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11
XCMS / Re: sequential addition of files to an xcms object
Last post by CoreyG -
Thanks Johannes, I'll definitely be checking this out.
We use data from blank+ISTD samples in our targeted metabolomics workflow extensively.
12
Hello Adiana,

Have you figured out how to extract the MS/MS from the result table? Thanks

Jie

Hi, Im trying to figure our how can I extract the MS/MS data in table from XCMS online,

any help?

Thanks

Adriana
13
XCMS Online / Re: About the MSMS matching through XCMS online
Last post by lijied -
Hi James,

I have the same issue as you. I tried to select the MS/MS column in the result table but unfortunately, I couldn't.  I guess this is the way to finding the MS/MS matching. Please let me know if you figure out how to do it. Thank you.

Jie

Hi, I don't seem to be able to change the columns in the results table. I have tried chrome, firefox, IE and edge. Any suggestions? I am trying to look at the MSMS matches, but can't access them as per the regular process.

Kind regards,

James
14
XCMS Online / Re: About the MSMS matching through XCMS online
Last post by lijied -
Hi Elena,

Thanks for the suggestions. I will read the paper to see if I can find any information related to my issue.

Jie

Hi,

I am not a user of XCMS so unfortunately I don't have the answer but maybe you'll find some help on the scripps website: https://xcmsonline.scripps.edu/landing_page.php?pgcontent=documentation

or in this paper: https://pdfs.semanticscholar.org/6901/570083ecd16ceaf296de88819f0105d309aa.pdf

Hi,

I am not a user of XCMS so unfortunately I don't have the answer but maybe you'll find some help on the scripps website: https://xcmsonline.scripps.edu/landing_page.php?pgcontent=documentation

or in this paper: https://pdfs.semanticscholar.org/6901/570083ecd16ceaf296de88819f0105d309aa.pdf

15
Tools / Re: Data from waters - mass measure in centroid mode
Last post by johannes.rainer -
We have AB Sciex data and so far I used proteowizard centroiding (with vendor option). This turned out to be a poor choice. What I am doing now is to export all data from Sciex as mzML in profile mode and perform the centroiding in R using MSnbase.

Have also a look at https://github.com/jotsetung/metabolomics2018 where I describe the centroiding with MSnbase (specifically https://jotsetung.github.io/metabolomics2018/xcms-preprocessing.html#23_centroiding_of_profile_ms_data).
16
Just chiming in with some explanations how xcms works now with large projects. We use now an object from the MSnbase package to represent the raw data, that only reads the raw (spectrum) data if required. That way the memory use is minimized. Peak detection is then performed on a per-file basis, i.e. reading the full data from one file, performing the peak detection on that and then removing the spectrum data again from memory. As Jan mentioned, you should be careful to not have too many parallel processes running, as the I/O will be the bottleneck, not the number of CPUs. On our cluster I use not more than 24 CPUs in parallel (although we have 292) because otherwise I got serious troubles with the I/O (this is most likely because our disk setup is ... suboptimal).

Just have a look at a recent xcms vignette (R 3.5.1, Bioconductor 3.8) how to perform the analysis. xcms uses by default this "onDisk" mode.

cheers, jo
17
XCMS Online / Re: About the MSMS matching through XCMS online
Last post by jamesabroadbent -
Hi, I don't seem to be able to change the columns in the results table. I have tried chrome, firefox, IE and edge. Any suggestions? I am trying to look at the MSMS matches, but can't access them as per the regular process.

Kind regards,

James
18
XCMS Online / Re: About the MSMS matching through XCMS online
Last post by Elena Legrand -
Hi,

I am not a user of XCMS so unfortunately I don't have the answer but maybe you'll find some help on the scripps website: https://xcmsonline.scripps.edu/landing_page.php?pgcontent=documentation

or in this paper: https://pdfs.semanticscholar.org/6901/570083ecd16ceaf296de88819f0105d309aa.pdf
19
Hi, as a workaround I usually split big data sets into subsets (~250 runs) to process them independently using XCMS. Then I use a linear or non-linear (rsc, svr,..) fitting of the shift in the retention time using 'known' metabolites to match variables across peak tables. As said, it's just a workaround but you can process each subset in parallel and reduce (a lot) the computing time and memory needed.
g
ps. if the raw data are already pretty well aligned, peak tables can be aligned using m/z & RT tolerances and a 'master-slave' approach in matlab/R/python/etc
20
MZMine is know to use a lot of memory. I imagine that is where your bottleneck is. But you should check that.

XCMS is much more memory efficient. Be aware that each core will use a certain amount of memory. So on a system like yours not using all cores will use less memory and might save you if memory is your bottleneck. Also don't use 80 cores on processes that are bottlenecked by HDD reads (like reading the raw data).

That said, with 10,000 samples you really need to be careful about how greedy you need to be in terms of how low in intensity you want to pick.