Scott Gigante

Quantifying the effect of experimental perturbations at single-cell resolution

This paper introduces a method to quantify perturbation effects at single-cell resolution using manifold-based likelihood estimation and vertex frequency clustering, achieving higher accuracy than existing methods.

Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases

A topological machine learning approach applied to single-nucleus RNA sequencing identifies shared glial activation states in AMD, AD, and MS.

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

The paper introduces Multiscale PHATE, a method for exploring high-dimensional biomedical data, and applies it to identify immune cell signatures associated with severe COVID-19 outcomes.

Visualizing Structure and Transitions for Biological Data Exploration

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.

Using long-read sequencing to detect imprinted DNA methylation

The paper uses long-read nanopore sequencing to detect imprinted DNA methylation and identifies known and novel differentially methylated regions.