用可解释AI分析单细胞数据，揭示亨廷顿病的致病基因与机制。
Explainable AI model reveals disease-related mechanisms in single-cell RNA-seq data
- 结合神经网络与SHAP方法识别疾病相关基因
- 发现传统差异表达与XAI方法有重叠和互补的基因集
- 适合研究神经退行性疾病机制的生物学家与算法开发者
神经退行性疾病（NDDs）机制复杂且治疗手段有限。单核RNA测序（snRNA-seq）可在单细胞水平解析转录组变化，但难以解释疾病机制。神经网络虽能处理复杂数据，却因可解释性差被称为“黑箱”。本研究提出一种结合神经网络与SHAP的可解释人工智能（XAI）方法，用于识别亨廷顿病（HD）相关基因及疾病进展机制。通过对比差异基因表达分析（DGE）与神经网络+SHAP方法，并引入基因集富集分析（GSEA），结果表明两种方法共享部分基因与通路，也发现独特结果，验证了XAI在揭示疾病机制上的价值。
原文摘要 · Abstract (English)
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.
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