Kevin R. Moon

This paper shows PHATE can better capture continuous population structure in genomic data than PCA or UMAP by tuning scale and balancing populations.

The paper introduces RF-PHATE, a supervised data visualization method that integrates expert knowledge to enhance interpretation of complex biological data.

The paper uses diffusion condensation to analyze the C. elegans nerve ring neuropil, revealing its stratified structure and developmental principles.

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.

MAGIC improves single-cell RNA-seq data by imputing missing values and revealing gene-gene interactions through a diffusion-based method.