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Motivation: The cellular system of a living organism is composed of interacting bio-molecules that control cellular processes at multiple levels. Their correspondences are represented by tightly regulated molecular networks. With increasing omics technologies, large-scale disparate data is generated. It has increased the use of molecular and functional interaction networks: gene co-expression, protein–protein interaction (PPI), genetic interaction, and metabolic networks. They are rich sources of information at different molecular levels. Effective integration of this biological information is essential to understand cell functioning and their building blocks (proteins). Therefore, it is necessary to obtain informative representations of proteins and their proximity that is not fully captured by the features extracted directly from one level of information. We propose BraneMF, a novel random walk-based matrix factorization method for learning node representation in multilayer networks with application to omics data integration. Results: We test BraneMF with PPI networks of Saccharomyces cerevisiae, a well-studied yeast model organism. We demonstrate the applicability of learned features for essential multi-omics inference tasks: clustering, function and PPI prediction. We compare it to state-of-the-art integration methods for multilayer networks. BraneMF outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks. The robustness of results is assessed by an extensive parameter sensitivity analysis. Availability: BraneMF is freely available at: https://github.com/Surabhivj/BraneMF
Background: Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and efficiently integrated. Here, we propose BRANEnet, a novel multi-omics integration framework for multilayer heterogeneous networks. BRANEnet is an expressive, scalable, and versatile method to learn node embeddings, leveraging random walk information within a matrix factorization framework. Our goal is to efficiently integrate multi-omics data to study different regulatory aspects of multilayered processes that occur in organisms. We evaluate our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied yeast model organism. Results: We test BRANEnet on transcriptomics (RNA-seq) and targeted metabolomics (NMR) data for wild-type yeast strain during a heat-shock time course of 0, 20, and 120 minutes. Our framework learns features for differentially expressed bio-molecules showing heat stress response. We demonstrate the applicability of the learned features for targeted omics inference tasks: transcription factor (TF)-target prediction, integrated omics network (ION) inference, and module identification. The performance of BRANEnet is compared with existing network integration methods. Our model outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks
IFPEN's Scientific Board awarded the 2018 Yves Chauvin prize to Aurélie Pirayre for her thesis entitled "Reconstruction and Clustering with Graph optimization and Priors on Gene networks and Images". Aurélie Pirayre received her award at the ceremony held at IFPEN's Rueil-Malmaison site on 19 November 2018. Her work represents an advance in the adaptation of image processing methods for the purposes of representing biological data in graph form. Directed by Jean-Christophe Pesquet (Université Paris-Est Marne-la-Vallée, now CentraleSupélec), her research was supervised by Frédérique Bidard-Michelot and Laurent Duval at IFPEN.