使用亲和网络融合（ANF）整合多组学数据用于癌症患者聚类
Integrate Multi-omic Data Using Affinity Network Fusion (ANF) for Cancer Patient Clustering
摘要：
将癌症患者聚类成亚组并识别癌症亚型是癌症基因组学中的重要任务。基于综合多组学分子谱分析的聚类通常可以比使用单一数据类型的聚类获得更好的结果，因为每个组学数据类型（代表患者的一个视图）可能包含互补信息。然而，它是具有挑战性的异构体学数据类型直接集成。本文在相似性网络融合（SNF）的基础上，提出了相似性网络融合（ANF），它是SNF的“升级”，具有多种优点。与SNF类似，ANF将每个组学数据类型视为患者的一个视图，并学习融合的亲和力（转换）矩阵进行聚类。我们将ANF应用于从GDC数据门户网站下载的精心处理的协调癌症数据集，该数据集由2193名患者组成，并将患者聚类为正确的疾病类型，产生了有希望的结果。我们的实验结果还证明了在患者聚类中结合使用ANF的特征选择和转换的能力。此外，特征间隙分析表明，使用我们提出的框架学习的四种癌症类型的亲和力矩阵可能已经成功地捕获了患者组结构，并可用于发现未知的癌症亚型。
英文摘要：
Clustering cancer patients into subgroups and identifying cancer subtypes is an important task in cancer genomics. Clustering based on comprehensive multi-omic molecular profiling can often achieve better results than those using a single data type, since each omic data type (representing one view of patients) may contain complementary information. However, it is challenging to integrate heterogeneous omic data types directly. Based on one popular method -- Similarity Network Fusion (SNF), we presented Affinity Network Fusion (ANF) in this paper, an "upgrade" of SNF with several advantages. Similar to SNF, ANF treats each omic data type as one view of patients and learns a fused affinity (transition) matrix for clustering. We applied ANF to a carefully processed harmonized cancer dataset downloaded from GDC data portals consisting of 2193 patients, and generated promising results on clustering patients into correct disease types. Our experimental results also demonstrated the power of feature selection and transformation combined with using ANF in patient clustering. Moreover, eigengap analysis suggests that the learned affinity matrices of four cancer types using our proposed framework may have successfully captured patient group structure and can be used for discovering unknown cancer subtypes.
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。
