Bayesian approach for analysis of time-to-event data in plant biology.

Abstract

Background 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. Results and discussion 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? Conclusion 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.

Plant phenotyping relevance

植物の発芽・生育などの時系列表現型データを解析するベイズ計算法を中心に開発しており、植物フェノタイピング手法の方法開発に該当する。

Code and data availability

The paper's maize seedling-emergence time-to-event data and Bayesian analysis are accessible via the authors' free public web application at bayes4plants.com, which hosts the simulation software and sample data from the study. The full analysis code is stated to be 'freely available here', but no explicit public URL/re