Humplík Jan F, Dostál Jakub, Ugena Lydia, Spíchal Lukáš, De Diego Nuria, Vencálek Ondřej, Fürst Tomáš
1Department of Chemical Biology and Genetics, Centre of the Region Haná for Biotechnological and Agricultural Research, Faculty of Science, Palacký University, Šlechtitelů 27, 78371 Olomouc, Czech Republic.
2Department of Mathematical Analysis and Application of Mathematics, Faculty of Science, Palacký University, 17. listopadu 12, 77900 Olomouc, Czech Republic.
Plant Methods. 2020 Feb 11;16:14. doi: 10.1186/s13007-020-0554-1. eCollection 2020.
Plants, like all living organisms, metamorphose their bodies during their lifetime. All the developmental and growth events in a plant's life are connected to specific points in time, be it seed germination, seedling emergence, the appearance of the first leaf, heading, flowering, fruit ripening, wilting, or death. The onset of automated phenotyping methods has brought an explosion of such time-to-event data. Unfortunately, it has not been matched by an explosion of adequate data analysis methods.
In this paper, we introduce the Bayesian approach towards time-to-event data in plant biology. As a model example, we use seedling emergence data of maize under control and stress conditions but the Bayesian approach is suitable for any time-to-event data (see the examples above). In the proposed framework, we are able to answer key questions regarding plant emergence such as these: (1) Do seedlings treated with compound A emerge earlier than the control seedlings? (2) What is the probability of compound A increasing seedling emergence by at least 5 percent?
Proper data analysis is a fundamental task of general interest in life sciences. Here, we present a novel method for the analysis of time-to-event data which is applicable to many plant developmental parameters measured in field or in laboratory conditions. In contrast to recent and classical approaches, our Bayesian computational method properly handles uncertainty in time-to-event data and it is capable to reliably answer questions that are difficult to address by classical methods.
植物与所有生物一样，在其生命周期中会发生身体形态的变化。植物生命中的所有发育和生长事件都与特定的时间点相关联，无论是种子萌发、幼苗出土、第一片叶子出现、抽穗、开花、果实成熟、枯萎还是死亡。自动化表型分析方法的出现带来了此类事件发生时间数据的激增。不幸的是，相应的充分数据分析方法却没有同步激增。
在本文中，我们介绍了植物生物学中针对事件发生时间数据的贝叶斯方法。作为一个模型示例，我们使用了玉米在对照和胁迫条件下的幼苗出土数据，但贝叶斯方法适用于任何事件发生时间数据（见上述示例）。在所提出的框架中，我们能够回答有关植物出土的关键问题，例如：（1）用化合物A处理的幼苗是否比对照幼苗出土更早？（2）化合物A使幼苗出土率至少提高5%的概率是多少？
恰当的数据分析是生命科学中普遍关注的一项基本任务。在此，我们提出了一种分析事件发生时间数据的新方法，该方法适用于在田间或实验室条件下测量的许多植物发育参数。与近期和经典方法不同，我们的贝叶斯计算方法能够妥善处理事件发生时间数据中的不确定性，并且能够可靠地回答经典方法难以解决的问题。
