I have checked through the diff reports and the highest P-value for feature lowest ranked feature in each subject is 0.98 or so. There are no 0 values? The highest ranked common feature is 166.021416 m/z with the following results: Subject A (control vs. intervention) UP(fc=3.7,pval=0), Subject B (control vs. intervention) UP(fc=2.3,pval=0.00018), Subject C (control vs. intervention) UP(fc=3.1,pval=0). I have checked for this feature in each individual diff report and I get the following p-values Subject A (p=5.77E-07), Subject B (p=0.000184097), Subject C (p=2.51E-06). Any idea why I might be getting zero values like this in the common features? I am filtering by 0.01 p-value, 2 fold change, then m/z tolerance of 0.005 and an RT tolerance of 20 seconds? Don't know if this clarifies it is not in all of the common features just some.
I am observing a p-value of zero in my metaXCMS common features? It is obviously incorrect to report this p-value..Is there a certain number of decimal places which metaXCMS reports? Are these values indeed significant or not? Has anyone else seen this?
Another thing I wish to do is to reimport the detailed common features diff report of metaXCMS back into R that I can annotate it using CAMERA. Is this possible? One way I have tried to annotate the mass spectral features is by using the annotateDiffreport wrapper function prior to generation of the diff report for metaXCMS as part of the workflow. This also usefully calculates a potential monoisotopic mass for each mass spectral feature rather than a m/z range. It would of course be more useful to be able to annotate the metaXCMS common features and to obtain estimated monoisotopic masses as I want to assess the mass accuracy aswell.
It is possible to annotate adducts and isotopes utilising the Agilent Qualitative software and to perform a user-generated monoisotopic mass database search however I would like to be able to perform the same analyses using XCMS/metaXCMS if possible for the purposes of comparison. I prefer to conduct my multivariate analyses in SIMCA-P+ rather than Mass Profiler Pro (partly as I am more fimiliar with the former and also because I feel MPP is not very transparent and limited in it's versatility). I don't know if anyone else has had the same thoughts or encountered similar difficulties.
I am using metaXCMS for human urine samples where there are 3 subjects and 2 conditions (fed and unfed) with 3 replicate injections and I need to normalise appropriately to account for sample dilution between subjects. It would be good to normalise across all of the samples together to the median fold change and this would usually be following peakpicking. However if I am treating each subject individually I cannot easily do this, perhaps there is a way as I don't want any false negatives/positives. I don't know how I could do this when I am generating a diffreport seperately for each individual? I read through the nature protocols paper and could not find any suggestions on fitting in a normalisation step. I have already used metaXCMS and it is working very well and some interesting features have arisen based on monoisotopic mass but I want to ensure my interpretation is accurate.
I am considering in future to normalise to osmolality and correcting each sample prior to data acquistion in future however for now does anyone have any suggestions of how I can normalise these urine samples so that I don't misintepret the second order analyses with metaXCMS and fit it into the work scheme?
Also does anyone have any suggestions on how I can search my own database or the soon to be released FooDB when generating the XCMS diff report instead of/ in addition to Metlin?
Thanks Jan I tried that forum advice and the --max-mem-size=6000M (as I believe Windows reserves 2GB for itself) seemed to work however the --max-vsize command in the 'target' box was not compatible with the current version of R. I think better understanding of the use of R, good habits and switching to a different operating system is the best way forward particularly for larger datasets I am acquiring. Thanks for your help.
Thanks for your advice, I have tried saving as seperate objects and increasing the memory limit with no luck. Is there also a way to increase the maximum allowable vector size? I read that it is possible to do this in the target field in the R shortcut but it does not seem to work either. I also tried updating my copy of R to 2.15 it was previously 2.14.2 (both 64 bit). I have noticed in many threads here that R does not run as well on windows I will see if an alternative operating system can be installed on the server with our IT, if you think that may solve the problem.
I am trying to annotate adducts using CAMERA from urinary data acquired using an UPLC-Agilent 'ifunnel' QTOF 6550 in centroid mode (converted to mzXML using msconvert)and I am having difficulty grouping correlated peaks with the groupCorr algorithm. I would really appreciate any help as I am a little bit stuck. Sorry for all the information but I thought it best to provide as much as possible.
I recieve this error message and believe it might be due to my memory usage: -
With 1028 groups (pseudospectra) With 18 samples and 62755 peaks Polarity mode is set to: Using automatic sample selection Annotated isotopes: 8739 Memory usage: 177 MB
Prior to this > xsF <- groupFWHM(xsa, perfwhm = 0.6) Start grouping after retention time. Created 1028 pseudospectra. and.... xsI <- findIsotopes(xsF, mzabs = 0.01) Generating peak matrix! Run isotope peak annotation Found isotopes: 8739
I am using a virtual machine with the following characteristics and I hoped these would be sufficient
Windows 7 64 bit 8GB of RAM 2 CPUs 1 System disk of 40GB 1 Data disk of 1TB
I wonder if the number of peaks picked using XCMS is rather a lofty figure? I put this down to being very high resolution spectra, (as there were some peak insertion problems using ppm values of 30 and 20) long gradient and EICs from the diffreport appeared satisfactory, perhaps I have gone wrong somewhere with my peak picking, peak grouping and retention time correction prior to using CAMERA.
My XCMS workflow consisted of: -
>peakmatrix <- xcmsSet(method="centWave", peakwidth=c(3,20), ppm=15, snthresh=10); An "xcmsSet" object with 18 samples Time range: 3.4-1500.1 seconds (0.1-25 minutes) Mass range: 100.0022-1049.6504 m/z Peaks: 836770 (about 46487 per sample) Peak Groups: 0 Sample classes: A1, A2, B1, B2, J1, J2 Profile settings: method = bin step = 0.1 Memory usage: 72.3 MB
I have been having trouble with the centWave peak picker I continually get the following error message at the very end of the process? Error in checkForRemoteErrors(val) : 100 nodes produced errors; first error: m/z sort assumption violated ! (scan 611, p 3578, current 322.1408 (I=15.85), last 322.1423)
I have tried peak picking with each individual sub folder in turn to see if one of the folders contains a corrupt file or maybe something else but I still get the same error.
Using the default XCMS not centWave it seems to work fine strangely. I also converted my files using the Databridge programme to netCDF files if this is pertinent information.
This is the command I utilised I set the CPWmin, CPWmax and snthresh beforehand. >peakmatrix <- xcmsSet(method="centWave", peakwidth=c(CPWmin,CPWmax), ppm=ppm, snthresh=xsnthresh, nSlaves=8)
Apologies for back to back help requests I am new to R and using XCMS and I am not a computational biologist by any means. I wonder if anyone has any ideas?
Apologies I am just familiarising with XCMS "uploading" is purely a misunderstanding, the error messages can be rather cryptic for the uninitiated but there is a wealth of information available.
Hi I was wondering if anyone has come across this error before when uploading your cdf files. The upload commences and completes 50 or so files but always seems to halt on this particular file? I'm puzzled. Thanks for your help.
ASP_P_P2_5901: Traceback: 1: .C("ProfBinM", x, y, as.integer(length(x)), as.integer(zidx), as.integer(length(zidx)), as.double(xstart), as.double(xend), as.integer(num), out = doubleMatrix(num, length(zidx)), NAOK = NAOK, DUP = FALSE, PACKAGE = "xcms") 2: profFun(object@env$mz, object@env$intensity, object@scanindex, bufsize, mass[1], mass[bufsize], TRUE, object@profparam) 3: .local(object, ...) 4: findPeaks.matchedFilter(<S4 object of class "xcmsRaw">) 5: findPeaks.matchedFilter(<S4 object of class "xcmsRaw">) 6: do.call(method, list(object, ...)) 7: .local(object, ...) 8: findPeaks(lcraw, ...) 9: findPeaks(lcraw, ...) 10: xcmsSet() Error in profFun(object@env$mz, object@env$intensity, object@scanindex, : caught access violation - continue with care In addition: Warning message: In xcmsRaw(files, profmethod = profmethod, profparam = profparam, : There are identical scantimes.
