Bjorn, Thank you so very much that was extremely helpful! I'll be well on my way to finishing my analysis.
-Robert M Jones On Tuesday, February 18, 2014 6:45:24 AM UTC-7, Bjorn wrote: > > Hi, > As far as I understand it, the TPP uses a mixture model-based approach to > determine posterior probabilities and it then uses these probabilities to > estimate the FDR. This can be done with or without the use of a decoy > database. However, when using some of the more sophisticated options (like > non-parametric modelling), you will need a decoy database to help the > modelling algorithms pin down the negative distribution. Also, when the > data is of not so excellent quality, the decoys can help make a better > distinction between good and bad identifications. > > After the analysis with ProteinProphet, you get something like this in the > ProteinProphet window: > > Prob Sens FPER Corr Incorr > 0.00 1.000 0.930 217 2874 > 0.10 1.000 0.337 217 110 > 0.20 1.000 0.337 217 110 > 0.30 0.915 0.216 198 55 > 0.40 0.865 0.152 187 34 > 0.50 0.816 0.102 177 20 > 0.60 0.777 0.074 168 14 > 0.70 0.747 0.058 162 10 > 0.80 0.647 0.019 140 3 > 0.90 0.608 0.010 132 1 > 0.95 0.569 0.005 123 1 > 0.96 0.547 0.004 119 0 > 0.97 0.529 0.003 115 0 > 0.98 0.507 0.002 110 0 > 0.99 0.470 0.001 102 0 > 1.00 0.152 0.000 33 0 > > The FPER is your FDR, so if you decide to set it at 1%, you notice that > this corresponds with a probability cut off of 0.90. Now, in your protein > list, you accept all proteins with a prob. of 0.09 or higher, which is > estimated to be 132 correct ones and 1 incorrect protein. Everything below > is discarded. > > I found lots of info in the following papers (esp. the last paper). > Hope this helps! > Cheers, > Bjorn > > [1] Choi, H., Fermin, D., and Nesvizhskii, A. I. Significance analysis of > spectral count data in label-free shotgun proteomics. Mol. Cell. Proteomics > 7, 12 (2008), 2373–2385. > > [2] Choi, H., Ghosh, D., and Nesvizhskii, A. I. Statistical validation of > peptide identifications in large-scale proteomics using the target-decoy > database search strategy and flexible mixture modeling. J. Proteome Res. 7, > 1 (2008), 286–292. > > [3] Choi, H., and Nesvizhskii, A. I. False discovery rates and related > statistical concepts in mass spectrometry-based proteomics. J. Proteome > Res. 7, 1 (2008), 47–50. > > [4] Choi, H., and Nesvizhskii, A. I. Semisupervised model-based validation > of peptide identifications in mass spectrometry-based proteomics. J. > Proteome Res. 7, 1 (2008), 254–265. > > [5] Deutsch, E. W., Mendoza, L., Shteynberg, D., Farrah, T., Lam, H., > Tasman, N., Sun, Z., Nilsson, E., Pratt, B., Prazen, B., Eng, J. K., > Martin, D. B., Nesvizhskii, A. I., and Aebersold, R. A guided tour of the > Trans-Proteomic Pipeline. Proteomics 10, 6 (2010), 1150–1159. > > [6] Keller, A., Nesvizhskii, A. I., Kolker, E., and Aebersold, R. > Empirical statistical model to estimate the accuracy of peptide > identifications made by MS/MS and database search. Anal. Chem. 74, 20 > (2002), 5383–5392. > > [7] Nesvizhskii, A. I. A survey of computational methods and error rate > estimation procedures for peptide and protein identification in shotgun > proteomics. J. Proteomics 73, 11 (2010), 2092–2123. > -- 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/groups/opt_out.
