For researchers who have flow or mass cytometry data sets that they want to interrogate in a less biased way

Overview

Hierarchical gating is the traditional method for the analysis of flow cytometry data, and its outcome relies heavily on the operator judgement and experience. Furthermore, as the number of parameters available increases, the complexity of the levels of gating can become unmanageable. With such a targeted approach, there is a risk that potentially important populations of cells are missed.

The ‘High-dimensional data analysis for cytometry' course will provide learners with a methodology for applying an unbiased approach to data analysis in cytometry, looking at the data as a whole and preventing the disadvantages of hierarchical gating. This training is developed and delivered by the expert teams working in the Crick Flow Cytometry and Bioinformatics and Biostatistics Science Technology Platforms (STPs).

The event is designed for researchers who have flow or mass cytometry data sets that they want to interrogate in a less biased way. Data analysis in this course relies on the R statistical programming language; learners will be shown how to extract data from Flow Cytometry Standard (FCS) files, concatenate data from multiple FCS files, and incorporate metadata relevant to the experimental design. Learners will also explore different dimensional reduction methods, learning about the advantages and disadvantages of each and how to use them.