percolator_concat_stderr

gistfile1.txt

Percolator version 3.01, Build Date Nov 16 2016 14:58:22
Copyright (c) 2006-9 University of Washington. All rights reserved.
Written by Lukas Käll (lukall@u.washington.edu) in the
Department of Genome Sciences at the University of Washington.
Issued command:
percolator -t 0.02 -m test.concat.psm.tsv -r test.concat.pep.tsv test.concat.pin
Started Wed Mar  8 08:48:30 2017
Hyperparameters: selectionFdr=0.01, Cpos=0, Cneg=0, maxNiter=10
Reading tab-delimited input from datafile test.concat.pin
Features:
lnrSp deltLCn deltCn lnExpect Xcorr Sp IonFrac Mass PepLen Charge1 Charge2 Charge3 Charge4 Charge5 Charge6 enzN enzC enzInt lnNumSP dM absdM
Found 19690 PSMs
Concatenated search input detected, skipping both target-decoy competition and mix-max.
Train/test set contains 13915 positives and 5775 negatives, size ratio=2.40952 and pi0=1
Selecting Cpos by cross-validation.
Selecting Cneg by cross-validation.
Split 1:    Selected feature 4 as initial search direction. Could separate 4623 training set positives in that direction.
Split 2:    Selected feature 4 as initial search direction. Could separate 4701 training set positives in that direction.
Split 3:    Selected feature 4 as initial search direction. Could separate 4650 training set positives in that direction.
Found 6974 test set positives with q<0.02 in initial direction
Reading in data and feature calculation took 0.288918 cpu seconds or 1 seconds wall clock time.
---Training with Cpos selected by cross validation, Cneg selected by cross validation, fdr=0.01
Iteration 1:    Estimated 7364 PSMs with q<0.02
Iteration 2:    Estimated 7370 PSMs with q<0.02
Iteration 3:    Estimated 7387 PSMs with q<0.02
Iteration 4:    Estimated 7400 PSMs with q<0.02
Iteration 5:    Estimated 7400 PSMs with q<0.02
Iteration 6:    Estimated 7398 PSMs with q<0.02
Iteration 7:    Estimated 7397 PSMs with q<0.02
Iteration 8:    Estimated 7405 PSMs with q<0.02
Iteration 9:    Estimated 7403 PSMs with q<0.02
Iteration 10:   Estimated 7402 PSMs with q<0.02
Learned normalized SVM weights for the 3 cross-validation splits:
 Split1  Split2  Split3 FeatureName
-0.3382 -0.6418 -0.5385 lnrSp
 0.7802  0.9219  1.0173 deltLCn
-0.2933 -0.3049 -0.3753 deltCn
-1.4325 -0.8598 -0.9613 lnExpect
 1.1402  1.6526  1.4503 Xcorr
 0.8775 -0.0381  0.8188 Sp
-0.6558  0.0101 -0.2641 IonFrac
-0.0032 -0.0341  0.1013 Mass
-0.1820  0.0555 -0.0189 PepLen
 0.0000  0.0000  0.0000 Charge1
-0.0933 -0.0722 -0.1392 Charge2
 0.1316  0.1262  0.1372 Charge3
-0.0964 -0.1331 -0.0123 Charge4
 0.0139  0.0091  0.0458 Charge5
 0.0193  0.0196  0.0146 Charge6
 0.1025  0.3099  0.3136 enzN
 0.0000  0.0000  0.0000 enzC
-0.1095 -0.2527 -0.2502 enzInt
 0.2132  0.1264  0.3269 lnNumSP
 0.3171  0.6445  0.6229 dM
-0.7204 -0.7712 -0.9014 absdM
-0.7389 -0.9315 -0.7722 m0
Found 7266 test set PSMs with q<0.02.
Tossing out "redundant" PSMs keeping only the best scoring PSM for each unique peptide.
Calculating q values.
Final list yields 5017 target peptides with q<0.02.
Calculating posterior error probabilities (PEPs).
Processing took 11.22 cpu seconds or 5 seconds wall clock time.