IEEE/ACM Trans Comput Biol Bioinform. 2021 Nov-Dec;18(6):2891-2897. doi: 10.1109/TCBB.2021.3063284. Epub 2021 Dec 8.
The identification of cancer subtypes is of great importance for understanding the heterogeneity of tumors and providing patients with more accurate diagnoses and treatments. However, it is still a challenge to effectively integrate multiple omics data to establish cancer subtypes. In this paper, we propose an unsupervised integration method, named weighted multi-view low rank representation (WMLRR), to identify cancer subtypes from multiple types of omics data. Given a group of patients described by multiple omics data matrices, we first learn a unified affinity matrix which encodes the similarities among patients by exploring the sparsity-consistent low-rank representations from the joint decompositions of multiple omics data matrices. Unlike existing subtype identification methods that treat each omics data matrix equally, we assign a weight to each omics data matrix and learn these weights automatically through the optimization process. Finally, we apply spectral clustering on the learned affinity matrix to identify cancer subtypes. Experiment results show that the survival times between our identified cancer subtypes are significantly different, and our predicted survivals are more accurate than other state-of-the-art methods. In addition, some clinical analyses of the diseases also demonstrate the effectiveness of our method in identifying molecular subtypes with biological significance and clinical relevance.
癌症亚型的鉴定对于理解肿瘤的异质性以及为患者提供更准确的诊断和治疗方法非常重要。然而，有效地整合多种组学数据以建立癌症亚型仍然是一个挑战。在本文中，我们提出了一种无监督的整合方法，名为加权多视图低秩表示（WMLRR），用于从多种组学数据中识别癌症亚型。给定一组由多种组学数据矩阵描述的患者，我们首先通过探索多个组学数据矩阵的联合分解中的稀疏一致低秩表示来学习统一的相似性矩阵，该矩阵编码了患者之间的相似性。与将每个组学数据矩阵同等对待的现有亚型鉴定方法不同，我们为每个组学数据矩阵分配一个权重，并通过优化过程自动学习这些权重。最后，我们在学习到的相似性矩阵上应用谱聚类来识别癌症亚型。实验结果表明，我们鉴定的癌症亚型之间的生存时间有显著差异，并且我们的预测生存率比其他最先进的方法更准确。此外，对这些疾病的一些临床分析也证明了我们的方法在识别具有生物学意义和临床相关性的分子亚型方面的有效性。
