Dates: online, January 16th-19th
- COURSE OVERVIEW
This course introduces what flow cytometry is and why we use it to analyze cell population composition in biological samples. We will learn about best practices for how to analyze flow cytometry data using R/Bioconductor. Said practices include: preprocessing of the data (compensation, transformation, and quality control), multi-dimensional cell population identification via clustering, and visualizing the results in 2D. These tools can be applied to all types of cytometry including flow, mass, and spectral.
- TARGET AUDIENCE AND ASSUMED BACKGROUND
This course is created for anyone interested in analyzing biological samples with single-cell flow cytometry. Background in flow cytometry and R/Bioconductor is not necessary as we will go over a short introduction to them. However, experience with programming will help greatl
- LEARNING OUTCOMES
1- Understand the flow cytometry machinery and its analytical purpose.
2- Be able to set up the infrastructure for and write basic data analytic scripts in R.
3- Describe and execute each step in the flow cytometry data analytics pipeline in R/Bioconductor.
4- Be comfortable with interpreting and eliciting conclusions from the results of the flow cytometry data analytics pipeline.
