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MOUSSE: Multi-Omics Using Subject-Specific SignaturEs
| Content Provider | MDPI |
|---|---|
| Author | Fiorentino, Giuseppe Visintainer, Roberto Domenici, Enrico Lauria, Mario Marchetti, Luca |
| Copyright Year | 2021 |
| Description | High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival. |
| Starting Page | 3423 |
| e-ISSN | 20726694 |
| DOI | 10.3390/cancers13143423 |
| Journal | Cancers |
| Issue Number | 14 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-07-08 |
| Access Restriction | Open |
| Subject Keyword | Cancers Biochemical Research Multi-omics Data Integration Precision Medicine Biomarker Identification Unsupervised Clustering Cancer |
| Content Type | Text |
| Resource Type | Article |
