Course description:

Single cell RNA-seq (scRNA-seq) is a powerful technology for profiling gene expression in individual cells. It has been widely applied to answer a variety of important biological and clinical questions. Analyzing scRNA-seq data is a challenging task due to the complexity of the data and the biological questions. In addition, there are numerous scRNA-seq analysis software available, and it could be difficult to choose the appropriate tools for one’s analysis.

This short course will introduce participants to a number of scRNA-seq analysis procedures. The topics include data preprocessing, data normalization, batch effect correction, cell clustering, pseudo-time construction, rare cell type identification, cell annotation, and differential expression. The emphasis is to provide guidance and hands-on experience in analyzing real-world scRNA-seq data. There will be several lab sessions, where real datasets and example R code will be provided for practice. This short course is of interest to researchers without prior experience working with scRNA-seq data, as well as more experienced individuals interested in learning practical solutions to some common analytic challenges. Experience with R is required.

Instructors:

• Hao Wu, Associate Professor, Department of Biostatistics and Bioinformatics, Emory University
• Ziyi Li, Assistant Professor, Department of Biostatistics, MD Anderson Cancer Center.

Course materials

Click the links below to obtain lecture slides. All lab materials (several RData and R files) are here as a single zip file.

• Lecture 1: Intro and data preprocessing.
• Lab 1 : preprocessing and visualization.
• Lecture 2: Normalization, batch effect, imputation, DE, simulator.
• Lab 2 : Normalization, batch effect, imputation, DE, simulator 
• Lecture 3: Clustering and pseudotime construction.
• Lab 3: Clustering and pseudotime construction
• Lecture 4: Supervised cell typing & related single cell data sources.
• Lab 4: supervised cell typing.  
• Lecture 5: scRNA-seq in cancer.