Unique and shared transcriptomic signatures underlying localized scleroderma pathogenesis identified using interpretable machine learning.

ABSTRACT: Using transcriptomic profiling at single-cell resolution, we investigated cell-intrinsic and cell-extrinsic signatures associated with pathogenesis and inflammation-driven fibrosis in both adult and pediatric patients with localized scleroderma (LS). We performed single-cell RNA-Seq on adult and pediatric patients with LS and healthy controls. We then analyzed the single-cell RNA-Seq data using an interpretable factor analysis machine learning framework, significant latent factor interaction discovery and exploration (SLIDE), which moves beyond predictive biomarkers to infer latent factors underlying LS pathophysiology. SLIDE is a recently developed latent factor regression-based framework that comes with rigorous statistical guarantees regarding identifiability of the latent factors, corr

SUBMITTER: Rosen AB 

PROVIDER: S-EPMC11981619 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature