Hej David, sorry for my late response, I wanted to do some proper testing before reporting back to you. I did the following tests now, always using the -Od option and checking manually for Decoy hits:
a) I reran the analysis that I posted above with these results: 1) X!Tandem only without iProphet: 1884 (557), 1% model estimated error, 18/1884 = 0.95% Decoy estimated error 2) X!Tandem only with iProphet: 1760 (854), 0.9% model estimated error, 6/1760 = 0.34% Decoy estimated error 3) MSGF only without iProphet: 2138 (632), 1% model estimated error, 31/2138 = 1.4% Decoy estimated error 4) MSGF only with iProphet: 1975 (876), 0.8% model estimated error, 8/1975 = 0.4% Decoy estimated error 5) X!Tandem and MSGF without iProphet: 2176 (590), 0.5% model estimated error, 28/2176 = 1.2% Decoy estimated error 6) X!Tandem and MSGF with iProphet: 2154 (1057), 1% model estimated error, 17/2154 = 0.8% Decoy estimated error b) I included a 5% FDR on peptide level after PeptideProphet, based on the model estimate: 1) X!Tandem only without iProphet: 1847 (569), 1% model estimated error, 13/1847 = 0.7% Decoy estimated error 2) X!Tandem only with iProphet: 1756 (848), 0.9% model estimated error, 5/1756 = 0.3% Decoy estimated error 3) MSGF only without iProphet: 2125 (647), 1% model estimated error, 29/2125 = 1.3% Decoy estimated error 4) MSGF only with iProphet: 1975 (875), 0.8% model estimated error, 8/1975 = 0.4% Decoy estimated error 5) X!Tandem and MSGF without iProphet: 2216 (655), 0.9% model estimated error, 37/2216 = 1.6% Decoy estimated error 6) X!Tandem and MSGF with iProphet: 2157 (1065), 1% model estimated error, 20/2157 = 0.9% Decoy estimated error c) I used the less redundant swissprot database: 1) X!Tandem only without iProphet: 1812 (419), 0.7% model estimated error, 15/1812 = 0.8% Decoy estimated error 2) X!Tandem only with iProphet: 1671 (752), 0.7% model estimated error, 8/1671 = 0.5% Decoy estimated error 3) MSGF only without iProphet: 2077 (602), 0.8% model estimated error, 0/2077 = 0% Decoy estimated error 4) MSGF only with iProphet: 1945 (855), 0.8% model estimated error, 0/1945 = 0% Decoy estimated error 5) X!Tandem and MSGF without iProphet: 2238 (622), 1% model estimated error, 51/2187 = 2.3% Decoy estimated error 6) X!Tandem and MSGF with iProphet: 2147 (1031), 0.9% model estimated error, 21/2144 = 1% Decoy estimated error My conclusions: - shutting off the IPROPHET option in ProteinProphet lets the model underestimate the error. However, it is not far off. - enabling the IPROPHET option in ProteinProphet gives a good error estimation, but one looses a lot of peptides from proteins that were identified by more than one peptide. As I understood the iProphet algorithms, such peptides should rather get a higher probability in iProphet. I also uploaded the data to my dropbox and will send you the link. -- You received this message because you are subscribed to the Google Groups "spctools-discuss" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at http://groups.google.com/group/spctools-discuss. For more options, visit https://groups.google.com/d/optout.
