Learning Objectives
- Discover how to annotate and organize your data sets on the Cytobank platform.
- Understand machine learning-assisted analysis of high dimensional data.
- Create meaningful illustrations with statistical inference results to easily communicate your findings.
Day 1 | From 9:00 AM to 13:00 PM CET
- The Cytobank information management system:
- How to create experiments and store your data.
- Don't lose track of your analysis workflow: linked analysis and sample tags.
- High quality data provides you high quality results:
- Scaling flow cytometry data on the Cytobank platform.
- Correct the spillover fluorescence with a compensation matrix
- Basic analysis features: stain index, manual gates and export statistics
- Break
- Visualize your high dimensional data using dimensionality reduction (DR):
- What is a DR algorithm and the Cytobank DR suite?
- Reading a DR map: how the data look after a DR analysis.
- Advanced DR settings and when we need to change them.
- Q&A
Day 2 | From 9:00 AM to 13:00 PM CET
- Automatic segregation of cells in phenotypical similar groups with FlowSOM:
- Why should we use a clustering tool?
- Setting up a FlowSOM analysis.
- Phenotype FlowSOM clusters and metaclusters.
- Identify significant metaclusters using common statistical inference tests.
- Discover new biomarkers with CITRUS:
- What is CITRUS?
- Getting all the information out of the CITRUS models.
- Effect of the CITRUS settings on the results.
- Break
- How to integrate your pipeline of analysis on the Cytobank platform: user case study examples.
- Q&A
