intCC: An efficient weighted integrative consensus clustering of multimodal data
intCC: An efficient weighted integrative consensus clustering of multimodal data
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intCC：多模态数据的有效加权综合共识聚类
DOI：
10.1142/9789811286421_0047
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发表时间：
2023
影响因子：
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通讯作者：
P. Kuan
中科院分区：
文献类型：
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作者：
Can Huang;P. Kuan
多组学数据的高通量分析为更好地了解复杂的人类疾病（如癌症）和潜在地发现新的亚型提供了宝贵的资源。集成聚类已经成为一个强大的无监督学习框架亚型发现。本文将集成方法、一致性聚类和核学习集成聚类相结合，提出了一种有效的加权集成聚类算法intCC。我们说明，intCC可以准确地发现潜在的集群结构，通过广泛的模拟研究和案例研究的TCGA泛癌症数据集。实现我们提出的方法的R包intCC可在https://github.com/candsj/intCC上获得。
High throughput profiling of multiomics data provides a valuable resource to better understand the complex human disease such as cancer and to potentially uncover new subtypes. Integrative clustering has emerged as a powerful unsupervised learning framework for subtype discovery. In this paper, we propose an efficient weighted integrative clustering called intCC by combining ensemble method, consensus clustering and kernel learning integrative clustering. We illustrate that intCC can accurately uncover the latent cluster structures via extensive simulation studies and a case study on the TCGA pan cancer datasets. An R package intCC implementing our proposed method is available at https://github.com/candsj/intCC.