Neurodegenerative diseases (NDDs) are complex and lack effective treatment due to their poorly understood mechanism. The increasingly used data analysis from Single nucleus RNA Sequencing (snRNA-seq) allows to explore transcriptomic events at a single cell level, yet face challenges in interpreting the mechanisms underlying a disease. On the other hand, Neural Network (NN) models can handle complex data to offer insights but can be seen as black boxes with poor interpretability. In this context, explainable AI (XAI) emerges as a solution that could help to understand disease-associated mechanisms when combined with efficient NN models. However, limited research explores XAI in single-cell data. In this work, we implement a method for identifying disease-related genes and the mechanistic explanation of disease progression based on NN model combined with SHAP. We analyze available Huntington's disease (HD) data to identify both HD-altered genes and mechanisms by adding Gene Set Enrichment Analysis (GSEA) comparing two methods, differential gene expression analysis (DGE) and NN combined with SHAP approach. Our results show that DGE and SHAP approaches offer both common and differential sets of altered genes and pathways, reinforcing the usefulness of XAI methods for a broader perspective of disease.

翻译：神经退行性疾病（NDDs）机制复杂且认知不足，缺乏有效治疗方法。单核RNA测序（snRNA-seq）数据分析的日益普及使得在单细胞水平探索转录组事件成为可能，但在阐释疾病潜在机制方面仍面临挑战。另一方面，神经网络（NN）模型能够处理复杂数据以提供洞见，但常被视为可解释性差的“黑箱”。在此背景下，可解释人工智能（XAI）与高效神经网络模型结合，可作为理解疾病相关机制的解决方案。然而，目前针对单细胞数据的XAI研究仍较为有限。本研究基于神经网络模型结合SHAP方法，实现了一种识别疾病相关基因及解释疾病进展机制的方法。通过分析可获取的亨廷顿病（HD）数据，我们采用差异基因表达分析（DGE）与神经网络结合SHAP两种方法进行比较，并辅以基因集富集分析（GSEA），从而识别HD相关变异基因及其机制。研究结果表明，DGE与SHAP方法既能提供共同的变异基因和通路集合，也能揭示差异性集合，这强化了XAI方法在获取更全面疾病认知方面的实用价值。