"Executive version": How do I extract the figures produced by the centwave algorithm to describe peak fitting, without watching all 60k ROIs flash past?
Hello xcms gurus,
Having previously used xcms as a way to automate extraction of EIC data, I'm having a crack at using it to pick peaks for our systems. The problem here is that I am a little bit of a fraud here - I am not doing metabolomics, but combinatorial 'systems' chemistry, and I fear that the systems I'm looking at are less amenable to this - the result of a messy "combinatorial explosion" (ca. 60k ROI per sample; lots of samples, and this is the first stage in my complexity progression)....
I am using the centwave algorithm, and a lot of the traces you get as it picks peaks have the kind of problem illustrated here:
[attachment=0:3u05twde]multiple peaks to pick.png[/attachment:3u05twde]
To get this image, I had to run something like the line below, and wait endlessly.
[n.b. settings slightly different to produce the image - different peakwidth, no fitgauss]
How do I extract these figures AFTER the process (or to files during it)? That would let me supervise, and return to the same EICs time after time, to check the optimisation. Alternatively, is there another way to plot what has been picked against the real data??
The products we're looking at are peptides. In the end I hope to look at sequence bias. Any suggestions? I feel like I'm getting somewhere (slowly), but would appreciate knowing if there is a smarter/established way to do this... e.g. I can produce (v. long) lists of possible products - should I be using a more targeted approach?
[Caveat: I'm a relative novice at manipulating ms data in r/with xcms - apologies if this is a little daft]
I'm using xcms to extract EICs from some LC-MS data (when I say some, I mean a lot - from a list of possible combinations in a combinatorial synthesis). When I compare EICs that are produced by xcms with those I produce using the software from the MS we are using (Bruker), I notice that the ones form xcms seem to incorporate a much wider band (+/- around the mass in question) - manifested in seeing a lot more peaks in the EICs, This is worrying, and I am not sure where I am introducing the error. Here is the workflow:
- Produce LC-MS data using Bruker Compass/Hystar. - Convert to mzXML using MsConvert GUI (Not changing settings: ie. 64 bit, Write index, using zlib compression, TPP compatability). - Open in r with xcms: "xr <- xcmsRaw(lcfile, profstep=0.005, profmethod="bin")" - Get EIC for each as follows: err <- 0.001 MzRange[1,1] <- td[i,2] - err MzRange[1,2] <- td[i,2] + err eic <- getEIC(xr, mzrange=MzRange, rtrange=RTRange) a <- attr(eic, "eic")$xcmsRaw[[1]] - Put the x,y data for the EIC into a plot.
Any ideas why the EIC I get that way would be so different that the one produced in Bruker software with mass +/- 0.001? Does that prodstep need to be much smaller? I imagined that half the err value would be sufficiently low, but I've tried down to 0.002 without any joy (that made my computer groan, but didn't improve the situation)?
Thanks for your help,
Andy
nb. I'm looking at the product of some (non-standard) combinatorial peptide synthesis, although for the problem I'm having, I don't think that's relevant. As well as getting a handle on what is where, I am using the EICs to construct a kind of "virtual BPC" to see if the mass lists I'm producing combinatorially can account for the BPC observed (that is, if all the products are of the kind we expect, even if we can't ID all of them explicitely) - if anyone can tell me if that approach is established/has a name/is stupid, that would also be much appreciated! It seems to produce a really good fit (and even with the wider EICs, that would be really improbable with the reasonable-length mass list I'm matching).
I'm pretty new to LC-MS. I am using it to compare a sample/set of samples from chemical experiments (complex systems) looking for new/unique products compared to a single blank/control (usually just the HPLC derivitisation procedure). I thought the peak-picking in xcms might be rather good at picking up things that we haven't noticed 'by eye' (in the UV detector or TIC) as we build up complexity.
Unfortunately, we're using a less-than-pro LCMS setup: we lack the hardware to synch the the LC and MS, so we are synching by pausing the MS acquisition until the LC injects. As a result, the LC retention times are all skewed by a significant gap, which can be read as the gap between the first and second scan, minus the time taken for the scans (typically 30-60 seconds, minus 1 second). The retcor function of xcms seems to have problems dealing with this; I imagine understandably, as the blank contains very few of the peaks observed in the other samples (in contrast to a lot of metabolomics applications, where most things seem to be present, and you're looking for smaller percentage changes in intensity).
This brings me to ask two questions:
- Is there a straightforward way to adjust the retention time 'by hand', either in the mzXML files, in original Waters .raw files, or after loading into xcms?
- Can anyone experience suggest a better approach than xcms for this application (making a list of 'significant' peaks, ideally disregarding those present in the blank) that doesn't require a lot of very expensive proprietary software? Or, indeed, a better approach within xcms that deviates significantly from the workflow described in "LC/MS Preprocessing and Analysis with xcms" document at http://http://bioconductor.org/packages/release/bioc/vignettes/xcms/inst/doc/xcmsPreprocess.pdf.
Thanks for your help,
Andy
nb. MS is a Synapt, acquiring HRes continuum data (0.5s scans) with a lockmass; converting .raw files to mzXML using mzconvert at the command line (ordering wrt retention time to fold in lockmass scans).
[Disclaimer: I'm new to LC-MS and I've tried to look for the answers to my question, but my sincere apologies if I'm asking an astonishingly stupid question]
I'm converting Waters data to .mzXML using msconvert, then loading it to xcms as raw data (something like, spec.raw <- xcmsRaw("spec.mzXML", profstep=0.001, profmethod="bin")). I would like to be able to extract metadata on the original Waters .raw file from which the .mzXML was taken; I know this is stored, as I can see it if I load the mzXML using readMzXmlFile (stored at specsm$metaData$parentFile[[1]]$fileName), but I havne't figured out how to get it out of xcms.
Any suggestions?
Thanks very much,
Andy
[edit: Or any other clever way to get the data out, without using the route I have been, since it takes about 10 mins to load the > 200 Mb files]
