BRACE: A novel Bayesian-based imputation approach for dimension reduction analysis of alternative splicing at single-cell resolution

Abstract

Bayesian approach is a powerful tool to solve challenging questions in life sciences. One such area of life sciences in which Bayesian approach has seen an increased utility in the recent years is single-cell biology. Alternative splicing represents an additional layer of complexity underlying gene expression profile that has the potential to reveal insights into the biological mechanisms underpinning heath and disease states. Dimension reduction analysis is the cornerstone of RNA-sequencing analysis and has the ability to guide selection of candidate biomarkers based on segregation of sample groups. Nevertheless, dimension reduction analysis at single- cell resolution remains a significant challenge for alternative splicing datasets, and therefore hitherto preclude the assessment of candidate isoforms. Here, we introduce BRACE (a Bayesian-based imputation approach for dimension Reduction Analysis of alternative splicing at single-CEll resolution). We demonstrated our Bayesian approach represents an improvement over existing methods for imputing missing percent spliced-in values, and subsequently applied our approach for the dimension reduction analysis of alternative splicing events at single-cell resolution. We further demonstrated the application of our Bayesian approach over a range of single-cell datasets with increasing complexity, namely cell populations that are transcriptomically distinct, similar, and heterogenous. We anticipate our approach to enable assessment and selection of cell state- or disease-specific biomarkers for downstream experimental validation.