Educators:
Michael Turewicz (bioinformatician) and Karin Schork (biostatistician) (BioInfra.Prot)

Date:
Monday, 9th Nov 2020

Location:
Online

Contents:
In this course you will learn about using R for the analysis of proteomics data. We will focus on data preprocessing methods and advanced methods for data analysis. In this regard the cpurse will touch upon:
• data normalization
• quality control, handling of missing values
• clustering, heatmaps
• ROC-curves

Please be aware that basic knowledge of R and methods for differential analysis of proteomics data are taught in our course “Differential analysis of quantitative proteomics data” the previous day (Monday, 2nd Nov 2020, http://goo.gl/forms/mpKHnbT1Um)

Learning goals:
• Independent usage of R functions for
- Data preprocessing
- Plots and graphs
- Statistical methods for data analysis
- Use of additional R packages
• Deeper understanding of statistical methods applied in differential analyses

Prerequisites:
• Basic understanding of high-dimensional data sets from quantitative proteomics or other life sciences;
• Basic knowledge of R (e.g. data import, basic plots, t-test, for loop) and basic knowledge of differential analysis of proteomics data. Both can for example be gained from our course “Differential analysis of quantitative proteomics data” the previous day (Monday, 2nd Nov 2020, http://goo.gl/forms/mpKHnbT1Um).
• Computer with stable internet connection, headset and camera

Keywords:
R; high-throughput data; omics; proteomics; data analysis, graphics, data preprocessing

Tools:
download and more information on R here:
https://cran.r-project.org/

We recommend using an editor such as RStudio, see
www.rstudio.com