Michael A. Kalwat

Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using DEGAS Spatial Smoothing

The paper introduces DEGAS, a method to identify high-risk cells in spatial transcriptomics data by transferring disease attributes to individual cells, validated in cancer and diabetes datasets.

Unique and conserved endoplasmic reticulum stress responses in neuroendocrine cells

The paper identifies unique and shared transcriptional responses to ER stress in various endocrine cell types using mRNA sequencing.

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning

This study uses deep transfer learning to identify diabetes- and obesity-associated β-cell subpopulations in single-cell RNA-seq data and validates key genes through immunostaining.

Target deconvolution of an insulin hypersecretion-inducer acting through VDAC1 with a distinct transcriptomic signature in beta-cells

This paper identifies VDAC1 as the target of SW016789, a hypersecretion inducer, and shows it causes distinct transcriptomic changes in beta-cells compared to ER stress.