William S. Chen

This paper introduces PhEMD, a method to embed drug perturbation effects in single-cell experiments, revealing low-dimensional manifold structures and enabling efficient drug effect inference.

The paper introduces PHATE, a visualization method that captures nonlinear structure in biological data, showing it preserves patterns better than existing tools and reveals novel insights in scRNA-seq data.

SAUCIE is a deep neural network that processes large single-cell datasets, performs batch correction, and identifies cluster-based signatures in dengue patient T cells.