Abstract
Single-cell transcriptomics offers critical insights into the molecular mechanisms of heart failure (HF) with reduced or preserved ejection fraction. However, understanding these mechanisms is hindered by the growing complexity of single-cell data and the difficulty in unmasking meaningful differential gene signatures among HF types. Machine learning, particularly deep neural networks (NNs), address these challenges by learning transcriptional patterns, reconstructing expression profiles and effectively classifying cells but often lacks interpretability. Recent advances in explainable AI (XAI) offer tools to clarify model decisions. Yet pinpointing differentially regulated genes with these tools remains challenging. We introduce a novel method to identify differentially explained genes (DXGs) based on importance scores derived from custom-built NNs. We highlight the superiority of DXGs in identifying HF subtypes-specific pathways that provide new insights into different types of HF. Offering a robust foundation for future research and therapeutic exploration in expanding transcriptome atlases.