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Topic: Peak integration in MS-Dial (Read 4831 times) previous topic - next topic

Peak integration in MS-Dial

Hi all,

I wonder if anyone knows how to deal with the default peak integration in MS-Dial and if that can be changed somehow?
I hope that attached image clearly depictures the issues I have: integration of peak in samples (S1-S6) and QCs are nice and clear while in all other samples (like Solvent for example), huge area under very tiny peak is added to overall peak area. This is very obvious when you see chromatograms, but after everything is exported to Excel, it becomes quite a problem. Especially for small peaks.
What I do not understand, is what baseline is used to perform integration like this? Obviously, using zero as a baseline is not ideal in chromatography peaks, especially if baseline is drifting.
Any suggestions and any explanation/help are welcome!

By the way, I am using MS-Dial for couple of years and in my opinion it is the best Metabolomics/Lipidomics software I tried so far. Great thanks to the developers!

Thank you.

Re: Peak integration in MS-Dial

Reply #1
Hi Sergei,
I see the same issue in my EICs in Table Viewer. I manually integrate, or zero peaks that represent just noise, but that may or may not be practical given the size of your data set (number of samples and number of features) obviously.

Hey, can I ask about your File IDs 14-16 and the annotations? I am struggling with how to deal with this from a workflow perspective in my data sets. Your annotations for 14 and 16 are different from 15, presumably because you had a different spectral match for 15 as compared with 14 and 16. So, do you inspect from Table Viewer and then go into the chromatogram files themselves to assess the spectral match? Or do you assume that the best hit for that alignment spot is the best hit for the feature? Maybe you have standards that you can use to verify which would make that easier (but that is not the case in my application). Or do you do Identification after Alignment? I have tried adjusting the EI Similarity scores in Alignment and in Identification, as well as other parameters (the sigma value in MS1Dec seems to be have a big impact in improving the deconvoluted spectra). I am using single quad GC-MS so that might be some of the issue...


Re: Peak integration in MS-Dial

Reply #2

sorry for late reply: when I have ambiguous identification I check my standard panel, which I run along with unknown samples. This gives me RT info. Also, if ms2 spectra was acquired, I check it against the database (mzCloud for instance). Hope this helps.


Re: Peak integration in MS-Dial

Reply #3
Hello Sergei
Normally, in my case, I just manually integrate it to zero.
I dont know if there's some way to put some 0 baseline, maybe at the normalization... IDK.


Re: Peak integration in MS-Dial

Reply #4
I've run into this issue as well. While I don't have an exact answer on how to solve it I can say I found 2 resources that explain what's happening.
"...The value of ‘-2’ in “Peak ID” column means that the peak is not detected by peak picking process. (but calculated by gap-filling method). In the case of gap-filled peak, the colors of the “Peak Int.” and “Peak Area” columns become light blue. In normal, the colors (red) reflect the level of peak intensity or peak area. You cannot refine the peak and alignment yet, but that function will be developed."

How Peak ID, alignment, and gapfilling work are explained in their math FAQ document which can be found here:

I wish their documentation were a bit better or a bit more up to date. Sometimes the examples are from an older version where the GUI choices or display is different from the version you're using.

So for what you've shown us for 'file ID' 0 -1, 4 - 7, 18, 19, and 21 are all examples of where gap filling by compulsion has happened. No peak was detected there, but because it's in your QCs it was forced into those samples (see last 2 pages of math FAQ).

I believe all the answers lie under the 'alignment' tab. I think you could have it exclude those by using a peak count filter(it says %, but I think it reflects an intensity value) or n% detected in one group, however in those cases it shouldn't even appear as an aligned feature rather than forcing signal into those blank samples. Alternatively you might be able to get them excluded by using blank subtraction. I'm thinking gap filling might be the problem here.

If I'm being honest all the stuff I've tried prior to exporting the aligned peak results has not had the desired results. For our data sets we have so many samples & ref matched IDs that it is incredibly laborious to manually investigate and possibly alter every sample over every data point.

Please let us know if you find a solution.