Single-Cell RNA-seq Data Analysis

This course equips students with the knowledge and skills for comprehensive analysis of single-cell and nucleus RNA sequencing data, emphasizing hands-on work with quality control, normalization, clustering, differential expression, pathway enrichment, trajectory analysis, cell-cell communication, spatial transcriptomics, and multiomics integration.

Course abbreviation: SCDA

Instructor: Dr. Fadhl Alakwaa

Research Investigator, University of Michigan, Ann Arbor, USA

Workload: 12 lectures.

Entrance requirements: Not specified

Used media: State-of-the-art single-cell data analysis tools and techniques

Objectives

• Understand principles of single-cell and nucleus RNA sequencing technologies
• Perform quality control on scRNA-seq and snRNA-seq data
• Apply preprocessing and clustering algorithms
• Annotate cell types using references and tools
• Use visualization tools and relevant datasets
• Conduct differential expression and pathway analysis
• Investigate cell-cell communication and trajectory analysis
• Integrate single-cell RNA-seq with other omics data
• Complete a real-world single-cell RNA-seq analysis project

Assessment

• Project presentations
• Feedback and discussion on projects
• Comprehensive project demonstrating proficiency in single-cell RNA-seq analysis