Wednesday, February 1, 2017
Comparing quantification methods!
At first look -- I have absolutely NO idea what this chart is saying, but I'm interested in the fact that they are comparing multiple quantification approaches! If you're thinking "...wait...hasn't this been done 16 times before...?" Yup! But there are some subtle differences here, and it makes it interesting. (These researchers aren't un-knowledgeable of the past, they cite at least 15 previous publications in the introduction).
This is the paper, btw!
The idea here is to compare low-magnitude fold differences on Q-TOFs. They argue here, quite correctly, that not everyone has an Orbitrap and it is important to know how best to do quantification on the Q-TOF systems in relation to small differences in fold change.
Further details on the quantification methods compared:
1) iTRAQ 8-plex
2) Samples compared -- 18 proteins spiked into an E.coli digest at various levels
3) Data was processed with Mascot and Progenesis IQ
4) iTRAQ ratios were processed using an in-house approach based on using the reporter counts a student t-test and H&B approach was applied to FDR (they go into this at length -- and it seems pretty smart)
5) Worth noting -- if you're doing a student t-test, missing values are gonna mess things up, so in the case of these they just set the ratio to >10 fold.
What did they find? Spectral counting does really well compared to these other approaches -- both in sensitivity and in specificity (where something like a 1.1 fold difference needed to be detected -- it did really well).
My take-away -- this definitely makes sense to me! Q-TOFs aren't very sensitive, they don't have very good dynamic range, and they don't have a lot of resolution (compared to FTICR/OT systems) -- they are, however, extremely fast! When you have the capacity to get lots of MS/MS spectra per unit time a quantification method that markedly improves as the number of MS/MS spectra increases is probably going to win out -- and here it definitely does.
That sounded more negative than I wanted it to -- every instrument has it's own pros and cons. This paper shows how to leverage the pros of this instrument type (which I don't have a lot of experience with) to get the best possible quantification data -- and that is definitely a good thing!
Shoutout to whoever posted this on the Twitter spectral counting thread yesterday!