Aaron Watters

The paper introduces a dataset and method for nuclear segmentation and tracking in mouse embryos, identifying Stardist-3D as the most accurate method for this task.

The paper introduces Inferelator 3.0, a tool for inferring gene regulatory networks from single-cell data, showing improved performance on yeast and mouse brain datasets.

The paper identifies new therapeutic targets in CRLF2-overexpressing B-ALL by analyzing TF-gene regulatory interactions using network inference and experimental validation.

The paper compares correlation/proportionality methods with MMvec for identifying microbe-metabolite interactions and finds MMvec performs better on real data due to its handling of sparsity.

Analysis of the ALS spinal cord using Spatial Transcriptomics reveals spatiotemporal dynamics of disease driven gene regulation.

The paper develops methods to infer transcriptional regulatory networks in Th17 cells using ATAC-seq and RNA-seq data, revealing new roles for transcription factors and their modules.