Interpretable Machine Learning to Model Drug Perturbations in Single Cell Genomics

Explore interpretable machine learning techniques for modeling drug perturbations in single-cell genomics in this 36-minute conference talk. Delve into approaches from deep representation learning used to identify the gene expression manifold and understand cellular variation. Learn about the compositional perturbation autoencoder (CPA), a deep autoencoder developed to describe the impact of drug or genetic modifications on cellular states. Discover how CPA enables interpretable modeling of perturbations, prediction of novel effects, and in-silico generation of putative interaction effects. Examine examples of CPA predicting dosage-specific drug effects and combinatorial genetic interactions. Gain insights into single-cell analysis for understanding cell fate, machine learning-based cell lineage estimation, and the application of generative neural networks in style transfer and domain adaptation for cellular studies.