Timothy Hamilton

A novel metric reveals previously unrecognized distortion in dimensionality reduction of scRNA-Seq data

Comparing local point neighborhoods before and after dimensionality reduction reveals that standard scRNA-seq pipelines, t-SNE, and UMAP often distort local neighborhoods by more than 95%, altering downstream clustering.

DeepAtlas: a tool for effective manifold learning

DeepAtlas generates local manifold embeddings and assesses the manifold hypothesis, finding that many datasets, including single-cell RNA-sequencing, do not conform to it.

Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing

The paper introduces a binomial model to identify biologically variable genes in scRNA-seq data, showing it outperforms existing methods in preserving biological structure.

Novel metrics reveal new structure and unappreciated heterogeneity in C. elegans development

This paper introduces a new metric called branch distance to analyze developmental phenotypes in C. elegans embryos, revealing previously unnoticed heterogeneity and links between cell fate and cell cycle pathways.

A lack of distinct cell identities in single-cell measurements: revisiting Waddingtons landscape

This paper challenges the idea that distinct cell types correspond to attractors in gene expression space by showing cells of different types occupy the same regions in epigenetic space.