SplicedVAE: Learning Splicing Ratios from scRNA-seq to Enhance RNA Velocity and Cellular Trajectories

Single-cell RNA sequencing (scRNA-seq) provides high-resolution snapshots of cellular states, yet inferring temporal dynamics such as RNA velocity and differentiation trajectories remains challenging. To address this, we present SplicedVAE, a supervised generative framework that augments the scVI variational autoencoder with a dedicated decoder for predicting per-gene splicing ratios only from raw counts. SplicedVAE jointly optimizes gene expression reconstruction and splicing-ratio prediction, enabling biologically informed regularization. Our model achieves improved splicing-ratio prediction accuracy (RMSE 0.1271, Pearson r = 0.67), enhanced latent representations, and substantially more coherent velocity fields compared to standard scVI. When reconstructed S/U counts are passed into scVelo, SplicedVAE recapitulates developmental flow patterns and yields high cosine similarity to ground-truth velocities. These results demonstrate multi-task learning can improve velocity-based trajectory reconstruction and establishes a foundation for future models capable of generating cellular trajectories.