Seems reasonable, except what instrument has profile IM values? Waters has 200 bins and their masses are profile; 200 doesn't seem enough to count as profile. Bruker is 1000ish bins but the masses are explicitly centroided at acquisition. I'll be honest I didn't follow much of the linked issue discussion.

@chambm What we want to express is whether or not the IM dimension is expected to contain a series of points for an analyte, or whether it has been processed s.t. each point refers to a distinct analyte (or two analytes so close we can't tell them apart). This is the same idea for profile spectrum vs. centroid spectrum.

Put another way, "Can I treat this spectrum as a peak list or do I need to do something to it before I can go off and use them?".

The bin spacing is a valid point for IM peak resolution, but I don't think that's something we can get from the vendor software and will vary in definition by unit and/or vendor.

For centroid spectra it's still extremely common to have multiple peaks for a single analyte, e.g. isotopes and charge states. Only a deconvolved and deisotoped centroided spectrum would (potentially?) be one peak per analyte (even then it depends how you define "analyte" I think).

But if I understand what you're saying, when coming straight from ProteoWizard, IM representation would always be ion mobility profile frame?

Apologies, I should have said ion, not analyte. But yes, you have the idea, I think.

I'm not aware of any filters in MSConvert that collapse the IM dimension, except maybe scanSumming?

ScanSumming just drops it entirely. I'm still not sure about this "centroid/profile" distinction in the IM dimension. I think it needs more consideration from other IM-interested folks.

I admit I worked primarily on larger molecules with fairly wide mobilograms/arrival time distributions using Waters Cyclic and Bruker timsTOF data, where frames/cycles do form well-defined peaks, and my approach to both was to gather everything into isotopic pattern fitted groups and look for structure over the mobility and RT dimensions. The example file in the linked issue are single frame IM feature extractions at a single RT.

The same idea is applied to timsTOF data in IonQuant, where m/z-IM-rt abundances are extracted from contiguous observations, but they use identification seeds instead of using traditional feature detection techniques. I think this article states the same idea is used in MaxQuant for timsTOF data.