CI-VAE for Single-Cell: Leveraging Generative-AI to Enhance Disease Understanding

Understanding cellular disease processes like cancer is key for improving diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) enables modeling transitions between normal and diseased cellular states in complex tissues. However, interpolating between healthy and diseased states in high-dimensional scRNA-seq data poses computational challenges. We use the Class-Informed Variational Autoencoder (CI-VAE), a generative AI model, to learn low-dimensional cell-type-specific representations from scRNA-seq data. During inference, CI-VAE interpolates between normal and diseased cells, robustly predicting cell-type-specific gene expression trajectories from healthy to disease states. Applied to colon cancer data, CI-VAE closely predicted observed transitions by generating synthetic gene expression changes associated with cancer progression for each cell type, potentially offering insights into underlying molecular mechanisms for disease understanding, biomarker discovery, and targeted therapy design.