MOUSSE: Multi-Omics Using Subject-Specific SignaturEs

Abstract

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.

Keywords: biomarker identification; cancer; multi-omics data integration; precision medicine; unsupervised clustering.

Figure 1

Graphical representation of the MOUSSE methodology (see Materials and Methods for details). (1) Omics datasets have to be provided in the form of matrices sharing the same set of subjects (in our analysis, data were downloaded from TCGA). (2) Input is converted into ranked subject-specific lists and then reduced to produce the signatures. (3) For each omics, the subjects are mapped into a network based on signature similarity. (3b) Optional optimization step in order to select the best signature length by calculating and comparing the networks built from signatures of different lengths. (4) The networks are fused into one. (5) Clusters are identified in an unsupervised manner from the joint network.

Figure 2

Box-and-whisker plot of classification score values for all the tools analyzed across the ten cancer datasets considered in the benchmark. The algorithms are sorted from left to right in decreasing order of their median classification score.

Figure 3

Bar plot of the z-scores calculated on the classification score medians achieved by the software across the ten cancer types.