Gene Expression Data for Beginners

Online genome databases are rapidly expanding and are already central to the biological and medical research. Such massive amount of information requires basic skills of how to find the data of interest and what to do with those data. The course will include exercises with data retrieval, processing, and basic elements of analysis. In this course we will first explain how to use R programming language for such tasks. Next, 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, gene ontology  tools will be covered. Considerable part of the time will be spent on exercises with R-scripts and data analysis. It is expected that students will be active in repeating script lines during the sessions and at home. Use of own data is welcomed.

•       Understanding the sources of gene expression data and approaches for data analysis
•       Understanding the principles of programming in R, learning basics of using and writing scripts in R.
•       Brief introduction to packages for gene expression data analysis.
•       Understanding basics of the experimental design and statistical robustness of the data sets.

Good understanding of molecular and cell biology. Having at least some experience with programming is helpful, but not strictly required. For absolute beginners: please sign up for the preceding this course “Very Quick R” to learn elementary basics of R programming.

Personal laptop with at least 8 GM RAM, any recent OS: Mac, Linux, or Windows10. Try to install recent version of R (with R-studio), not older than 1 year. If failed, it will be explained in the first session.

 No. Useful links for self-improvement will be recommended during the course.

99% participation required. Final evaluation is mostly based on successful homework. Expect homework after each seminar for 3 hours each.