Computational analysis methods for cytometry data
Sunday , Nov 20
Advances in both instrumentation capabilities and breadth of reagent availability have led to the expansion of large flow and mass cytometry panels; however, despite this newer ease in generation of high-parameter flow data, the proper extraction of results from larger panels is currently bottlenecked due the limitations of available analysis tools and prohibitively challenging learning curve associated with mastering these tools. In this workshop, we will discuss several recent developments that enable more efficient and comprehensive computational analysis and visualization of cytometry datasets. We will look how flow and mass cytometry human immunophenotyping data are visualized with cutting edge tools like opt-SNE and UMAP, and discuss the benefits of each approach.
We will have ample time for a Q&A to talk about the challenges of algorithmic high-dimensional data analysis and best solutions to address the most common tasks that comprise flow, mass and/or genomic cytometry data comprehension – please free to bring your questions to the table.
What You’ll Learn:
- Familiarize the audience with recent advances in dimensionality reduction approaches in cytometry datasets.
- Introduce and discuss the basic principles of popular dimensionality reduction algorithms and compare their strengths and weaknesses.
- Demonstrate how computational analysis enhances the power of high parameter flow cytometry.