Laura Cantini (CNRS, IBENS)
Due to the advent of high-throughput technologies, high-dimensional “omics” data are produced at an increasing pace. In cancer biology, national and international consortia have profiled thousands of tumors at multiple molecular levels (“multi-omics”) allowing to gather a comprehensive molecular picture of this disease. Moreover, multi-omics profiling approaches are currently being transposed at single-cell resolution, further increasing the information accessible from cancer samples.The current main challenge is to design appropriate methods to integrate this wealth of information and translate it into actionable biological knowledge.In this talk, I will discuss two main computational directions for multi-omics integration: (i) multilayer networks to integrate a large range of interactions and (ii) joint dimensionality reduction to extract biological knowledge simultaneously from multiple omics. First, I will present their application on bulk data and then I will discuss our ongoing research in single-cell.Selected associated publications & preprintsCantini L, Medico E, Fortunato S, Caselle M. Detection of gene communities in multi-networks reveals cancer drivers. Scientific reports. 2015 Dec 7;5(1):1-0.Cantini, L., Zakeri, P., Hernandez, C., Naldi, A., Thieffry, D., Remy, E., Baudot, A., 2021. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nature Communications 12.Kang Y, Thieffry D, Cantini L. Evaluating the reproducibility of single-cell gene regulatory network inference algorithms. Frontiers in genetics. 2021 Mar 22;12:362.Huizing GJ, Peyré G, Cantini L. Optimal Transport improves cell-cell similarity inference in single-cell omics data. bioRxiv. 2021 Jan 1.