I am noticing some irregularities in my EICs from XCMS Online using the Orbitrap II HPLC setting for LTQ-Velos data. There are several noisy "peaks" (just baseline noise) as well as "half peaks" (integration stops near the apex of a peak). I looked through the method settings I wasn't really sure where to troubleshoot. I did notice that mzwid is set to 0.015, and this is 75 ppm error at 200 m/z and 15 ppm error at 1000 m/z -- well outside the range of the Orbitrap's resolution. So I turned this setting down to 0.005, and also based on the observed chromatography I adjusted the max peak width to 50 sec.
I'm wondering if A) I have misinterpreted what mzwid is doing and B) if there are other features the community can suggest to improve peak integration. I can live with noise peaks as those are (relatively) easy to ID and remove from the final feature table. However the "half peaks" are not as easy to spot without close EIC inspection and any help getting rid of them would be appreciated.
I have posted on this topic before, but hoping to push the conversation ahead by framing the subject a little differently.
I'm extracting lung tissue data from C18 with positive ESI on an Orbitrap. I often look at methionine's behavior, including how XCMS or similar software integrates it, compared to Thermo's Xcalibur software. This is because methionine is usually a "model metabolite" on our platform.
For this particular run, there are two "methionine" peaks that are 2-3 ppm off of the monoisotopic mass (plus the proton). One elutes sharply at 60 sec, the other a bit more broadly at 140 sec. Now for the interesting part: XCMS integrated this as 4 peaks. In my output table, they go in this order:
150.0588mz_130sec (npeaks=48 in 48 samples) 150.0588mz_88sec (npeaks=54 in 48 samples) 150.0587mz_106sec (npeaks=42 in 42 samples) 150.0587mz_66sec (npeaks=37 in 37 samples)
So first of all, I'm not sure why they go in this order in the column which is overall an ascending m/z column. Is it ordered by SNR or another metric? It can't be by m/z value, as break the overall ascending order trend in the column.
Second, which of these features is really the peaks of interest?
Finally, what am I doing wrong when I use XCMS to get twice as many peaks? Right now I am troubleshooting whether it is my setting of mzdiff=-0.001 (I am not sure where I came up with that value in the first place), and trying out -0.00005 instead.
I have had a frustrating issue with XCMS for a while now. Rather than describe this I've attached a PPTX file that shows first several peaks of the same m/z which have been listed, and in the second slide there is a trace of the 10 ppm window of that m/z. As you can see there are one or maybe two peaks in the trace but XCMS gave me several more from out of the noise.
How to fix this? I have HF QExactive data, positive mode, Hilic HPLC. I used centWave and obiwarp.
I'm curious if anyone using an LC-coupled QExactive workflow has a set of XCMS parameters they are happy with for recovering features that could ultimately be credentialed (to borrow terminology from Mahieu et al*). This paper has published some optimized parameters for XCMS, but I wonder if they need to be adjusted for the QExactive and if so, how (lowering ppm window significantly is one guess). I also have to bear in mind that my experiment won't involve isotopic credentialing, so the optimization in the paper may not be appropriate for a less initially rigorous discovery platform. I am much more used to working with apLCMS than XCMS, so I apologize if I am treading on old territory with this question.
*Credentialing Features: A Platform to Benchmark and Optimize Untargeted Metabolomic Methods, NG Mahieu et al, Anal Chem 2014
Assume you run a study with plasma samples and include a water sample as a blank for a discovery metabolomics experiment. Will that blank influence the m/z features detected? I have noticed that XCMS rarely outputs zeros in the resulting data matrix, and wonder if this is because it is integrating noise in an m/z window for missing values or because it is ignoring features with missing values. In other words, I am asking what will happen if the plasma samples have a peak that passes SNR threshold and other criteria, but the water blank doesn't have this peak. Will the feature be ignored or will XCMS try to integrate the water sample's missing value?
