The Problem

Plant ionomics — the quantitative analysis of elemental composition in plant tissues — generates complex multivariate datasets requiring sophisticated statistical treatment. Manual analysis is time-consuming, error-prone, and often lacks consistency across studies. Researchers in ionomics need automated, reproducible workflows that apply appropriate statistical methods and generate publication-ready results.

The Approach

Ionomica is an automated computational workflow that processes raw ionomics data through a comprehensive analytical pipeline:

• Data validation and quality control
• Statistical modeling (mixed models, generalized additive models, multivariate analysis, machine learning)
• Automated visualization generation
• Reproducible research documentation

The system implements best practices from agronomic research and statistical analysis, ensuring methodological rigor while dramatically reducing analysis time.

Technical Implementation

• Python-based statistical pipeline
• Automated machine learning model selection and hyperparameter tuning
• User interface and automated reporting with Marimo

Current Status

Currently used in agricultural research projects. Ongoing development focuses on expanding statistical methods and improving integration with additional data sources.