BRIE

BRIE quantifies splicing isoform proportions from single-cell RNA-seq data using a Bayesian hierarchical model with sequence-informed priors.

Key Features:

• Bayesian hierarchical model: Implements a Bayesian framework to perform statistical inference of isoform proportions across individual cells.
• Sequence-informed informative prior: Learns an informative prior distribution from sequence features to regularize estimates in the presence of scRNA-seq noise.
• Exon inclusion ratio estimation: Produces reproducible estimates of exon inclusion ratios (isoform proportions) at single-cell resolution.
• Differential isoform quantification: Enables quantification and comparison of isoform expression differences between single-cell RNA-seq datasets.

Scientific Applications:

• Single-cell splicing variability analysis: Characterizes stochasticity and variability in RNA splicing events across individual cells using scRNA-seq data.
• Differential isoform expression studies: Detects and quantifies differences in isoform proportions between experimental conditions or datasets.
• Developmental biology case study: Has been applied to analyze splicing during mouse gastrulation (example: analysis of 130 cells).

Methodology:

Uses a Bayesian hierarchical model that learns an informative prior from sequence features to estimate exon inclusion ratios per cell and supports differential isoform quantification.