FloREN: Decoding Immune Regulatory Networks through Interpretable Graph Transformer Patient Representations
TL;DR
A Framework for Learning Over REgulatory-Embedding Networks (FloREN), a supervised and interpretable sample representation method that enables improved sample stratification and biomarker discovery and supports downstream analyses that found specific immune network mechanisms in immune-mediated inflammatory diseases (IMIDs).
Abstract
Single-cell RNA sequencing (scRNA-seq) enables detailed characterization of cellular heterogeneity, yet understanding the full cellular and regulatory environment of complex tissues remains challenging. In the era of large single-cell atlases, this technology has become increasingly accessible, and datasets have grown in scale and statistical power. As a result, sample representation methods have emerged as a promising strategy to summarize patient-level biological variation. However, most existing approaches rely on unsupervised learning frameworks with ambiguous biological interpretability. Here we present a Framework for Learning Over REgulatory-Embedding Networks (FloREN), a supervised and interpretable sample representation method. FloREN models single-cell data as a heterogeneous network integrating cells and genes together with gene regulatory and cell-cell communication relationships. Through condition-aware embeddings and interpretable attention networks, FloREN enables improved sample stratification and biomarker discovery. In addition, the framework supports downstream analyses that found specific immune network mechanisms in immune-mediated inflammatory diseases (IMIDs).