Multi-project and Multi-profile joint Non-negative Matrix Factorization for cancer omic datasets

Abstract

Motivation: The integration of multi-omic data by using machine learning methods has been focused to solve relevant tasks such as predicting sensitivity to a drug, or subtyping patients. Recent integration methods, such as joint Non-negative Matrix Factorization (jNMF), have allowed researchers to exploit the information contained in the data to unravel the biological processes of multi-omic datasets. Results: We present a novel method called Multi-project and Multi-profile joint Non-negative Matrix Factorization (M&M-jNMF) capable of integrating data from different sources, such as experimental and observational multi-omic data. The method can generate co-clusters between observations, predict profiles and relate latent variables. We applied the method to integrate low-grade glioma omic profiles from The Cancer Genome Atlas (TCGA) and Cell Line Encyclopedia (CCLE) projects. The method allowed to find gene clusters that were mainly enriched in cancer-associated terms. We identified groups of patients and cell lines similar to each other by comparing biological processes. We predicted the drug profile for patients, and we identified genetic signatures for resistant and sensitive tumors to a specific drug.