Pre-processing & batch correction | Lecture

Trainer: Zhichao Miao

Overview: In this session, you will learn about the single-cell data pre-processing and the batch correction. The data pre-processing part includes quality control, contamination removal, doublet prediction, data normalisation and the selection of highly variable genes. The batch correction part will introduce batch regression as well as data integration. It will discuss the difference between these two approaches. 

Learning objectives:

By the end of the session you will be able to:

• Explain the reasons for single-cell preprocessing steps
• Describe the difference between Smart-seq2 and 10x
• Describe the biological meaning of each quality control metric
• Explain the meaning of spike-ins
• Choose thresholds of the metrics
• Use scrublet to predict doublets
• Explain the reason for data normalisation
• Describe the difference between batch correction and data integration
• Use harmony for data integration

Materials:

• Presentation slides

• Pre-recording

Additional learning

• Example notebooks for data processing together with the single-cell data