SpaTemHTP

"SpaTemHTP" is an analytical pipeline to address the challenges of processing and analyzing the vast amounts of data generated by high-throughput phenotyping (HTP) platforms, particularly in outdoor and field-based settings. With the rapid advancement of phenotyping technologies, accurately estimating the genotypic component of plant phenotype over time has become crucial and challenging, mainly when data inaccuracies, failures, and environmental factors substantially affect phenotype measurements.

Core Modules and Functionalities:

- Detection of Outliers: The first module in the SpaTemHTP pipeline focuses on identifying and handling outliers in the dataset, ensuring that anomalous data points do not skew subsequent analyses.

- Imputation of Missing Values: Recognizing the common issue of missing data in outdoor HTP platform measurements, this module efficiently imputes missing values, allowing for a more complete and accurate analysis of the plant growth data.

- Mixed-Model Genotype Adjusted Means Computation with Spatial Adjustment: The final module computes mixed-model genotype adjusted means, incorporating spatial adjustment to account for the environmental and positional effects on phenotype expression. This step is crucial for accurately estimating genotypic values and growth curves.

- Smooth Genotype Growth Curves Estimation: By sequentially applying these three steps, SpaTemHTP is particularly effective in estimating smooth genotype growth curves from raw data, even when such data contain significant noise.

- Change-Point Analysis and Growth Phase Modeling: A pipeline extension includes modeling the genotype time series data and performing change-point analysis to identify growth phases and the optimal timing for distinguishing genotypic differences.

- Genotype Clustering and ANOVA: The estimated genotypic values during the optimal growth phase are used to cluster genotypes, with two-way ANOVA confirming the consistency of these clusters throughout the growth duration.