RT Journal
A1 Nguyen, Tin
A1 Tagett, Rebecca
A1 Diaz, Diana
A1 Draghici, Sorin
T1 A novel approach for data integration and disease subtyping
JF Genome Research 
JO Genome Research 
YR 2017 
FD December 01 
VO 27 
IS 12 
SP 2025 
OP 2039 
DO 10.1101/gr.215129.116 
UL http://genome.cshlp.org/content/27/12/2025.abstract 
AB 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.