StatFaRmer: cultivating insights with an advanced R shiny dashboard for digital phenotyping data analysis

Area of Science:

• Agricultural Science
• Plant Biology
• Computational Biology

Background:

• Digital phenotyping is rapidly advancing, requiring robust tools for analyzing complex plant trait data.
• Traditional phenotypic studies often face challenges with time-series data, timestamp variations, and outliers common in digital phenotyping.
• Efficient analysis of plant phenotypic parameters is crucial for understanding genotype-environment interactions and crop improvement.

Purpose of the Study:

• To develop and present StatFaRmer, a user-friendly, open-source tool for analyzing time-series plant phenotypic data.
• To provide a versatile platform that integrates seamlessly with common phenotypic study workflows and various data sources.
• To automate data preparation and enable customizable statistical analyses, including ANOVA, for digital phenotyping research.

Main Methods:

• StatFaRmer utilizes data from spreadsheets (XLSX, CSV) and is designed to manage variations in timestamps and outliers.
• The tool automates data preparation and offers well-documented, customizable ANOVA tests with diagnostics and significance estimation.
• It is implemented as an open-source Shiny dashboard, providing installation and operational instructions for Windows and Linux.

Main Results:

• StatFaRmer successfully handles large datasets and diverse experimental designs across multiple plant species, including wheat, corn, soybean, and sugar beet.
• The tool ensures reliable analysis of time-series phenotypic data, accommodating common challenges like timestamp variations and outliers.
• Automated data processing and customizable statistical tests streamline the analysis pipeline for phenotypic studies.

Conclusions:

• StatFaRmer offers a versatile and user-friendly solution for the analysis of digital plant phenotyping data.
• The tool's ability to handle complex datasets and provide reproducible analysis enhances its utility in plant science research.
• StatFaRmer supports efficient investigation of plant traits across various species and experimental conditions, contributing to advancements in crop science.