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Messages - Jan Stanstrup
This topic has been moved to Courses and training
When: Sept 25-27 2017
Where: Vorau, Austria
To register and for more information, go to www.MOVISS.eu, and follow us on Twitter @MOVISSmeet
We would like to invite you to Bio&Data, the first workshop of the newly established MOVISS - "Mountain Village Science Series" taking place in Vorau, Austria (Sep 20-23, 2017). MOVISS Bio&Data is different to the usual conferences. It is rather constructed as a small, problem-driven meeting, full of discussions and questions about how to deal with metabolomics data reasonably. In this way, we hope to constructively engage some of the greatest minds collaboratively in solving some of the challenges of the metabolomics and bioinformatics community.
Four sessions are planned, each devoted to a separate step of the metabolomics process; Design of Experiments, Analytical Analysis, Data Processing and Statistical Analysis in the biosciences will all be discussed including your data if you bring them for discussions.
We plan a summary of this discussion will be produced as a paper for publication to share within the wider metabolomics community. Finally, you can continue with the R Summer School from September 25 - 27, 2017 in Vorau!
The 10th Metabomeeting will be held at the University of Birmingham in the UK on the 11-13th December 2017.
The deadline for oral presentation abstracts is 15th July 2017
The deadline for poster abstracts is 1st October 2017.
The meeting agenda will be available on the conference web-site and will include sessions on some or all of the following:
I have not been aware of intf since I am normally using centwave that doesn't have this concept. I am using into because when I compared to intb the CAMERA grouping was better with into suggesting that was a more stable measure.
As for intf vs into I cannot answer but I guess it depends how good the fitted model is.
pic3 looks like shoulder peaks. But difficult to see when I only have that zoom level.
What you need to do is take one of the largest peaks, look at the spectra, zoom in around the mass peak at low intensity. If you see a lot of small peaks (1-5%) around the real peak --> that is shoulder peaks and you need to filter them before an analysis.
0.0005 sounds too narrow to be to compare peak tables. 0.0025 seems more reasonable. The tools might choose the mass differently (at apex, mean/median across the peak, mean/median across samples).
16238 seems like a lot of features. Suggests to me something is up. Could be if there are many shoulder peaks, if you detect a lot of noise, or chop up peaks like mzmine did.
I am not familiar enough mzmine to tell you what to tweak but it makes sense if the other tools give you 1 or 2 peaks for that noisy peak.
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.
I think the XCMS online people rarely come by here so you might consider contacting them directly: https://xcmsonline.scripps.edu/landing_page.php?pgcontent=contact
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 assume these examples are not from the same file?
Also how did you compare the masses? Which tolerance? Surely there must be some overlap.
The warning about profile mode might be due to too low ppm setting or orbitrap shoulder peaks: http://www.metabolomics-forum.com/index.php?topic=1044.msg3019#msg3019
You are getting the inserting error thing? If just few you can ignore.
If you don't wanna try to install the package you could do:
In that case you'd have to decipher the help page from here: https://github.com/stanstrup/chemhelper/blob/master/man/analyze.xcms.group.Rd
I think there is a number of newer functions for useful visuals but I have not used them yet.
For the grouping step I have written a function I find useful: http://www.metabolomics-forum.com/index.php?topic=577.msg1789#msg1789
You can also see where peaks where found with that.
I am having some dependency problems with my package so it might be easier to get the function (analyze.xcms.group) directly from: https://github.com/stanstrup/chemhelper/blob/master/R/xcms_helpers.R
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
The EMN's next webinar is next week.
Click here to learn more about this latest webinar by Dr. Stephan Hann.
Please register for "How well do I quantify? Concepts for method validation and evaluation of measurement uncertainty in metabolomics" to be held on February 01st, 2017 14:00 UTC/14:00 GMT at:
I wrote a function that can be helpful to try to debug this: http://www.metabolomics-forum.com/index.php?topic=577.msg1789#msg1789
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.