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Messages - Jan Stanstrup

151
Announcements / Announcing new forum
Dear members of the Metabolomics Society and users of the metabolomics forums,

The Website and Communications Committee, of the Metabolomics Society is excited to announce that we have now merged and updated the previous version of the metabolomics-forum.com and the Metabolomics Society’s forums (previously interest-groups.metabolomicssociety.org).
We hope that this new forum will re-ignite vibrant discussions on all things metabolomics. We have packed the new forum with new features to inspire just that. Please read below in the next post, if you want to know more about some of the new features. Please give us feedback and comments for improvement, all suggestion are welcomed. Please use the site related forum for suggestions and bug reports.

Old forum profiles
Because we merged the two old forums into a whole new forum there might have been some changes to your old user account. Following changes has been implemented:
we merged some accounts with the same name (or user ID) to avoid duplicate accounts.
In all cases the password from metabolomics-forum.com was used for the new account.
Please check your previous usernames in the user list and let me know if your accounts were not merged.

To avoid carrying over a lot of spam accounts, we have deleted all accounts with zero posts. Please feel free to create a new account if yours was deleted, our apologies for that there was no easy way to “separate the wheat from the chaff“.
For users moving over from the Metabolomics Society forum, you might have to reset your password to be able to login.
153
XCMS / Re: filter scans by collision energy
It seems the "special MS1" has been saved as MSn data in the file. But you also have regular MS1 in the file. the MS1 does not have CollisionEnergy information AFAIK.
So I think you will get what you want if you just don't read the MSn data and treat them normally.
Perhaps do some EICs and compare to your vendor software to check that things are as expected.
154
XCMS / Re: "object 'xset' not found"
I'm really not sure what you are trying to accomplish...
Seems like you are trying to do one subset at a time and merge with c. I don't see how that could help. XCMS does one file at a time anyway (unless using multithreading, nSlaves).

Forgetting the question of whether this makes sense if you don't want to keep all the data somewhere until after the loop you need to merge inside the loop. So something like when i=1 assign to xset. When it is not 1 assign to new_xset and do xset <- c(xset,new_xset).

1 GB is a lot for a data file. Are you sure it is in centroid mode?
155
XCMS / Re: Getting peaks filled in that don't seem to be there...
I have the same problem often too and no general solution. XCMS seems to be a bit "too good" at finding peaks in many cases.
Have you tried raising the SNR and/or the prefilter count? If this is the typical noise level your prefilter looks very low. I have also found that real peaks usually survive quite a lot higher SNR.
157
Other / Re: Library construction
I am looking for the same thing.
I would like some kind of system where we can catalogue our standards (where are they, where were they bought, structure, uncertain structures) and associated spectra (cleaned spectra and original file).
It should also be possible to include spectra of unknowns and reference back to which file the spectra was taken from.

Does something like this exist?
158
XCMS / Re: fillPeaks deleting feature groups?
Code: [Select]
library(faahKO)
library(xcms)

xs_dens <- group(faahko,method="density")
xs_near <- group(faahko,method="nearest")

xs_filled_dens <- fillPeaks(xs_dens)
xs_filled_near <- fillPeaks(xs_near)

Code: [Select]
> xs_dens
An "xcmsSet" object with 12 samples

Time range: 2506.1-4147.7 seconds (41.8-69.1 minutes)
Mass range: 200.1-599.3338 m/z
Peaks: 4776 (about 398 per sample)
Peak Groups: 407 
Sample classes: KO, WT 

Profile settings: method = bin
                  step = 0.1

Memory usage: 0.709 MB
> xs_filled_dens
An "xcmsSet" object with 12 samples

Time range: 2502.9-4150.8 seconds (41.7-69.2 minutes)
Mass range: 200.1-599.3338 m/z
Peaks: 6121 (about 510 per sample)
Peak Groups: 407 
Sample classes: KO, WT 

Profile settings: method = bin
                  step = 0.1

Memory usage: 0.831 MB
> 
> xs_near
An "xcmsSet" object with 12 samples

Time range: 2506.1-4147.7 seconds (41.8-69.1 minutes)
Mass range: 200.1-599.3338 m/z
Peaks: 4776 (about 398 per sample)
Peak Groups: 1496 
Sample classes: KO, WT 

Profile settings: method = bin
                  step = 0.1

Memory usage: 0.86 MB
> xs_filled_near
An "xcmsSet" object with 12 samples

Time range: 2501.4-4150.8 seconds (41.7-69.2 minutes)
Mass range: 200.1-599.4 m/z
Peaks: 17952 (about 1496 per sample)
Peak Groups: 1496 
Sample classes: KO, WT 

Profile settings: method = bin
                  step = 0.1

Memory usage: 2.16 MB


Not sure if we are talking about the same thing but the number of peak groups seem to stay constant in this example.
159
XCMS / Re: Extracting blank samples in a discovery batch
You only really need to use the data grouping if you want to use the stats part of XCMS. As for what is best to do with group I am not that sure myself. Personally I usually don't group the data and just do the kind of calculations you have done.

Some thoughts to consider:
If you expect your groups to be very different then it might be easier to do the alignment/grouping sensibly by grouping the data.
Also, if for example you have many groups and you do the calculations you did you might have a very low threshold for inclusion that could let a lot of noise in.
I would recommend not processing your sample with your blanks if possible as they often cause a lot of trouble for the alignment (since so few features are there).

Quote
I suppose you would want to set the minfrac = # samples within smallest group / # of total samples.
Yes, but putting it a bit lower to allow it to miss a few in smallest group too.

Quote
include the blanks as a group and use 1/12 (round down to 0.15)?
But if you do that it would also only need to be found within 15% of any of the samples which might be a bit too liberal. minfrac and minsamp should work as an AND so you could also just set minfrac low and use minsamp to control what happens in the "real groups" that might be easier to do sensibly without grouping the data. If you have similar sized groups that is. Otherwise grouping the data starts to make most sense.

Quote
Or are you suggesting instead to extract samples per condition-group, so you'd extract the 5 cases, 5 controls and 2 blanks as 3 data matrices. But then how to combine them...?
No I am not suggesting to process the datasets separately. Just for the grouping step you should consider if you want features that are only found in a subset or not. If you expect that you do you can either group the data and set minfrac and minsamp more strictly or if you don't group the data do the considerations/calculations you did.

In my mind it only really matters if you have very different groups so you'd expect some features to be absent from one group. Then you might be able to be a bit more strict (and thus get less noise) within each group than you would otherwise have to be with the considerations you just did.
Otherwise the calculations you did is how I would think. Just putting the requirements a bit lower to allow features to not have been found in exactly all samples within a group.
160
Mass spectrometers / Re: QTRAP parameters for lipidomics
I assume you mean chromatography etc? I suggest finding some of Fiehn's papers in lipidomics and using their system as a starting point.

Other than that it seems you have a nominal mass instrument? It will be very hard doing something sensible with that in lipidomics...
161
XCMS / Re: fillPeaks deleting feature groups?
About your post: I don't know what happened but it seems all old accounts were at some point in 2011 set to another user group. So your posts ended up as posts that needed to be approved. I have approved your posts and your account should be fixed too.

For you problem it will be a lot easier to figure out if you can post an example. FillPeaks should not remove anything. Why are you using "nearest" btw? Normally you'd use the default that is "density".
162
XCMS / Re: Overlapping EIC plots
Seems to work?
Can you produce an example where it does not?

Code: [Select]
library(xcms)
library(faahKO)

cdfpath <- file.path(find.package("faahKO"), "cdf")
files <- list.files(cdfpath, recursive = TRUE)

xraw <- xcmsRaw(paste0(cdfpath,"/",files[1]))
xraw2 <- xcmsRaw(paste0(cdfpath,"/",files[2]))

plotEIC(xraw, add=FALSE)
plotEIC(xraw2, add=FALSE)

plotEIC(xraw, add=FALSE)
plotEIC(xraw2, add=TRUE,col="red")
163
XCMS Online / Re: centwave for Centroided data
All the peak picking algorithms need centroided data. centwave works best for high resolution data so if you GCMS is nominal mass you might get better results with matchedfilter.
164
XCMS Online / Re: Restrict Retention Time
I don't use XCMS online but in XCMS you can do it indirectly by restricting the scan range. So you figure out what scan number correspond to the time you want and set that as the limit. Be aware that as far as I recall XCMS drops empty scans so the scan numbers you see in your vendor software might not correspond perfectly.
165
XCMS / Re: m/z value off by 0.1~0.2 Da
@hwhitwell Perhaps yoru version of proteowizard still doesn't do the conversion correctly? Have you tried my method using masswolf?

@metaboRap The best thing would be to post a reproducible example or at the very least the settings/script you used. Is the 738.5467 different from what the peak should be? Or just you find the range too large? Did you check your converted raw files? If this is related to the conversion problem you should see if the raw data has the right masses or not. The number of features can depends on a lot of parameters and is not a good measure of quality.