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Messages - Jan Stanstrup

22
XCMS / Re: Consequences of using Centwave for profile mode data
I would ask for here complete settings at this point and see if you can replicate at all. the prefilter setting in particular might explain how she was able to run the analysis at all. The grouping step might also have put humpty dumpty somewhat back together again but you should see a huge npeaks number in the peak table. a positive "mzdiff" I guess also might join things up to look sensible on the surface.
23
XCMS - FAQ / Re: XCMS - MS1 scans empty
You could try with --simAsSpectra  --srmAsSpectra. Sometimes QQQ data is saved as chromatograms and not spectra.
But I am not understanding what you are doing. You are actually doing a parent ion scan? So then you do have MS2 data. Why expect MS1 data in your setup?
24
XCMS - FAQ / Re: XCMS - MS1 scans empty
You are using a Q-TOF in MS1 mode? For example a QQQ might save the data in a way that won't work in XCMS without using different conversion settings.

You can also open the converted data in MzMine or seeMS (part of ProteoWizard) and see what data is there.
25
XCMS / Re: Consequences of using Centwave for profile mode data
Gusses of what will happen:
* She uses a wide mz window in peak picking to get the whole peak inside. In effect treating it like it has much less resolution
* She uses a normal mz window (ppm) and each peak is split in many pieces. Each real peak is represented by many features in her feature table. Probably there is many more features than you would expect.

It is hard to imagine you could do this without noticing. Perhaps they put the raw data up but forgot to say that they centroided?
26
XCMS / Re: Side/ Partial Peak artifacts
For the orbitrap like shoulder peaks I wrote a filter, xcmsRaw.orbifilter, you can find here https://gitlab.com/R_packages/chemhelper.
It runs through each scan in a file and starts with the largest mass and eliminates everything in a set mz range around that peak that is smaller than some fraction of the main peak.
It runs through all peaks in the scan until all peaks have been "evaluated".

Currently you'd need to do this on all the raw files and write them out to a new set of raw files.


As for the chromatographic side peaks I am not sure you can fix that with parameters (apart from the grouping you already mentioned). Also in your plots they look like to me to be legitimate additional small peaks so I don't see what the peak picker should be doing differently. You have fronting and tailing peaks and I guess that will always be problematic.
Your ~447 peak is very noisy. If your data in general looks like that the matchedfilter algorithm could give you better results. It generally is more robust to noisy data.
27
Courses and training / [COURSE] Introduction to Nutritional Metabolomics
Course dates
01 July 2019 - 05 July 2019

Place: Copenhagen

Info, sign-up, program: https://phdcourses.dk/Course/65906 

Content
The course will provide an overview on LC-MS based untargeted metabolomics and its application in nutrition. It will be delivered using a mixture of lectures, hands-on data preparation and analysis, computer-based practical sessions, and discussions. Visits to wet labs and instructions on human sample preparation procedures is included but with minimal hands-on.

The students will go through common steps in a typical metabolomics study using a real-life case. This case study includes collected plasma (or urine) samples from a nutritional intervention. The sample preparation and analysis on UPLC-QTOF has been conducted and the students will further process and analyse the acquired data with various free-ware tools (MZmine, Workflow4Metabolomics and Metaboanalyst). They will finally work on identification of relevant metabolites using several web-based structure elucidation tools. The course will finalize by presentations of reports generated by the students based on the case study.

The course will be structured as initial short lectures on theory followed by hands-on exercises which will teach the students to transfer the theoretical information to practice.

29
Other / Re: Peak alignment with large dataset (over 2500 samples and growing)
Yes the loadings do suggest that. But since you don't see that in your boxplots I think the pattern is too complex to appreciate there. So I think you need to look at some individual compounds. The corrections methods always work in each feature individually for the same reason.
30
Other / Re: Peak alignment with large dataset (over 2500 samples and growing)
I did not know you had done Obiwarp. If it works well then it could be fine.

Looking at intensity distributions IMO won't tell you enough. As illustrated in the Brunius paper features behaves differently and some remain constant. I would look at a few compounds I know and see how they behave across your batches.

Looking at the loadings of your PCA might also give you a clue. You should be able to see if your batch difference is dominated by a few features or it is all features moving in one direction.