De novo homology assessment from landmark data: A workflow to identify and track segmented structures in plant time series images

Abstract

Assessing the phenotypes underlying plant growth and development is integral to exploring the development, genetics, and evolution of morphology and plays an essential role in agronomic and basic research studies. Although various automated or semi-automated phenomic approaches have recently been developed, tools assessing differential growth of plant organs remains a key topic of interest, but one which is often difficult to analyze due to the requirements of segmenting and annotating specific structures or positions in the plant body in time-series data. To address this gap, we have developed a generalized workflow linking our previously published function, acute, with a companion function, homology, in the PlantCV environment. The homology function uses a generalized strategy of dimensionality reduction via starscape followed by hierarchical clustering through constella to identify constellations of segments in eigenspace that represent the same landmark in consecutive images of a time-series. We devised a quality control function, constellaQC, that can test the accuracy of the clustering approach, and we use it to show that the approach accurately clustered the pseudo-landmarks derived from acute, although with several sources of error. We discuss the reasons for and consequences of these errors in automated workflows, and suggest how to develop these functions so that they can easily be repurposed for other phenomics datasets that may vary in dimensional complexity.