Data-Mining Techniques for Single Cell Data

The Presenter

Yvan Saeys
Ghent University - VIB
Belgium

Session Summary

In this tutorial we will provide the students with some fundamentals about the different types of data mining techniques that can be applied to extract knowledge from single-cell data. In addition to the basic methodology, we will also give some examples of the different tools that implement these methodologies. Use cases of the different data mining methodologies will be illustrated on different types of single cell data: flow/mass cytometry, single-cell imaging, and single-cell transcriptomics data. 

The following topics will be covered in the course:

• Data pre-processing and quality control.
• Overview of the different types of models (descriptive and predictive models).
• Techniques for automated gating (unsupervised and supervised techniques).
• Comparing samples Identifying biomarkers.
• Visualization of high-dimensional single-cell data.
• Modeling cell developmental trajectories.

At the end of the tutorial, the student should have a broad overview of the different types of data mining techniques that can be used to answer specific biological questions about his/her single cell data, as well as the tools that are around to perform these analyses.

CMLE Credit: 1.5