SFB 1054 Seminar - Yvan Saeys
Title: Unravelling cell developmental dynamics using single-cell bioinformatics
Recent advances in single-cell analysis, such as 20+color flow cytometry, mass cytometry and single-cell transcriptomics allow scientists to measure an increasing number of parameters per cell, generating large and high-dimensional datasets. To analyze, visualize and interpret these data, newly available computational techniques should be adopted, evaluated and improved upon by the immunological community.
In this talk, I will give an overview of the different types of computational approaches that can be applied to high-dimensional single-cell data. I will discuss both existing supervised and unsupervised machine learning techniques that can be used for data visualization, population identification, differential analysis and biomarker detection, as well as introduce a novel class of computational techniques to better model cell developmental dynamics. An overview of current state-of-the-art methods as well as novel developments in the field will be presented, and case studies on myeloid cell development will be used to highlight the implications for immunology research.
Keywords: Computational cytometry, single-cell transcriptomics, Cell development.
Saeys Y, Van Gassen S. and Lambrecht B.N.Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat Rev Immunol. 2016, 16(7):449-462
Cannoodt R, Saelens W, Saeys Y. Computational methods for trajectory inference from single-cell transcriptomics. Eur J Immunol. 2016, 46(11):2496-2506
TranslTUM, Center for Translational Cancer Research,
Johannes B. Ortner Forum, Room 22.0.1, EG
Ismaninger Str. 22, 81675 München
Host: Christina Zielinski (B10)