CYTO U Webinar - The Art of Dimensionality Reduction: What Really Matters
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CYTO U Webinar - The Art of Dimensionality Reduction: What Really Matters

When: March 28, 2019
12pm Eastern
Where: United States
Presenter: Karel Drbal, Ph.D.

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The Art of Dimensionality Reduction: What Really Matters

Thursday, March 28th, 2019 at 12:00 pm EST (US & Canada)


Presented by:

Karel Drbal, Ph.D.
Department of Cell Biology, Faculty of Science, Charles University, Prague, Czechia

Moderated by:

Sofie Van Gassen
Postdoctoral Researcher
VIB-UGent Center for Inflammation Research
Ghent, Belgium

About the Faculty

Karel Drbal obtained a MSc in Cell Biology and PhD in Immunology from Charles University in 1991 and 2000, respectively. For most of his career he has been specialized in monoclonal antibody generation and characterization (MEM-series) by flow cytometry. After postdoctoral fellowships with Dr. Hannes Stockinger at Medical University Vienna and Dr. Vaclav Horejsi at Institute of Molecular Genetics of CAS, he held the CSO position (2010-2013) in biotech enterprise Exbio Praha. Since 2013 he is teacher of Immunology, Systems Biology and Cytometry, and in 2015 he launched the lab of Molecular dynamics of the immune response at Charles University. His studies in the field includes the following:

Focus on clinical collaboration and precision medicine approach
Analysis of microevolution of the immune system in response to certain human pathologies correlated with high mutation rates of tumor cells or pathogens
Research and technological development of large-scale generation of binding proteins (monoclonal antibodies and engineered alternatives)
Multivariate data analysis, image analysis and structural bioinformatics

Webinar Summary

I will start with brief introduction to the state-of-the-art dimensionality reduction algorithms (SNE variants and UMAP) for unsupervised cytometry data analysis. Next, these algorithms will be compared side by side to the new EmbedSOM algorithm based on FlowSOM clustering approach. Shortly, the importance of input data quality, the logic of clustering and embedding workflow and the output annotation will be described with the examples in R environment. Finally, the results of benchmarking datasets as well as few use cases analyzed by hierarchical dissection of the embedded data will be presented. To close, the comparison to manual workflow and the subjective visual perception of the output quality will be discussed, and the future directions of unsupervised vs. supervised analysis beyond the field of cytometry will be outlined.

Learning Objectives

Discuss the principle of the new EmbedSOM algorithm and the associated workflow
Learn the differences between the embedding algorithms for data visualization
Describe hierarchical dissection of complex datasets including statistical output
Discuss the strengths and weaknesses of the unsupervised process

Who Should Attend

Clinical and research scientists collecting multidimensional data not only in the field of cytometry, but also microscopy, transcriptomics, and proteomics
Computational scientists and bioinformatics core personnel interested in unsupervised data visualization



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