Multi-omics data fusion using adaptive GTO guided Non-negative matrix factorization for cancer subtype discovery. (January 2023)

• Record Type: Journal Article
• Authors: Bansal, Bhavana; Sahoo, Anita
• Is Part Of: Computer methods and programs in biomedicine. Volume 228(2023)
• Publication Date: 2023-01
• Subjects: Multi-omics Data -- Cancer Subtype Identification -- Non-negative Matrix Factorization -- Integrative Clustering -- Meta-heuristic Optimization -- Survival Analysis; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107246
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel integrative method using multi-omics data is proposed for cancer subtyping. An adaptive weight-update strategy is devised to enhance traditional GTO algorithm. Initial points obtained by Ada-GTO improve the clustering performance of sparse-jNMF. Log-rank test using KM-curve shows the superiority of the proposed method. Proposed method allows multi-omics data integration and useful in guided treatments. Abstract: Background and objective: : Cancer subtype discovery is essential for personalized clinical treatment. With the onset of progressive profile techniques for cancer, a large amount of heterogeneous and high-dimensional transcriptomic, proteomic and genomic datasets are easily accumulated. Integrative clustering of such multi-omics data is crucial to recognize their latent structure and to acknowledge the correlation within and across them. Although the integrative analysis of diversified multi-omics data is informative, it is challenging when multiplicity in data inflicts poor accordance w.r.t. clustering structure. The objective of this work is to develop an effective integrative analysis framework that encapsulates the heterogeneity of various biological mechanisms and predicts homogeneous subgroups of cancer patients. Method: : In this paper, improved sparse-joint non-negative matrix factorization (sparse-jNMF) has been devised for the problem of cancer-subtype discovery. The initial points of sparse-jNMF have improved using a novel meta-heuristic algorithm adaptive gorilla troops optimizer (Ada-GTO). Improving the initialization of sparse-jNMF enhances its convergence behavior and further strengthens the clustering performance. In addition, the consensus clustering approach has been adopted to construct a patient-patient similarity matrix for obtaining stable clusters of patient samples. Result: : The proposed framework has been applied to 4 different real-life multi-omics cancer datasets, namely colon adenocarcinoma, breast-invasive carcinoma, kidney-renal clear-cell carcinoma, and lung adenocarcinoma. The proposed method results in patient clusters with better silhouette scores and cluster purity than classical initialization and similar meta-heuristics for initial point estimation approaches. Survival probabilities estimated using Kaplan-Meier (KM) curve show statistically significant difference ( p < 0.05) for the homogenous cancer patient clusters obtained using the proposed method as compared to iCluster. The presented approach further identified the somatic mutations for the classified subgroups, which is beneficial to provide targeted treatments. Conclusion: : This paper proposes Ada-GTO guided sparse-jNMF framework for cancer subtype discovery, considering the information from multiple omic features that provide comprehension. The proposed meta-guided framework outperforms all other state-of-the-art counterparts. It also has great potential for obtaining the homogeneous subgroups of other diseases. Graphical abstract: Image, graphical abstract