MODEC: an unsupervised clustering method integrating omics data for identifying cancer subtypes
MODEC: an unsupervised clustering method integrating omics data for identifying cancer subtypes
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DOI：
10.1093/bib/bbac372
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发表时间：
2022-09
影响因子：
9.5
通讯作者：
Yanting Zhang;H. Kiryu
中科院分区：
文献类型：
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作者：
Yanting Zhang;H. Kiryu
癌症亚型的识别可以帮助研究人员了解隐藏的基因组机制，提高诊断的准确性，改善临床治疗。随着高通量技术的发展，研究人员可以从多个来源访问大量数据。由于多组学和临床数据的高维性和复杂性，需要对多组学数据的集成进行研究，开发有效的工具来实现这一目的仍然是研究人员面临的挑战。在这项工作中，我们提出了一种不利用任何先验知识的完全无监督聚类方法(MODEC)。我们使用多种优化和深度学习技术来整合多组学数据，以识别癌症亚型和分析重要的临床变量。由于基因水平的数据集存在非线性，我们采用流形优化方法从原始的组学数据中提取本质信息，得到一个低维的潜在子空间。然后，MODEC使用基于深度学习的聚类模型迭代定义聚类质心，并通过最小化Kullback-Leibler发散损失为每个样本分配聚类标签。MODEC应用于癌症基因组图谱数据库中的6个公共癌症数据集，在亚型结果的准确性和可靠性方面优于8种竞争方法。MODEC在确定生存模式和显著的临床特征方面具有极强的竞争力，可以帮助医生监测疾病进展并提供更合适的治疗策略。
The identification of cancer subtypes can help researchers understand hidden genomic mechanisms, enhance diagnostic accuracy and improve clinical treatments. With the development of high-throughput techniques, researchers can access large amounts of data from multiple sources. Because of the high dimensionality and complexity of multiomics and clinical data, research into the integration of multiomics data is needed, and developing effective tools for such purposes remains a challenge for researchers. In this work, we proposed an entirely unsupervised clustering method without harnessing any prior knowledge (MODEC). We used manifold optimization and deep-learning techniques to integrate multiomics data for the identification of cancer subtypes and the analysis of significant clinical variables. Since there is nonlinearity in the gene-level datasets, we used manifold optimization methodology to extract essential information from the original omics data to obtain a low-dimensional latent subspace. Then, MODEC uses a deep learning-based clustering module to iteratively define cluster centroids and assign cluster labels to each sample by minimizing the Kullback-Leibler divergence loss. MODEC was applied to six public cancer datasets from The Cancer Genome Atlas database and outperformed eight competing methods in terms of the accuracy and reliability of the subtyping results. MODEC was extremely competitive in the identification of survival patterns and significant clinical features, which could help doctors monitor disease progression and provide more suitable treatment strategies.