Hi Hiroshi
depending on the processing settings, sometimes we observe the same compound splitting into 2-3 different features, and we think the mass slice width (in Peak detection) may be one of the parameters driving this effect (in addition to the alignment tolerance across samples).
How can we properly to choose a slice width that fits our data, i.e. neither a too wide nor too narrow?
For high-res Orbitrap a width of 0.05 Da is suggested. As an example, attached is an internal standard run on our LC Orbitrap (acquired at 140.000 resolution, profile).
From my understanding of Tsugawa et al 2015 (Peak spotting) https://www.ncbi.nlm.nih.gov/pubmed/25938372 and the math presentation describing the MS-DIAL peak detection algorithm (default 0.1 Da), it seems MS-DIAL would correct the merging of BPCs across different m/z widths, somewhat similarly to XCMS? Below, from Smith et al 2006 (Peak detection) https://pubs.acs.org/doi/10.1021/ac051437y
"An important detail is the relationship between spectral peak width and slice width.
- if the peak width is larger than slice width: the signal from a single peak may bleed across multiple slices. Low-res MS produce peak widths greater than the XMCS default 0.1 m/z slice. The MEND peak detection algorithm uses a scoring function to assess whether a chromatographic peak is also at the maximum of a spectral peak, preemptively removing such bleed. Instead of eliminating spurious extra peaks during detection, our algorithm uses a post-processing step that descends through the peak list by intensity, eliminating any peaks in the vicinity (0.7 m/z) of higher intensity peaks.
- if the peak width is smaller than the slice width: high-res TOF or Fourier transform MS often exhibit such behavior. In that case, depending on the scan-to-scan precision of the instrument, the signal from an analyte may oscillate between adjacent slices over chromatographic time, making an otherwise smooth peak shape appear jagged. Based on operator knowledge of the mass spectrometer characteristics, we optionally combine the maximum signal intensity from adjacent slices into overlapping EIBPCs (i.e., 100.0/100.1, 100.1/100.2, etc.), That initial step produces both smooth and jagged chromatographic profiles, which are then used for filtration and peak detection. During the vicinity elimination postprocessing step, peaks detected from smooth profiles (integrated from the full signal) are selected over peaks detected from jagged profiles (integrated from an incomplete signal)."
Thanks!
Stefano
Hi Stefano,
one way should be that I have to make a "preview" function with a raw data visualization to optimize the parameters for especially mature analysts like you.
(but sorry, not yet done..)
You are right. The important parameter should be mass slice width and m/z tolerance for peak alignment.
However, if you see seriously unwanted features, please send me the demo files. I will improve the program for peak detection and peak alignment by the information.
Thanks,
Hiroshi
Hi Hiroshi,
whenever you find the time, this kind of pre-view function would be awesome!
I will see if I can share some demo files later. We saw this problem of feature splitting both with GC and LC
Thank you!
Stefano
Hi Hiroshi and Stefano
I am having the same problem reported by Stefano, in LC-q-exactive platform (HILIC chromatography)
Basically I have my compound split over adjacent scans, and thus multiple identification features for a same compound, clearly this is related to the peak width, that moreover, usually in HILIC is wider than RP
How did you solve this situation?
Kind regards
Eduardo
Dear Eduardo:
when many duplicate peaks occurred, I recommend to do:
1. Use larger "MS1 tolerance" of data collection tab.
2. Use lager "Mass slice width" of peak detection tab.
3. Use larger "Smoothing level" of peak detection tab.
4. Use larger "Retention time tolerance" of alignment tab.
5. Use larger "MS1 tolerance" of alignment tab.
Thanks,
Hiroshi