Are you looking for the peakTable function or am I misunderstanding what you are trying to do? If you use CAMERA the equivalent function is getPeaklist.
fillpeaks peaks are in @peaks yes.
I would think into is the most common.
I don't think there is a real benchmark available anywhere. Difficult to establish a ground truth. It is always a compromise.
As for the annotations the isotope annotation and adducts/fragments are two different steps and the adducts/fragments is the computationally intensive one they seem to have limited.
Of course another option would be using XCMS through R so you have control...
I have no clear idea of what can help this but my first attempts would be integrate=2 and trying matchedfilter. I have had better luck with matchedfilter getting it to integrate noisy data at all.
btw.: how did you get those plots?
Another thing. You are not showing your profparam but be sure to set it to something like "list(step=0.005)". Otherwise fillpeaks might integrate a too wide EIC slice. Another thing to consider here: https://github.com/sneumann/xcms/pull/3
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Can you make a minimum reproducible example? Is the warning from diffreport? I don't know what it tries to put in the row names. Searching the forum it seems this is an old problem. Best way to get it resolved is to make an example (+ the data) that can be reproduced and post on the xcms github issues page.
The warning however should not be a big deal as it is only about assigning row names. The warning says it simply gives up assigning row names.
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Content The course will provide an introduction to the LC-MS based nutritional metabolomics studies, aiming to cover key steps such as study design, sample collection and analysis, data handling methodologies and metabolite identification.
The students will go through common steps in a typical metabolomics study. Therefore, the major focus will be application of various free or commercial tools for data preprocessing, data analysis, and metabolite identification with computer based hands-on training using example datasets that will be provided to the students.
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First thing to do is ask yourself if you can explain why a sample is different.
Does the loadings give a hint? --> If all variables are on one side you have systematic sensitivity/concentration issues. Does sample analysis order help explain? Is it a well defined group of variables that cause the difference? What are they? Could they be contaminants?
Would you expect that there could be large differences between biological samples (e.g. different humans) or should they be much more similar than between treatments (e.g. different types of wine)?
I have not tried so I cannot answer definitively but I'd think so. Question is if the software you want to use afterwards understands it. I guess you have two "functions"? You could split those apart with the scanEvent filter. You'd need that filter also to get rid of the lockmass scans.
btw.: I had some MS/MS data today where I used the lockmassRefiner. That seemed to do something wrong. On the other hand it seemed that with my data I got the calibrated data which was not the case with previous versions. So for calibrated data compare with your vendor software to check that the peaks have the right mass.
I am not familiar with the normalization in xcms Online. That seems to be specific to their interface.
The other option is about which values to use. "into" is the area under the peaks, maxo is the height of the peaks. So it is not directly related to normalization I would assume. Just they are on the same tab. Normally you'd use "into".
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