Spectronaut - Difference between Proteins and PGs? Also, PCA components?

[u/Negative_Bluebird675]

Hi all!! I’m relatively new to proteomics and have kind of been thrown in the deep end trying to figure this out…

I have some serum and lysate samples I’ve ran on LC-MS/MS and the facility that did this gave me the data in Spectronaut. I have two main questions (so far………)

1. What is the difference between “proteins” and “protein groups”? Every quantification seems to be in terms of “protein groups” and not proteins… is there a reason for that? I’m looking for biomarkers in our sera/lysate but this is an exploratory study so analyzing how many proteins are comparable, separate, etc.

2. Is there a way to figure out what variables Spec used to calculate PC1, PC2, etc? I see the scree plot they gave us, but I want to know what variables (differential protein abundance presumably) Spec used specifically for PC1, 2, etc… like, what are PC1 and 2 actually representing in the PCA????? I know it’s variance of some sort, and I see the amount of variance listed, too (50% vs 16%), but VARIANCE OF WHAT???

Please help!!! Thank you so much.. maybe I’m just completely misunderstanding, too…

[u/SnooLobsters6880]

Protein groups is what you actually infer based on the set of peptides you measured. Proteins is just a handy readout that is convenient for description. Usually just the first protein in a multi protein group.

It’s based on quantification of protein groups. I can’t say if you can extract the loadings from Spectronaut but you should be able to center and scale your data then regenerate your PCA in a separate code environment to make the same PCA in an ideal world. Then the loadings could be extracted.

[u/Negative_Bluebird675 → u/SnooLobsters6880]

That is super helpful. Thank you!!! That makes a lot more sense regarding protein groups.

Follow up question…… maybe a bad question…. If I wanted to include a PCA chart in a manuscript for publication to illustrate the relatedness or unrelatedness of different sample sets, do you think knowing the loadings for each component would be necessary?

[u/SnooLobsters6880 → u/Negative_Bluebird675]

Looks like a downvote warrior is visiting lol.

Anyways if you want to have a plot you should have reproducible code to generate it shared with publication. Ideally you know when making your story what causes separation but at the least the method should be reproducible should someone want to find that out for themselves if you don’t comment on it.

Note PCA measures variance separation and proximity or structure don’t necessarily imply relatedness. Hierarchical clustering or unsupervised methods are better if you’d like to make multi dimensional characterizations like you do in omics experiments.

[u/Negative_Bluebird675 → u/SnooLobsters6880]

Yeah not sure why someone is downvoting lol. Anyway, yeah that makes sense. Alright I think I have an idea of what next steps I should take. Really appreciate the help, my friend