DifferentialRegulation

DifferentialRegulation detects differentially regulated genes between two groups of samples by targeting differences in the balance of spliced and unspliced mRNA abundances from single-cell RNA-sequencing (scRNA-seq) or bulk RNA-sequencing data.

Key Features:

• Bayesian hierarchical modeling: Embeds multiple samples in a Bayesian hierarchical model that accounts for sample-to-sample variability.
• Quantification uncertainty handling: Treats ambiguous reads separately from spliced and unspliced reads, and allocates reads compatible with multiple genes to their gene of origin via a latent variable approach.
• Splice version allocation: Allocates reads to their gene/transcript of origin and to the respective splice version.
• MCMC parameter inference: Infers parameters via Markov chain Monte Carlo techniques using Metropolis-within-Gibbs.
• Data flexibility: Works with both single-cell and bulk RNA-sequencing data.

Scientific Applications:

• Differential regulation detection: Identifies changes between experimental conditions with respect to the relative abundance of unspliced mRNA over total mRNA.
• Comparative gene regulation studies: Enables comparisons between groups of samples such as healthy versus diseased, or treated versus untreated samples.
• Gene expression production analysis: Supports joint investigation of spliced and unspliced mRNA to study gene regulation and changes in gene expression production.

Methodology:

DifferentialRegulation uses a Bayesian hierarchical model with a latent variable approach where reads are allocated to their gene/transcript of origin and to the respective splice version. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques using Metropolis-within-Gibbs.