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Messages - Jan Stanstrup
06 July 2020 - 10 July 2020
Info, sign-up, program: https://phdcourses.ku.dk/detailkursus.aspx?id=107283&sitepath=NAT
The course will provide an overview of 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 freeware tools (e.g. R, XCMS, MZmine and Metaboanalyst). They will finally work on identification of relevant metabolites using several web-based structure elucidation tools. The course will conclude 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.
The most common tools for untargeted data would be XCMS+CAMERA or MZmine. There is nothing straight forward about doing this on untargeted data though.
If you have "paired samples" where you are not interested in the differences within the pairs it can reduce variance to put them in the same batch. For example if you have several persons you might consider having the same person in the same batch for all time course samples.
But as you say. Spread evenly conditions and randomize within the batch.
Use plenty of QC samples so that you afterwards can correct for potential intra- and inter-batch drifts.
You can try converting your original files with msconvert from Proteowizard and add: --simAsSpectra --srmAsSpectra
try something like this as a starting point:
missing = 200
extra = 100
If you are including very sparse samples, like artificial mixtures or blanks, you might need to go even higher.
span =1 is also very strict but should not affect the error.
xset1in the console it should tell you if there are any groups.
Do you mean that you get the same error when you do
xset2 <- retcor(xset1, family= "s", plottype= "m", missing=1, extra=1,as when you do
That would be very unusual I think.
@michael.witting has a tutorial on how to do something with MS2 data here: https://github.com/michaelwitting/metabolomics2018/blob/master/XCMS_Witting.pdf
I am not sure how complete the XCMS features are on this but you can find moreon the work here: https://github.com/sneumann/xcms/issues/321
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?
You can also open the converted data in MzMine or seeMS (part of ProteoWizard) and see what data is there.