Deep generative modeling platform for plant omics data and digital twins

Overview

This research proposes a specialized computational package tailored for plant physiology that adapts deep generative modeling techniques from single-cell omics to agricultural research. The core hypothesis is that the challenges of identifying how genes interact with environmental conditions to produce specific plant phenotypes are analogous to those in single-cell omics, meaning proven methodologies from that field can be adapted for plant biology.

The platform addresses critical challenges in plant omics data analysis, including dimensionality reduction, multi-modal data integration, and accounting for unwanted variation caused by differences between field sites, weather conditions, experimental protocols, and measurement techniques. By leveraging probabilistic models with neural network architectures, the package will enable researchers to better understand gene-environment-phenotype relationships while quantifying the uncertainty inherent in agricultural systems.

Technical specifications

Core capabilities:

• Dimensionality reduction across large plant omics datasets
• Multi-modal data integration for combining different omics layers
• Clustering and annotation of biological samples
• Differential expression analysis adapted for plant systems
• Uncertainty quantification and variability modeling
• Removal of unwanted variation from observed covariates

Technical approach:

• Built on deep generative models (DGMs), a class of probabilistic models using likelihood-based methods and neural networks
• Inspired by scvi-tools architecture from the Yosef Lab at UC Berkeley
• Python-based implementation with user-friendly interface
• Compatible with existing plant biology workflows
• Scalable stochastic inference for handling large agricultural datasets
• Support for complex study designs common in field experiments

Technology readiness level

The project is at an early conceptual stage. Initial work will focus on adapting existing single-cell omics methodologies to plant physiology contexts, followed by validation against real-world agricultural datasets. The development plan emphasizes defining preliminary datasets and milestones, then iteratively tuning the platform to practical plant research needs and field-study conditions.