This section allows you to view all posts made by this member. Note that you can only see posts made in areas you currently have access to.
Messages - Jan Stanstrup
In my dataset I get features > 1000 with no problems. Are you sure your data contains data above 1000 Da?
If you are sure your data has peaks > 1000 Da your best bet is to post a reproducible example with code and sample data.
No clue. The first I would try is to install xcms (devel branch) and mzR from github. Your versions are a bit old and might not like each other.
The last webinar "Machine learning powered metabolomic network analysis" by Dr. Dmitry Grapov is now online.
I haven't tried to manipulate it like that so I don't know.
But your peakwidth is too narrow I think if you want all in one. You have a max of 10 sec. If you want to merge all you need several minutes I assume. At least I'd try that.
One way to achieve this, I think, is to set mzdiff to something positive. That should make everything connected integrate as one. Of course this might have unwanted effects in other places.
I think this is gonna be very difficult to achieve with so much overlap. What I would try is lowering the minimum peakwidth. If that doesn't work last resort is trying matchedfilter instead of centwave.
The recorded webinar is now online: http://metabolomicssociety.org/resources/videos/88-videos/258-2017-emn-webinars-public
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...