Mark A. Anderson

The paper introduces Vespucci, a machine-learning method for identifying perturbation-responsive regions and genes in spatial transcriptomics data, showing it outperforms existing methods.

This study identifies neuronal architectures responsible for autonomic dysreflexia after spinal cord injury and demonstrates how epidural stimulation can counteract it in mice, rats, and humans.

The paper introduces the Tabulae Paralytica, a multimodal atlas of spinal cord injury, revealing biological principles and developing a gene therapy to restore walking in old mice.

This study evaluates differential expression methods in single-cell transcriptomics, finding that many produce false discoveries due to ignoring biological variation.

The paper introduces a machine-learning method, Augur, to identify cell types most responsive to biological perturbations in single-cell data and validates it on multiple datasets.