Stephen J. Fleming

The paper analyzes single-nucleus RNA-seq data from the human striatum and prefrontal cortex to study inter-individual variation in cell abundances and gene expression, finding age-related changes and genetic associations.

multiVIB integrates single-cell multi-omics data across modalities and species with a probabilistic contrastive learning framework, achieving state-of-the-art performance while preserving biological variation.

This study creates a single-nucleus and spatial transcriptomic atlas of the COVID-19 liver, revealing cellular and functional disruptions associated with organ damage.

TissueMosaic performs cross-sample differential analysis of spatial transcriptomics data using self-supervised representation learning to improve detection of genes associated with tissue structure changes.

This study maps the transcriptional landscape of the rat cardiovascular system at single-cell resolution, identifying 26 cell types and revealing tissue-specific molecular diversity and communication networks.

CellMincer is a self-supervised deep learning method for denoising voltage imaging data, achieving significant noise reduction and improved functional phenotype separation.

CellBender removes systematic background noise from droplet-based single-cell data using a deep generative model, improving alignment with established gene expression patterns.

This study uses deep learning to analyze aortic dimensions from MRI and identifies genetic loci associated with aortic diameter, highlighting SVIL as a key gene and developing a polygenic score for aneurysm risk.

This study creates a single-cell and spatial atlas of autopsy tissues from COVID-19 patients, revealing altered cellular responses and potential therapeutic targets.

The paper uses single-nucleus RNA sequencing to define the transcriptional and cellular diversity of the non-failing human heart, identifying 9 major cell types and 20 subclusters.