使用图注意力网络从单细胞数据预测疾病状态
Disease State Prediction From Single-Cell Data Using Graph Attention Networks
摘要：
单细胞RNA测序（scRNA-seq）已经彻底改变了生物学发现，提供了组织中细胞异质性的无偏倚图像。虽然scRNA-seq已被广泛用于提供对健康系统和疾病的洞察，但它尚未用于疾病预测或诊断。图注意力网络（GAT）已被证明是通用的，通过从原始特征和图结构学习的广泛任务。在这里，我们提出了一个图形注意力模型，用于从多发性硬化症（MS）患者的大型数据集上的单细胞数据预测疾病状态。MS是一种中枢神经系统疾病，可能难以诊断。我们训练我们的模型从血液和脑脊液（CSF）获得的7名MS患者和6名健康成年人（HA）的队列单细胞数据，产生66，667个单个细胞。我们在预测MS方面达到了92%的准确率，优于其他最先进的方法，如图卷积网络和随机森林分类器。此外，我们使用学习的图形注意力模型来深入了解对这种预测很重要的特征（细胞类型和基因）。图注意力模型还允许我们为强调两种条件之间差异的细胞推断新的特征空间。最后，我们使用注意力权重来学习一个新的可以可视化的低维嵌入。据我们所知，这是第一次尝试使用图形注意力和深度学习来从单细胞数据预测疾病状态。我们设想将这种方法应用于其他疾病的单细胞数据。
英文摘要：
Single-cell RNA sequencing (scRNA-seq) has revolutionized biological discovery, providing an unbiased picture of cellular heterogeneity in tissues. While scRNA-seq has been used extensively to provide insight into both healthy systems and diseases, it has not been used for disease prediction or diagnostics. Graph Attention Networks (GAT) have proven to be versatile for a wide range of tasks by learning from both original features and graph structures. Here we present a graph attention model for predicting disease state from single-cell data on a large dataset of Multiple Sclerosis (MS) patients. MS is a disease of the central nervous system that can be difficult to diagnose. We train our model on single-cell data obtained from blood and cerebrospinal fluid (CSF) for a cohort of seven MS patients and six healthy adults (HA), resulting in 66,667 individual cells. We achieve 92 % accuracy in predicting MS, outperforming other state-of-the-art methods such as a graph convolutional network and a random forest classifier. Further, we use the learned graph attention model to get insight into the features (cell types and genes) that are important for this prediction. The graph attention model also allow us to infer a new feature space for the cells that emphasizes the differences between the two conditions. Finally we use the attention weights to learn a new low-dimensional embedding that can be visualized. To the best of our knowledge, this is the first effort to use graph attention, and deep learning in general, to predict disease state from single-cell data. We envision applying this method to single-cell data for other diseases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
