Hello Reeju,
Thank you for trying the TPP for you analysis and for your questions and slides. Here is what I think might be happening. In 6.3 the scores from comet and the PeptideProphet analysis based on the models were different from 7.1.
Here is the comet f-value model in 6.3:
Here is the f-value model from 7.1:
The goal of PeptideProphet is to identify a bimodal distribution and fit two distributions to the observed black line, with the corrects modeled in green and the incorrect in red. In the first case (6.3) the observed distributions is not really bimodal to the eye and the PeptideProphet model is fitting both distributions to the one peak because the observed distribution is not bimodal enough, and please correct me if I am wrong, you are not providing known true negatives (decoys) to help train the model and keep the red distribution “pinned" by them. In the second case (7.1), the bimodal distribution is fitting much better in my opinion as the red curve is mostly in the correct place and the green model is fitting the shoulder where the majority of the correct PSMs in your data will be. I know you feel like you “lost” some IDs going fro m 6.3 analysis to 7.1, but I would say you likely improved the confidence in the results by the reanalysis. You can verify this by incorporating unknown true negatives (entrapement decoys) in you database to use as another FDR estimate to compare to the model’s results from 6.3 and 7.1.
Cheers!