Workshops

Chemical and biological data analysis in R for beginners

• elementary matrix algebra
• basic statistics
  • t-test, analysis of variance (ANOVA)
  • Tukey’s HSD test (honest significant difference test)
• correlations
• clustering analysis
• data visualization

Multivariate data analysis in R

• Principal component analysis as an exploratory data analysis (PCA)
• Calibration/regression models
  • linear regression (OLR)
  • multiple linear regression (MLR)
  • principal component regression (PCR)
  • partial least squares regression (PLSR)
• Discrimination and classification
  • partial least squares regression – discriminant analysis (PLSDA)
  • linear discriminant analysis (LDA)
  • soft independent modeling of class analogy (SIMCA)
  • likelihood ratio framework (LR)

Chromatographic data processing

• peaks integration
• normalization
• retention time normalization
• multisample set alignment
• visualization

LC-MS, GC-MS data processing

• possible LC-MS and GC-MS approaches
• software for data processing
• processing pipeline
• post-processing metod
• data visualization

Metabolomic and proteomic data processing and analysis

• introduction to metabolomics and proteomic from experimental design to results visualization challenges in omics
• data processing: reviewing the software, online platforms
• pipeline of data processing
• data post-processing: reviewing the software and online platforms
• high-dimensionality reduction and data visualization
• univariate and multivariate data analysis
• metabolite identification and metabolomics data bases reviewing