Tengo la misma duda, cómo se puede solucionar?

Hello there! Simulating the molecule of interest for your research is good to identify vibrational modes and have a good educative guess of how its spectrum will look like. If you already know this and want to explore variation, classification or regression, simulating might not be as relevant. However, I have used simulated spectra and manually changed its baseline and/or intensity to simulate artifacts. This helped me visualize why the scores and loadings plot behaved in specific ways. Hope this helps! - Edwin

Always makes me happy when it helps someone! In the following link I started writing R scripts for SNV, MSC, and normalization. May not be optimal but they will give you a good start. www.uprm.edu/ccs-cicsa/files-info-for-research/r-language-resources/ Have a great day! - Edwin

you dont need R that much.There's a Git link in which the codes for all these are already given

Thank you for your kind words. Hope you are doing well. Mean centering is used to remove the common information on your data. Chemometrics assumes that variation implies information, hence why mean centering is so useful. When using PCA or PLS, mean centering the data allows the average variation between samples to be placed on the origin (0,0) of the scores plot. Auto scaling is usually used to leave the mean at zero and the standard deviation at one. From what I've understood, you scale variance when the variables that are being analyzed have very different magnitudes. This make some variables overshadow others, hence dividing the std allows them to play on an even field. Hope this gave some insight. Regards! - Edwin

Hello! Thank you for your kind words. The choice depends on what I (Edwin) want to study with my spectra. For presentation I would use baseline correction and SNV to maintain as much as the original shape of the spectra as possible. For creating models, I usually use 12 different combinations of DP methods. Use SNV, MSC, SG1, and SG2 separately and then combine them in different orders (SNV+SG1, SG1+SNV, MSC+SG1, etc.). Once I have a matrix with each different DP method, I create a model for each different matrix. This way I can determine which combination gave the most optimal results. However if the artifact can be seen clear as day on the data you can simply use the DP method that best reduces the variation. Hope this helps! We are welcomed to any suggestions and/or corrections. Hope you have a great day!

Application of compositional data analysis to proteomics data.

This was a very helpfull lecture however It would be interesting to run code (real time) on r and comment the results of the outputs with real data.

@@claudiaazevedo4073 Thank you for your feedback! A great idea, I will develop a webinar or workshop in the future. - Edwin

This is a very helpful video, but I have a question, which is the difference between PLS DA and SIMCA? I mean, when should I use each method? Thanks

Nos alegra escuchar eso Mucho éxito en su trabajo!

Me gustaría que siguieran compartiendo más videos de esto

Thank you for the feedback! Hope you have a great day