Dynamic sample augmentation discovers gene biomarker for IgA nephropathy

Abstract

Bulk RNA-seq data suffers from the issues of “high dimensionality and small sample size,” which limits its application in disease research. This paper proposes a dynamic data augmentation method based on Layer-wise Relevance Propagation (LRP) aimed at improving classification performance and biological interpretability under small-sample conditions. The method utilizes the LRP algorithm to calculate the contribution weight of each gene to the classification result and uses this weight to guide sample generation. By systematically amplifying biologically meaningful signals, it constructs semantically reliable augmented samples, avoiding the semantic distortion caused by traditional random perturbations. Simultaneously, a dynamic augmentation mechanism is introduced that tightly couples sample generation with model training, providing difficult-to-classify samples with multiple iterative optimization opportunities and forming a virtuous cycle where classification performance and augmentation quality improve synergistically. On this basis, population-level gene biomarkers are identified from the trained model. Innovatively, an open-environment enrichment analysis method is proposed—that is, instead of being limited to a few feature genes of a single subtype, the union of feature genes from all disease subtypes is taken for enrichment analysis, revealing shared biological pathways from a systems-level perspective and providing a more comprehensive interpretation for subtype-specific mechanism research. Experimental results show that this method effectively improves classification accuracy, and through this open-environment enrichment approach, six hub genes were identified as gene markers for IgAN.