RT Journal Article
T1 A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data
A1 Pérez Valencia, Diana M.
A1 Rodriguez Alvarez, Maria Jose
A1 Boer, Martin P.
A1 Kronenberg, Lukas
A1 Hund, Andreas
A1 Cabrera Bosquet, Llorenç
A1 Millet, Emilie J.
A1 Eeuwijk, Fred A. van
K1 2417.14 Genética Vegetal
K1 1209.03 Análisis de Datos
AB High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.
PB Scientific Reports
SN 20452322
YR 2022
FD 2022-02-24
LK http://hdl.handle.net/11093/4518
UL http://hdl.handle.net/11093/4518
LA eng
NO Scientific Reports, 12(1): 3177 (2022)
NO Ministerio de Ciencia, Innovación y Universidades | Ref. BCAM Severo Ochoa accreditation SEV-2017-0718
DS Investigo
RD 16-sep-2024