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XCMS Online / Delete processing job
Last post by moises.guerrero -
Hello, I need some help please.
I submitted two jobs on Xcms online, but after two weeks, the % of processing remains on 0 %, so I am trying to delete this jobs
But, when I select the delete option, the jobs are not deleted, and only appears the text: Job(s) NOT deleted
Someone know how to delete this processing jobs?
XCMS Online / How to view a completed job
Last post by jigu9991 -
Hi all. I was wondering if someone could shed some light on my situation. I submitted a job, and afterwards I received emails saying "Job Queued", followed by "Job Complete". This is all well and good. However, when I go onto the XCMS online site (while logged into my account) and I click the "View results" page, the job in question still says "Queued" in the status column - and so I can't seem to view the results. This is despite receiving an email telling me "Job Complete" - and yes, it is the same job number.

Within the "Job Complete" email itself, it reads:

Your job (#1155533) was successfully completed. You may access XCMS Online ([%FORMS_RETURN_URL%]%FORMS_RETURN_URL%) to view your results.

However, the link they provided in the email ([%FORMS_RETURN_URL%]%FORMS_RETURN_URL%) did not work, quite obviously.

I'm a bit confused as to why this is happening. The job seems to have been submitted successfully, was queued and then (according to the email notification at least) was completed. However on the website, it's still in the "queued" stage so I cannot access the completed job results. Has anyone encountered this problem before? How do I resolve it, so that I can actually view my results? Am I forgetting to do something I'm supposed to in order to get access to the completed job? Any help is appreciated.

Thanks in advance!
XCMS / Re: Optimize peak-picking
Last post by maialba -
I still miss T338, T345, T370, T470, T500, T510, T580 and T620. Do you find them?

I've also tried to change the minimum peakwidth but I still miss some peaks... when I find peaks with overlapping I miss the less intense peaks.
I have some data that was acquired on an Agilent 6340 ion trap, so low resolution.  The data was acquired in positive mode and all in one 300 minute (5 hour) run.  I have since used proteowizard to "chop it up" into smaller (195 sec) chunks.  I have found that the original timing was retained and the chunks are indeed 195 seconds in length.  The samples were introduced into the MS through a novel device that has very little if any chromatography and thus is much more like direct injections.  To work out the analysis, I am currently working with a small subset of these runs (5 injections).

I also have a set of this data that I have gone into and replaced the original scan data with the scan numbers from just the 1st run in an attempt to group the data per the retention time data.

OK now to the question.  I am using xcmsSet in R to analyze the data.  I have picked peaks with this code:

"xset <- xcmsSet(samples, snthresh= 1.8, method = c("matchedFilter"), step = 0.2, steps = 1)"

and I think I have good results but am unsure how to visualize this data, any suggestions there?  Then when I group using:

"xset <- group(xset)"

It looks OK but again, I am not sure how to visualize this to check.  Finally when I try to RT correct this data across what are now separate files using:

"xset2 <- retcor(xset, family = "symmetric", plottype = "mdevden")"

‚ÄčAll I get is an error that says :

"Error in .local(object, ...) : No group information found"

Indeed the picked xset returns  with Peak Groups: 0

Two questions here, 1. is this likely to be because the original retention time data was saved during the file clipping and conversion step?  2.  Is there away around this so I can look for differences in this data?

Thank you so much for any help you can offer,
XCMS / Re: Howto re-integrate with new rtborders?
Last post by JosB -
I need a small peakwidth because of triacylglycerols coming off around the same time as the cholesterol esters. Isobaric compounds (different combinations acyl chains that add up to the same m/z but are chromatographically separated) that elute close together. Everything is fine, except the CholE.

However, explaining the problem here, led me to a solution: what I needed was getPeaks(). One can define the peakrange (an mz range and rt range) and get the integrated peak area from the xcmsRaw object. I will replace the appropriate xset@peaks with the result from this function and that should do the trick.

Thanks .

XCMS / Re: Howto re-integrate with new rtborders?
Last post by Jan Stanstrup -
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.
XCMS / Re: Howto re-integrate with new rtborders?
Last post by JosB -
I'm integrating as:

xset <- xcmsSet(files=project.files, profmethod = "bin", method="centWave", ppm = 2,  peakwidth=c(3,10), snthresh = 10, prefilter = c(5,2e4), mzdiff = 0.003, fitgauss = TRUE, integrate = 1, BPPARAM = MulticoreParam(4,progressbar=TRUE))

so I'm already using a positive mzdiff.

I was thinking to change the rt boundaries in xset@groups, clearing out the xset@groupidx and then do a fillpeaks, leaving the original peaks intact to prevent messing up the peakidx part.  But feeding an empty list to fillpeaks() doesn't seem to be allowed.
XCMS / Re: Optimize peak-picking
Last post by sneumann -
which peak exactly are you missing ? As you see below, I can happily find
the M184T407.


Code: [Select]
sneumann@acryl:/tmp/maialba$ R

R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(xcms)
Loading required package: mzR
Loading required package: Rcpp

Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    anyDuplicated, append,, as.vector, cbind, colnames,, duplicated, eval, evalq, Filter, Find, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, Map, mapply, match, mget,
    order, paste, pmax,, pmin,, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unlist, unsplit

Loading required package: ProtGenerics
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: MSnbase
Loading required package: BiocParallel

This is MSnbase version 2.1.10
  Read '?MSnbase' and references therein for information
  about the package and how to get started.

Attaching package: 'MSnbase'

The following object is masked from 'package:stats':


The following object is masked from 'package:base':


This is xcms version 1.51.9

> file <- "acq1.mzXML"
> xs <- xcmsSet(file, method="centWave", ppm=10, peakwidth=c(3,25), snthresh=2, mzCenterFun="wMean", integrate=2, fitgauss=F, scanrange=NULL, noise=0, sleep=0, verbose.columns=F)
DEBUG: using original centWave.
Detecting mass traces at 10 ppm ... OK
Detecting chromatographic peaks in 31566 regions of interest ... OK: 12594 found.
> peaks(xs)[peaks(xs)[,"mz"] < 185 & peaks(xs)[,"mz"] > 184 & peaks(xs)[,"rt"] < 450 & peaks(xs)[,"rt"] > 350, ]
            mz    mzmin    mzmax      rt   rtmin   rtmax       into       intb
 [1,] 184.0751 184.0749 184.0752 407.033 400.336 412.055 32211658.9 32192175.1
 [2,] 184.0748 184.0746 184.0750 448.890 442.190 456.424 45293500.1 45270113.3
 [3,] 184.0743 184.0742 184.0745 385.269 376.898 390.291  7070136.5  7048055.0
 [4,] 184.0743 184.0742 184.0745 371.038 366.016 376.898  2287296.5  2269111.5
 [5,] 184.0746 184.0745 184.0747 437.168 424.612 442.190 35453580.9 35425005.4
 [6,] 184.0745 184.0743 184.0746 416.241 412.055 424.612 19979393.7 19958609.4
 [7,] 184.0743 184.0742 184.0744 361.830 356.808 366.016   793141.5   777554.1
 [8,] 184.0743 184.0742 184.0745 395.314 390.291 400.336  4780042.9  4763156.6
 [9,] 184.0744 184.0743 184.0746 429.634 424.612 431.308  9079473.6  9067784.3
           maxo   sn sample
 [1,] 4220260.0 5793      1
 [2,] 3970454.8 5450      1
 [3,] 1122035.1 1539      1
 [4,]  421025.2  576      1
 [5,] 3052630.8 4190      1
 [6,] 2557234.2 3510      1
 [7,]  107556.0  146      1
 [8,]  691897.0  948      1
 [9,] 1943207.9 2666      1
> sessionInfo()
R version 3.2.3 (2015-12-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.2 LTS

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=en_US.UTF-8    
 [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] xcms_1.51.9         MSnbase_2.1.10      BiocParallel_1.9.4
[4] Biobase_2.30.0      ProtGenerics_1.7.0  BiocGenerics_0.16.1
[7] mzR_2.9.10          Rcpp_0.12.8        

loaded via a namespace (and not attached):
 [1] RColorBrewer_1.1-2     BiocInstaller_1.20.3   plyr_1.8.4            
 [4] iterators_1.0.8        tools_3.2.3            zlibbioc_1.16.0       
 [7] MALDIquant_1.16        digest_0.6.11          tibble_1.2            
[10] preprocessCore_1.32.0  gtable_0.2.0           lattice_0.20-33       
[13] Matrix_1.2-3           foreach_1.4.3          stringr_1.1.0         
[16] S4Vectors_0.8.11       IRanges_2.4.8          multtest_2.26.0       
[19] stats4_3.2.3           grid_3.2.3             impute_1.44.0         
[22] survival_2.40-1        XML_3.98-1.5           RANN_2.5              
[25] limma_3.26.9           ggplot2_2.2.1          reshape2_1.4.2        
[28] magrittr_1.5           MASS_7.3-45            splines_3.2.3         
[31] scales_0.4.1           pcaMethods_1.60.0      codetools_0.2-14      
[34] MassSpecWavelet_1.36.0 assertthat_0.1         mzID_1.13.0           
[37] colorspace_1.3-2       stringi_1.1.2          affy_1.48.0           
[40] lazyeval_0.2.0         munsell_0.4.3          doParallel_1.0.10     
[43] vsn_3.38.0             affyio_1.40.0         

XCMS / Re: Howto re-integrate with new rtborders?
Last post by Jan Stanstrup -
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.
XCMS / Re: Optimize peak-picking
Last post by Jan Stanstrup -
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.