This course aims to teach learners how to model drug perturbations in single-cell genomics using interpretable machine learning methods. The course covers topics such as single-cell analysis for understanding cell fate, learning trajectories, cell lineage estimation, and predicting single-cell perturbation effects. The teaching method involves a combination of deep representation learning approaches and the introduction of the 'compositional perturbation autoencoder' model. The intended audience for this course includes individuals interested in computational biology, machine learning, and single-cell genomics looking to understand cellular variation and the impact of perturbations at a transcriptomic and epigenomic level.
Interpretable Machine Learning to Model Drug Perturbations in Single Cell Genomics
Overview
Syllabus
Intro
The power of many
Single cell analysis for understanding cell fate in health & disease
Learning trajectories: cell cycle from morphometry
single-cell transcriptomies analysis
Machine learning based cell lineage estimation
cells as basis for understanding health
style transfer & domain adaptation by generative neural networks
scGen: predicting single-cell perturbation effects using generative models
Aim: interpretable and scalable perturbation modeling
Compositional perturbation autoencoder: training
Learning & predicting combinatorial genetic perturbations
Taught by
Open Data Science
