Integrative analyses of high-throughput ‘omic data, such as DNA methylation, DNA copy number alteration, mRNA and protein expression levels, have created unprecedented opportunities to understand the molecular basis of human disease. In particular, integrative analyses have been the cornerstone in the study of cancer to determine molecular subtypes within a given cancer. As malignant tumors with similar morphological characteristics have been shown to exhibit entirely different molecular profiles, there has been significant interest in using multiple ‘omic data for the identification of novel molecular subtypes of disease, which could impact treatment decisions. Therefore, we have developed intNMF, an integrative approach for disease subtype classification based on non-negative matrix factorization. The proposed approach carries out integrative clustering of multiple high dimensional molecular data in a single comprehensive analysis utilizing the information across multiple biological levels assessed on the same individual. As intNMF does not assume any distributional form for the data, it has obvious advantages over other model based clustering methods which require specific distributional assumptions. Application of intNMF is illustrated using both simulated and real data from The Cancer Genome Atlas (TCGA).

The summary table, followed by cross tabulation, represents somatic mutations in a few genes (highlighted by previous studies [44, 45]) presented as percentage of their presence in each of the three integrative clusters. Graphical representation of this table has been provided with S8 Fig.

The summary table, followed by cross tabulation tables, represents the receptor status for estrogen (ER), progesterone (PR) and human epidermal growth factor 2 (HER2) presented as percentage of their presence in each of the six intNMF clusters; and somatic mutations in four genes TP53, PIK3CA, GATA3 and MAP3K1.