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

Soumyashree Kar1, Vincent Garin2, Jana Kholová2

  • 1Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India.

Frontiers in Plant Science
|December 17, 2020
PubMed
Summary

This study introduces a new analytical pipeline to process noisy data from high-throughput phenotyping (HTP) platforms. The pipeline accurately estimates plant genotypic performance over time, supporting breeding decisions.

Keywords:
HTP-pipelineSpATSchange point analysiscross-validationhigh-throughput phenotypingsimulation

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Area of Science:

  • Plant science
  • Agricultural technology
  • Data science

Background:

  • High-throughput phenotyping (HTP) platforms generate vast amounts of plant data.
  • Analyzing this data to understand genotypic variation over time presents significant challenges, especially with noisy or missing measurements from outdoor HTP systems.

Purpose of the Study:

  • To develop and validate an analytical pipeline for accurate estimation of genotypic plant performance from noisy HTP data.
  • To improve the extraction of meaningful biological information from field-based phenotyping experiments.

Main Methods:

  • A three-module pipeline was developed: outlier detection, missing value imputation, and mixed-model genotype-adjusted means with spatial adjustment.
  • The pipeline was tested on chickpea and sorghum data for traits like leaf area and plant height across two seasons.
  • Change-point analysis was used to identify growth phases and optimal times for genotype comparison.

Main Results:

  • The pipeline effectively smooths genotype growth curves from noisy HTP data, handling up to 50% missing values and 20-30% data contamination.
  • It accurately identifies growth phases and genotype clusters based on performance during optimal growth periods.
  • The method provides high-quality plant growth time-series data beneficial for breeding programs.

Conclusions:

  • The developed analytical pipeline significantly enhances the ability to extract valuable genotypic information from challenging outdoor HTP datasets.
  • This approach supports more informed breeding decisions by providing reliable plant growth time-series data.
  • The R code is publicly available, promoting wider adoption in plant science research.