Hi, Brendan et al.,

We run our samples (with replicates) in Agilent QqQ, then import .d file to Skyline. Then we go through all the parameters for manually exploring, correct and refine the picked SRM chromatograms. Finally, export a custom-defined report as .csv.

From here onwards how can I analyze the data? We do Normalization across all peptides for each sample and then scaling each peptide across all the sample. Do you recommend this procedure?
(Raw data example attached; this is how my exported data looks)

I want to know, how you all doing the data analysis after exporting the data to excel, importantly, because for the same protein peptide behavior is different (diff due to flying?)
for e.g. Protein "A", have peptide; a1, a2, a3, Protein "B" have peptide b1, b2, But when each peptide peak area is different; a1=100, a2=800, a3=2000, how can we average our peptides to a protein?

How to compare, Wildtype vs mutants.

I'd appreciate good example tutorial if available. Also, if you are using R Scripts, would love to have a copy to see?

Thank you for considering my request and looking forward to hearing from you ASAP.

VJ