cellmig

cellmig implements Bayesian hierarchical models for uncertainty-aware quantitative analysis of high-throughput live cell migration data.

Key Features:

• Hierarchical Data Modeling: Models nested data structures where cells are nested within technical replicates that are nested within biological replicates.
• Condition-Specific Velocity Quantification: Quantifies condition-specific velocity changes such as drug effects while accounting for technical artifacts.
• Uncertainty Quantification: Explicitly quantifies uncertainty arising from technical or biological variability.
• Synthetic Data Generation: Enables synthetic data generation for experimental design optimization.

Scientific Applications:

• High-Throughput Cell Migration Analysis: Analyzes cell migration across many wells treated under different biological conditions from high-throughput cell imaging workflows.
• Drug Effect Characterization: Quantifies drug effects on cell migration velocity in the presence of technical noise and biological variability.
• Experimental Design Optimization: Supports optimization of experimental design through synthetic data generation prior to conducting experiments.

Methodology:

Bayesian hierarchical models implemented as an R package.