranchingGPs补充数据可在生物信息学在线上获得。
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). 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. https://github.com/cap76/BranchingGPs Supplementary data are available at Bioinformatics online.
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DOI：
10.1093/bioinformatics/btr064
发表时间：
2011-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Grant CE;Bailey TL;Noble WS
通讯作者：
Noble WS
影响因子：
46.9
作者：
Setty M;Tadmor MD;Reich-Zeliger S;Angel O;Salame TM;Kathail P;Choi K;Bendall S;Friedman N;Pe'er D
通讯作者：
Pe'er D
DOI：
10.1093/bioinformatics/btv267
发表时间：
2015-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Penfold CA;Millar JB;Wild DL
通讯作者：
Wild DL
影响因子：
14.9
作者：
Huala, E;Dickerman, AW;Rhee, SY
通讯作者：
Rhee, SY
DOI：
10.1126/science.1171647
发表时间：
2009-05-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Boller T;He SY
通讯作者：
He SY