Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell...

Area of Science:

• Molecular Biology
• Computational Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) generates large, complex datasets crucial for understanding cell diversity and function.
• Current scRNA-seq analyses often rely on noise removal and dimensionality reduction, potentially obscuring biological insights.
• Existing methods may struggle to fully leverage the inherent stochasticity and multimodal nature of scRNA-seq data.

Purpose of the Study:

• To propose a novel physical approach for scRNA-seq data analysis that leverages data stochasticity and multimodality.
• To develop a framework for integrating nascent and mature RNA counts using biophysical models.
• To reveal underlying regulatory processes and transcriptional modulation not detectable by conventional methods.

Main Methods:

• Utilized a Python package named Monod for data analysis.
• Applied biophysical models to integrate nascent and mature RNA counts from scRNA-seq data.
• Leveraged variations across RNA modalities to identify transcriptional changes.

Main Results:

• Demonstrated the meaningful integration of nascent and mature RNA counts.
• Identified transcriptional modulation undetectable through average gene expression changes.
• Enabled quantitative comparison of gene regulation hypotheses.
• Facilitated analysis of data from different scRNA-seq technologies within a unified framework.
• Reduced reliance on complex normalization and transformation techniques.

Conclusions:

• The proposed physical approach offers a powerful alternative for scRNA-seq data analysis.
• Integrating nascent and mature RNA provides deeper insights into transcriptional regulation.
• The Monod package enables robust analysis, minimizing data distortion and enhancing biological discovery.