Multi-Omics Playground is currently available for beta testing for a limited number of early users. Please contact us if you are interested to participate in our beta program.
MultiOmics Playground will be our next easy-to-use yet powerful platform for the analysis of multiple omics data types.
Integrative analysis of heterogeneous omics data is essential to obtain a comprehensive overview of otherwise fragmented information and to better understand dysregulated biological pathways leading to a specific condition. MultiOmics Playground integrates multi-omics data in two aspects. Firstly, it integrates multiple types of omics data such as DNA copy number, mRNA, methylation, mutations, proteomics, and metabolomics. Additionally, it integrates multiple scales of data: from molecular data, to biological concepts, and finally to the macroscopic phenotype. The elegance of Multiomics Playground is that it integrates multi-omics and multi-scale features, all in a single multi-dimensional model.
Multi-layer framework for multi-omics data integration
MultiOmics Playground uses an innovative multilayer network for the integration and analysis of multi-omics data of heterogeneous types. Each layer of the multilayer network represents a certain data type: input layers correspond to genotype features and nodes in the output layer correspond to phenotypes, while intermediate layers may represent genesets or biological concepts to facilitate functional interpretation of the data. MultiOmics Playground then calculates the highest coefficient paths in multilayer network from each genomic feature to the phenotype by computing an integrated score along the paths. These paths may indicate the most plausible signaling cascade. More detailed explanation about our method can found here.
Application example: Multi-omics biomarker discovery
In biomarker discovery, the goal is to identify genotype features that are most associated (“predictive”) with the phenotype data, while remaining consistent with the multilayer structure. Given a phenotype, and using the definition of the path coefficient, you can compute a biomarker score for each genotype feature. The biomarker score is simply the path coefficient from genotype to phenotype feature. In the figure below, you can see a biomarker analysis of Erlotinib sensitivity in breast cancer. Figure A shows a systems analysis using a 6-layer omics network. The maximum coefficient paths are highlighted in green for sensitivity, and in red for resistance to Erlotinib. Figure B shows the top scoring features with the highest path coefficient for each layer.
Application example: Drug response prediction in personalized medicine
In predictive analysis, the aim is to predict phenotype features that are most associated with some given genotype data, while consistent with the multilayer structure. Given a genotype, and using the definition of the path coefficient, you can compute a prediction score for each phenotype feature. The prediction score is simply the path coefficient from a specific genotype feature to the phenotype features. The predictive analysis of drug sensitivity is important for targeted therapy in personalized medicine. In the picture below you can see the predictive analysis of drug sensitivity for EGFR mutated lung cancer. Figure A shows a systems analysis using a 5-layer omics network. The maximum coefficient paths are highlighted in green for mutated EGFR, and in red for wildtype EGFR. Figure B presents the top scoring features with highest path coefficient for each layer.
See also Omics Playground and Rep-seq Playground (BETA).