Single nuclei sequencing of human HFpEF (Heart Failure with reduced ejection fraction) and Mice TAC (Transaortic constriction) data
Single-cell transcriptomics has emerged as a powerful technology for understanding cardiovascular diseases, providing valuable insights into transcriptomic changes linked to heart failure, both with and without preserved ejection fraction. However, significant gaps persist in our understanding of the molecular mechanisms underlying these different types of heart failure, and we continue to seek innovative approaches to tackle the challenges posed by the increasing scale and complexity of single-cell datasets in identifying meaningful gene signatures. The integration of machine learning, particularly deep neural networks, has shown immense promise in addressing these challenges by learning transcriptional patterns from single-cell transcriptomic data, reconstructing expression profiles, and effectively classifying cells. Recent advancements in explainable artificial intelligence enhanced the interpretation of these models by attributing importance scores, such as Shapley values. However, methods for identifying differentially regulated gene contribution scores have not yet been integrated. In this study, we introduce a novel method to identify differentially explained genes (DXGs) based on importance scores derived from custom-built neural networks capable of classifying heart failure subtypes at the single-cell level. We highlight the superiority of DXGs in identifying heart failure-relevant pathways, presenting them in a format comparable to traditional differential expression analyses. Using this method, we identify novel signatures providing new insights into the molecular basis of heart failure and offering a robust foundation for future research and therapeutic exploration.