Syllabus
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
Annotation
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.