SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data.

Soumyashree Kar,Vincent Vadez,Hiroyoshi Iwata,Vincent Garin,Milan O Urban,J Adinarayana,Surya S Durbha,Jana Kholová,Ryokei Tanaka

doi:10.3389/fpls.2020.552509

Abstract

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.

Highlights

During the last decade, progress in phenotyping methods have given ground for the development of many high-throughput phenotyping (HTP) platforms (Berger et al, 2010; Tisne et al, 2013; Cabrera Bosquet et al, 2015; Vadez et al, 2015), established across the globe to support rapid screening of plant phenotypes
Using real-data analyses and simulations, we evaluated the different components of SpaTemHTP to estimate their relative contribution in the quality of the temporal series of genotypic estimates
We evaluated the efficiency of the outlier detection, missing value imputation, and their combination by calculating the correlation between the reference genotype best linear unbiased estimates (G-BLUEs) and the G-BLUEs obtained with strategies S5–S9

Summary

Introduction

Progress in phenotyping methods have given ground for the development of many high-throughput phenotyping (HTP) platforms (Berger et al, 2010; Tisne et al, 2013; Cabrera Bosquet et al, 2015; Vadez et al, 2015), established across the globe to support rapid screening of plant phenotypes These platforms generate large-scale phenotypic datasets that are complex to handle, process, and interpret. An important aspect that contributes to the complexity of HTP data handling is the presence of exogenous effects, which primarily include systemgenerated noise and fluctuations in environmental conditions This is the case in HTP platforms characterizing phenotypes in open-environments or under non-controlled conditions like the LeasyScan, the PhenoField (Barker et al, 2016), or the Field scanalyzer (Virlet et al, 2017) platforms. Existing ones largely use image-based analysis of plant phenotypes (Hartmann et al, 2011; Artzet et al, 2019), and some are even platform or trait-specific (Galkovskyi et al, 2012; Faroq et al, 2013; Hasan et al, 2018)

Methods

Results

Conclusion