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.

高通量技术的进步允许对多种类型的组学数据进行测量，但是几种不同数据类型的有意义的集成仍然是一个巨大的挑战。另一个重要且困难的问题是发现以相关临床差异为特征的分子疾病亚型，例如生存率。这里，我们提出一个新的方法，被称为p erturbation聚类的数据在tegration和疾病小号ubtyping（PINS），能够解决这两个挑战。该框架已通过基因表达，DNA甲基化，非编码microRNA和拷贝数变异数据在成千上万的癌症样本上得到验证，这些数据可从基因表达综合，广泛研究所，癌症基因组图谱（TCGA）和欧洲基因组-现象档案。这种同时分型的方法可以准确地识别出已知癌症亚型和具有明显不同生存特征的患者的新亚组。结果是从基因组规模的分子数据中获得的，而没有任何其他类型的先验知识。这种方法足够通用，可以替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附加功能。