BayVel: A Bayesian Framework for RNA Velocity Estimation in Single-Cell Transcriptomics

TL;DR

BayVel introduces a Bayesian hierarchical model for RNA velocity estimation in single-cell transcriptomics, directly modeling raw data to improve accuracy, uncertainty quantification, and address limitations of existing methods like scVelo.

Contribution

BayVel is the first Bayesian framework that models raw count data for RNA velocity, resolving identifiability issues and providing uncertainty estimates without post-processing.

Findings

BayVel outperforms scVelo in simulated data accuracy.

BayVel provides reliable posterior distributions for parameters.

Application to real data shows differences from scVelo, questioning previous findings.

Abstract

RNA velocity is a model of gene expression dynamics designed to analyze single-cell RNA sequencing (scRNA-seq) data, and it has recently gained significant attention. However, despite its popularity, the model has raised several concerns, primarily related to three issues: its heavy dependence on data preprocessing, the need for post-processing of the results, and the limitations of the underlying statistical methodology. Current approaches, such as scVelo, suffer from notable statistical shortcomings. These include identifiability problems, reliance on heuristic preprocessing steps, and the absence of uncertainty quantification. To address these limitations, we propose BayVel, a Bayesian hierarchical model that directly models raw count data. BayVel resolves identifiability issues and provides posterior distributions for all parameters, including the RNA velocities themselves, without the need for any post processing. We evaluate BayVel's performance using simulated datasets. While scVelo fails to accurately reconstruct parameters, even when data are simulated directly from the model assumptions, BayVel demonstrates strong accuracy and robustness. This highlights BayVel as a statistically rigorous and reliable framework for studying transcriptional dynamics in the context of RNA velocity modeling. When applied to a real dataset of pancreatic epithelial cells previously analyzed with scVelo, BayVel does not replicate their findings, which appears to be strongly influenced by the postprocessing, supporting concerns raised in other studies about the reliability of scVelo.