Single-cell RNA-seq data analysis

Summer 2019

Charles University

Course Description:

Basic computer skills for processing, visualizing, and interpreting single-cell RNA-seq (scRNA-seq) data. Basic R programming will be introduced. Publicly available scRNA-seq data from current biology research will be used to illustrate the steps involved in the analysis.

Instructor:

Joe Song (joemsong@cs.nmsu.edu)

Fulbright Visiting Professor, Department of Cell Biology, Charles University

Professor of Computer Science, Faculty Member of Molecular Biology
New Mexico State University

Prerequisite:

1. Basics of molecular biology.

2. Some exposure to programming languages such as R, SAS, Python, C/C++, or MATLAB are highly desirable. However, the course will introduce the basics of R programming.

Meeting time:

Mondays 14:50—16:20 from 18/02/2019 to 17/05/2019 (13 weeks)

Examination period 27/05/2019 to 30/06/2019

Projects:

Select a scRNA-seq data set of interest to their own research. Then apply the data analysis methods learned in class on the data set.

Grading:

Project assignments 80%

Final project presentation 20%

Textbook:

Martin Hemberg et al. Analysis of Single-Cell RNA-Seq Data.

(PDF file will be distributed for free.)

Topics:

Week 1. Introduction to single cell RNA sequencing

Week 2. Introduction to R and bioconductor

Week 3. Expression data quality control

Week 4. Normalization of library size

Week 5. Removing unwanted confounders

Week 6. Cluster analysis

Week 7. Gene selection

Week 8. Differential expression analysis

Week 9. Trajectory inference

Week 10. Meta-analysis

Week 11. Sequencing reads quality control

Week 12. Mapping scRNA reads to genes