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Integration of multi-omics data

Published on December 31, 2025   38 min

A selection of talks on Cardiovascular & Metabolic

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0:00
Hello. My name is Ana Conesa. I'm a research professor at the Spanish National Research Council, working at the Institute for Integrative Systems Biology in Valencia. I will be talking to you about the methods and the strategies developed in my lab for the integration of multi-omics data.
0:24
The analysis of multi-omics data, as you may know, is about the integration of different types of molecular information that are represented in different layers that go from the genomics data, epigenomics data, gene expression transcriptomics, proteomics, metabolomics, and even other types of biological data like single-cell of radiomics data. We see this problem as a situation in which we could have two different approaches to do the integration. One approach is that you concentrate on the samples, on the observations, and you try to use these molecular data to cluster samples in a way that we can identify different subgroups. In this scenario, once you have identified the subgroups, you can predict to which of them an additional external sample will belong. The other approach is to concentrate on the features. So evaluate the features that are measured across the different samples and use them to infer connections or relationships between them, leading to multi-layered networks. In this situation, we can have two different conditions, and because of the data identified, particular components, particular features change their connectivity between these two conditions. In both cases, you may have biomarkers that are either definitory of the clustering solution, or they are hubs, so they are important components, in this multi-layered network. My lab is mostly interested in this second scenario, in which we are looking at features, and we are trying to understand what the relationship is between them.

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Integration of multi-omics data

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