标题： 可解释的人工智能模型揭示单细胞RNA测序数据中的疾病相关机制 显示英文标题

标题： Explainable AI model reveals disease-related mechanisms in single-cell RNA-seq data

摘要： 神经退行性疾病（NDDs）由于其机制不明确，缺乏有效的治疗方法。 越来越常用单核RNA测序（snRNA-seq）的数据分析方法可以在单细胞水平上探索转录组事件，但在解释疾病背后的机制方面面临挑战。 另一方面，神经网络（NN）模型可以处理复杂数据以提供见解，但因其可解释性差而被视为黑箱。 在这种情况下，可解释的人工智能（XAI）作为一种解决方案出现，当与高效的NN模型结合时，可以帮助理解与疾病相关的机制。 然而，有限的研究探讨了XAI在单细胞数据中的应用。 在本工作中，我们实施了一种方法，基于NN模型结合SHAP来识别与疾病相关的基因和疾病进展的机制解释。 我们分析可用的亨廷顿病（HD）数据，通过添加基因集富集分析（GSEA）比较两种方法，差异基因表达分析（DGE）和NN结合SHAP方法，以识别HD改变的基因和机制。 我们的结果表明，DGE和SHAP方法提供了共同和不同的改变基因和通路集，加强了XAI方法在更广泛视角下研究疾病的有效性。 显示英文摘要

摘要： 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.