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Host professor's lecture - MB151P120

Title:	Host professor's lecture
Czech title:	Přednášky hostujících profesorů
Guaranteed by:	Department of Cell Biology (31-151)
Faculty:	Faculty of Science
Actual:	from 2019
Semester:	both
E-Credits:	3
Hours per week, examination:	2/0, Ex [HT]
Capacity:	25
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	English
Note:	you can enroll for the course repeatedly enabled for web enrollment you can enroll for the course in winter and in summer semester

Guarantor:	prof. RNDr. Jan Černý, Ph.D.
Teacher(s):	prof. RNDr. Jan Černý, Ph.D.

Opinion survey results Examination dates WS schedule SS schedule

Annotation -

Last update: RNDr. Nataša Šebková, Ph.D. (24.10.2019)

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.

Literature -

Last update: RNDr. Nataša Šebková, Ph.D. (24.10.2019)

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

(PDF file will be distributed for free.)

Requirements to the exam -

Last update: RNDr. Nataša Šebková, Ph.D. (24.10.2019)

Project assignments 80%

Final project presentation 20%

Syllabus -

Last update: doc. RNDr. František Půta, CSc. (05.02.2019)

Single-cell RNA-seq data analysis

Summer 2019

Charles University

Course Description:

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