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              - Introduction to Metabolomics: Experimental Design and Standards
 - 
                                
                                1. Metabolomics: a brief introduction 1
- Dr. Reza Salek
 
 - 
                                
                                2. Metabolomics: a brief introduction 2
- Dr. Reza Salek
 
 - The Theoretical Basis of Metabolomics Data Analysis
 - 
                                
                                6. Analysis of LCMS-based untargeted metabolomics data 1
- Dr. Pietro Franceschi
 
 - 
                                
                                7. Analysis of LCMS-based untargeted metabolomics data 2
- Dr. Pietro Franceschi
 
 - 
                                
                                8. NMR workflow from data processing to metabolite identification
- Dr. Gwénaëlle Le Gall
 
 - Statistical Data Analysis in Metabolomics
 - 
                                
                                9. Univariate statistics and metabolomics
- Dr. Ron Wehrens
 
 - 
                                
                                10. Multivariate statistics and metabolomics
- Dr. Ron Wehrens
 
 - 
                                
                                11. Data fusion: examples in fusing metabolomics and transcriptomics data
- Dr. Johan A. Westerhuis
 
 - Metabolomics Resources and Computational Tools
 - 
                                
                                12. Metabolomics resources
- Prof. David Wishart
 
 - 
                                
                                13. FAIR data in metabolomics
- Prof. Dr. Christoph Steinbeck
 
 - 
                                
                                14. MassBank and RMassBank
- Dr. Emma L. Schymanski
 
 - Metabolic Networks and Pathways
 - 
                                
                                15. Metabolomics data analysis in the context of metabolic networks
- Dr. Fabien Jourdan
 
 - 
                                
                                16. Metabolomics flux introduction
- Prof. Marta Cascante
 - Dr. Igor Marín de Mas
 
 - 
                                
                                17. Metabolomics flux on the genome scale
- Prof. Marta Cascante
 - Dr. Igor Marín de Mas
 
 
Printable Handouts
Navigable Slide Index
- Introduction
 - Eye-ball your data!
 - R session: visualization methods
 - More than two dimensions
 - Principal component analysis and visualization
 - Matrix decomposition with SVD
 - Matrix dimensions
 - Example data: Italian wines
 - PCA score plots
 - Loading plots - I
 - Loading plots - II
 - Biplots
 - Scaling
 - How many components in a PCA? scree plots
 - R session: PCA
 - PCA QC example I
 - PCA QC example II - batch correction
 - Finding differences between groups - t tests
 - Finding differences between groups - PCA
 - PCA on the spiked-apple data
 - Finding differences between groups - PLSDA
 - Fitting a PLS(DA) model
 - Interpretation of PLS models - spike-in data
 - Alternatives
 - R session: PLS
 - Data fusion
 - Useful R packages
 - Conclusions
 - Acknowledgements
 
Topics Covered
- Principal Component Analysis (PCA)
 - PLS and PLSDA
 - Class discrimination
 - Variable selection
 
Links
Series:
Categories:
Therapeutic Areas:
Talk Citation
Wehrens, R. (2023, July 6). Multivariate statistics and metabolomics [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved November 4, 2025, from https://doi.org/10.69645/ULUZ4936.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Ron Wehrens has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
 
A selection of talks on Methods
Transcript
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                  0:00
                
                
                  
                    Hello, my name is Ron Wehrens.
                  
                    I am a researcher at Wageningen University and Research at the Biometrics Department.
                  
                    And today I'm going to talk about "Multivariate Statistics in the Context of Metabolomics".
                  
                
              
                  0:14
                
                
                  
                    So, whenever you do data analysis,
                  
                    it's really important to look at your data and
                  
                    to visualize them in several different ways.
                  
                    This is also discussed in the lecture on univariate statistics.
                  
                    And one of the reasons for doing this is that
                  
                    the human mind is a very good pattern recognizer,
                  
                    so we are able to see patterns that are very hard to pick up by automated methods.
                  
                    So things that we might pick up are outliers,
                  
                    or we might see relationships between variables.
                  
                    And we might also assess whether the plots that
                  
                    we see confirm our expectations on the data.
                  
                    So, one of the key elements of doing
                  
                    graphics is trying to visualize information in each graph.
                  
                    And you can do that in a good way and in a bad way.
                  
                    A very good reference on making good graphics is the book by Bill Cleveland,
                  
                    already more than 20 years old, "Visualizing Data".
                  
                    And I would recommend anyone to pick up a copy at the library and look at it.
                  
                
              
                  1:09
                
                
                  
                    R is one of
                  
                    the most popular data analysis programs
                  
                    currently around and it is also the program that we are using in this lecture series.
                  
                    This is a small exercise,
                  
                    showing you the power of some of
                  
                    the built-in visualization tools that are there in R. So,
                  
                    you can simply copy and paste the commands that are on
                  
                    this slide and take a break from this lecture,
                  
                    do the R session, do the examples,
                  
                    and when you're ready, come back to this lecture,
                  
                    and we can proceed.