Cellular Heterogeneity

AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity

AAnet is a neural network for nonlinear archetypal analysis of single-cell data.

A Venkat*, S Youlten*,…,S Krishnaswamy, CL Chaffer. Cancer Discovery In Revision.

AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity

Beta cells are essential drivers of pancreatic ductal adenocarcinoma development

Disentangling the origin of β cells misexpressing pro-tumorigenic hormones in the context of obesity and other stressors.

C Garcia*, A Venkat*, DC McQuaid*,…,S Krishnaswamy, MD Muzumdar. In Revision.

Graph Fourier MMD for Signals on Graphs

GFMMD embeds signals defined on graphs via an optimal witness function.

S Leone, A Tong, G Huguet, A Venkat, G Wolf, S Krishnaswamy. SampTA 2023.

Graph Fourier MMD for Signals on Graphs

Mapping the gene space at single-cell resolution with gene signal pattern analysis

GSPA learns gene-gene relationships from single-cell data by embedding the gene space.

A Venkat, S Leone, S Youlten, E Fagerberg, J Attanasio, NS Joshi, S Krishnaswamy. Nature Computational Science 2024.

Mapping the gene space at single-cell resolution with gene signal pattern analysis

Multiscale geometric and topological analyses for characterizing and predicting immune responses from single cell data

This review describes approaches to characterize immune heterogeneity at multiple resolutions.

A Venkat, D Bhaskar, S Krishnaswamy. Cell Trends in Immunology.

Multiscale geometric and topological analyses for characterizing and predicting immune responses from single cell data

PD-1 maintains CD8 T cell tolerance towards cutaneous neoantigens

PD-1 avoids immunopathology by preventing CD8 T cells to attain fully functional state.

M Damo, N Hornick, A Venkat,…,S Krishnaswamy, N Joshi. Nature 2023.

PD-1 maintains CD8 T cell tolerance towards cutaneous neoantigens