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 course will touch upon:
• data normalization
• quality control, handling of missing values
• clustering, heatmaps
• ROC-curves
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 e-Learning course “Differential analysis of quantitative proteomics data”.
Download Training material here:
https://www.denbi.de/images/Training/eLearning/Material_Advanced_Rcourse.zip
Download and more information on R here:
https://cran.r-project.org/
We recommend using an editor such as RStudio, see
https://www.rstudio.com