New Robust Clustering Model for Identifying Cancer Genome Landscapes
New Robust Clustering Model for Identifying Cancer Genome Landscapes
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DOI：
10.1109/icdm.2016.0026
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Hongchang Gao;Xiaoqian Wang;Heng Huang
中科院分区：
文献类型：
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作者：
Hongchang Gao;Xiaoqian Wang;Heng Huang
近几十年来，全面基因组数据的可用性促进了对癌症分子画像的了解。具体来说，通过进行癌症聚类，可以将癌症样本根据其分子特征的异同分成若干组。传统的癌症聚类通常分析来自单个组织的癌症样本，但这种分析无法揭示不同类型癌症之间的联系。跨人类癌症的景观分析可以帮助发现跨癌症组织共享的分子特征，为设计针对不同癌症患者的新基因疗法提供机会。然而，基因组数据中的噪声水平很高。鲁棒聚类方法是解决这一问题的关键。本文提出了一种新的鲁棒聚类方法，从一个新的角度对TCGA癌症数据进行景观分析，即消除噪声，然后对清理后的数据进行聚类，而不是像现有的抗噪声范数方法那样削弱噪声的影响。在基因组数据集和聚类基准数据集上进行的大量实验证实了我们提出的方法的有效性和正确性。
In recent decades, the availability of comprehensive genomic data has facilitated the insight of molecular portraits of cancer. Specifically, by conducting cancer clustering, cancer samples can be divided into several groups according to their differences and similarities in molecular characteristics. Traditional cancer clustering usually analyzes cancer samples from a single tissue, but such analysis cannot reveal the connections among different types of cancer. Landscape analysis across human cancers can help discover molecular signatures shared across cancer tissues, providing an opportunity to design new gene therapy tailored for different cancer patients. However, the noise level in genomic data is high. The robust clustering method is crucial to tackle this problem. In this paper, we propose a new robust clustering method to approach the landscape analysis for TCGA cancer data from a novel view, which is to eliminate the noise and then perform clustering on the cleaned data rather than weaken the effect of noise as existing noise-resistant norm methods. Extensive experiments on both genomic datasets and clustering benchmark datasets confirm the effectiveness and correctness of our proposed method.