CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.

Exploratory analysis of single-cell RNA sequencing (scRNA-seq) typically relies on hard clustering over two-dimensional projections like uniform manifold approximation and projection (UMAP). However, such methods can severely distort the data and have many arbitrary parameter choices. Methods that can model scRNA-seq data as non-discrete "gene expression programs" (GEPs) can better preserve the data's structure, but currently, they are often not scalable, not consistent across repeated runs, and lack an established method for choosing key parameters.

A qualitative analysis of PA leaders in Kentucky.

Objective: To describe Kentucky's physician associate/assistant (PA) leadership pathway and provide advice for individual leadership trajectories.

Methods: A qualitative study using semistructured interviews and inductive coding methodology to identify themes of PA leaders.

Results: Participants were primarily female (76.