Developmental normalization of phenomics data generated by high throughput plant phenotyping systems

Diego Lozano-Claros,Guang Deng,Xiangxiang Meng,Eddie Custovic,Mathew G Lewsey,Oliver Berkowitz,James Whelan

doi:10.1186/s13007-020-00653-x

Abstract

BackgroundSowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number.ResultsThe DAPD method improved the accuracy of phenotyping measurements by decreasing the statistical dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset.ConclusionsDAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.

Highlights

Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experi‐ ments in high throughput plant phenotyping (HTPP) systems
We developed Digital Adjustment of Plant Development (DAPD), which synchronizes shoot phenotypic measurements of multiple Arabidopsis plants by normalizing time-series measurements to a reference time point
We propose the DAPD method to control for temporal differences in development within plant phenotyping datasets

Summary

Introduction

Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experi‐ ments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. We developed the Digital Adjustment of Plant Development (DAPD) normalization method It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. Time-series normalization methods can numerically fit the time-series measurements to a single timeline and reduce dispersion These methods do not take into consideration developmental information nor the effect of the allometric scaling of growth; individual seedlings can have similar trait values but may be at different developmental stages. To do so requires the integration of methods that quantitatively measure growth and developmental traits in time-series for hundreds of plants, detect defined developmental stages, and shift the time-series data for each plant independently such that developmental stages align across all plants without distortion of the underlying growth trends

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Conclusion