Developmental Normalization of Phenomics Data Generated by High Throughput Plant Phenotyping Systems

Abstract

Abstract Background Sowing 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. Results The 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 A. thaliana 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.

Plant phenotyping relevance

植物表現型ハイスループット測定の時系列データを正規化するDAPD法を開発し、測定精度・分散低減を検証しており、方法が研究の中心である。

Code and data availability

The paper's DAPD normalization and segmentation analysis code is explicitly stated to be publicly available on the authors' GitHub repository, matching an allowed URL. No phenotype dataset or image deposit is stated; the in-house dataset is not publicly shared.