A Bayesian mixture model for the analysis of allelic expression in single cells

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Allele-specific expression (ASE) at single-cell resolution is crucial for understanding gene expression dynamics.
• Challenges in ASE analysis include low read coverage and high biological variability.
• Discarding multi-mapping reads can lead to inaccurate ASE findings.

Purpose of the Study:

• To develop a robust method for ASE analysis in single-cell RNA-Seq data.
• To improve the accuracy of allelic proportion estimation.
• To reduce sampling variability without losing cell-specific heterogeneity.

Main Methods:

• Developed a novel method for ASE analysis from single-cell RNA-Seq data.
• Utilized a hierarchical mixture model to pool information across cells.
• Applied the method to re-evaluate allelic bursting and track ASE patterns during development.

Main Results:

• The proposed method accurately classifies allelic expression states.
• Pooling information across cells improves allelic proportion estimation.
• The approach reduces sampling variability while maintaining cell-to-cell heterogeneity.

Conclusions:

• The developed method offers a more accurate approach to single-cell ASE analysis.
• This method enhances the understanding of gene expression stochasticity and dynamics.
• It provides a valuable tool for developmental biology and other fields studying gene regulation.