A novel approach for data integration and disease subtyping.
A novel approach for data integration and disease subtyping.
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DOI：
10.1101/gr.215129.116
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发表时间：
2017-12
期刊：
影响因子：
7
通讯作者：
Draghici S
中科院分区：
文献类型：
--
作者：
Nguyen T;Tagett R;Diaz D;Draghici S
高通量技术的进步允许测量许多类型的组学数据，但几种不同数据类型的有意义集成仍然是一个重大挑战。另一个重要而困难的问题是发现具有相关临床差异（如生存）特征的分子疾病亚型。在这里，我们提出了一种新的方法，称为数据集成和疾病亚型（PINS）的扰动聚类，它能够解决这两个挑战。使用基因表达、DNA甲基化、非编码microRNA和拷贝数变异数据，该框架已在数千个癌症样本上进行了验证，这些数据来自基因表达Omnibus、Broad研究所、癌症基因组图谱（TCGA）和欧洲基因组-表型档案。这种同时分型的方法可以准确地识别已知的癌症亚型和具有显著不同生存概况的患者的新亚组。结果是从基因组尺度的分子数据中获得的，没有任何其他类型的先验知识。该方法具有足够的通用性，可以取代生物医学研究范围之外的现有无监督聚类方法，并具有集成多种类型数据的额外能力。
Advances in high-throughput technologies allow for measurements of many types of omics data, yet the meaningful integration of several different data types remains a significant challenge. Another important and difficult problem is the discovery of molecular disease subtypes characterized by relevant clinical differences, such as survival. Here we present a novel approach, called perturbation clustering for data integration and disease subtyping (PINS), which is able to address both challenges. The framework has been validated on thousands of cancer samples, using gene expression, DNA methylation, noncoding microRNA, and copy number variation data available from the Gene Expression Omnibus, the Broad Institute, The Cancer Genome Atlas (TCGA), and the European Genome-Phenome Archive. This simultaneous subtyping approach accurately identifies known cancer subtypes and novel subgroups of patients with significantly different survival profiles. The results were obtained from genome-scale molecular data without any other type of prior knowledge. The approach is sufficiently general to replace existing unsupervised clustering approaches outside the scope of bio-medical research, with the additional ability to integrate multiple types of data.
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影响因子：
64.5
作者：
Cancer Genome Atlas Research Network
通讯作者：
Cancer Genome Atlas Research Network
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel
影响因子：
64.8
作者：
Alizadeh, AA;Eisen, MB;Staudt, LM
通讯作者：
Staudt, LM