Course structure and aims

Each lecture will introduce one topic in 5 - 10 min. The lectures will focus on the big picture and will conceptually outline the major ideas and approaches while also providing references to resources offering more comprehensive discussion.
Lectures are avilable at: slavovlab.net/stats and from the YouTube playlist: stats.slavovlab.net

Topics

• Systematic errors and biases
• Accuracy vs. Precision
• Estimating statistical significance
• Types of missing data
• Non ignorable missing data
• Imputation
• Protein set enrichment analysis
• PSM-level statistics
• Proteoforms and protein inference
• PEP and FDR
• Data Normalization
• Batch correction
• Propagating errors
• Uncertainty analysis
• Linear models
• Power analysis & sample size
• A receiver operating characteristic curve
• Suggest topics

Slides template