Course: Gene Expression Data for Beginners

 

 

Summary: Online genome databases are rapidly expanding and are already central to the biological and medical research. Such massive amount of information needs basic understanding of how to find the data of interest and what to do with those data. In this course we will mostly focus on gene expression data, since they are the most commonly used in contemporary research and scientific publications. Students will practice with data retrieval mostly from GEO. We will practice with three types of data: expression microarrays, bulk RNAseq and single-cell RNAseq data. Students will learn the most typical protocols of data processing and statistical analysis for all three cases. Attention will be given to data normalization, filtering, annotation, and visualization. Optionally, elements of ChIPseq, as well as to genomic (promoter) motifs and gene ontology tools will be covered. Students will be introduced to basic programming in Python, R and few other script types. The course will include exercises with data retrieval, processing, and basic elements of analysis. Use of own data is welcomed.

 

Learning outcomes:  


                   ·  Understanding the sources of gene expression data and approaches for data analysis 


 

                   ·  Understanding the principles of R and Python packages use for genome data analysis. Basic knowledge of using and writing scripts in R and Python. 


 

                   ·  Understanding basics of the experimental design and statistical robustness of the data sets. 


 

        Assumed pre­knowledge 
A good understanding of molecular biology and genetics. Having experience with programming can be helpful, but is not required.

 

        Equipment 
 Personal laptop with 8 GM RAM, preferably Mac, Linux, or Windows10. R (with R-studio) and Python3 (installed as standalone or via Anaconda, optionally PyCharm) can be preinstalled, or will be installed during the course.

 

        Compulsory literature 
No 


 

        Recommended 
Articles and online resources will be recommended during the course 


        Exam
100% participation required, final assignment is mostly based on successful homework. Expect homework after each seminar for 3 hours each.

        Date, Time  Approximately 7 seminars 3 hours each, October-November.

EC (without exam)

1

Course coordinator

• Leonid Bystrykh, PhD
  

Language

English

Contact person

Back to listing