tdc stderr, target psms, decoy psms

decoy.tdc.txt

PSMId	score	q-value	posterior_error_prob	peptide	proteinIds

target.tdc.stderr.txt

Percolator version 3.02.0, Build Date Feb  2 2018 16:57:41
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 -Y -P -decoy_ -m target.tdc.txt -M decoy.tdc.txt -s -t 0.05 make-pin.pin
Started Thu Feb 20 00:32:27 2020
Hyperparameters: selectionFdr=0.01, Cpos=0, Cneg=0, maxNiter=10
Reading tab-delimited input from datafile make-pin.pin
Features:
lnrSp deltLCn deltCn XCorr Sp IonFrac PepLen Charge1 enzN enzC enzInt lnNumDSP dM absdM 
Found 210 PSMs
Separate target and decoy search inputs detected, using target-decoy competition on Percolator scores.
Train/test set contains 105 positives and 105 negatives, size ratio=1 and pi0=1
Selecting Cpos by cross-validation.
Selecting Cneg by cross-validation.
Split 1:	Selected feature 12 as initial direction. Could separate 70 training set positives with q<0.01 in that direction.
Split 2:	Selected feature 12 as initial direction. Could separate 70 training set positives with q<0.01 in that direction.
Split 3:	Selected feature 12 as initial direction. Could separate 70 training set positives with q<0.01 in that direction.
Found 105 test set positives with q<0.05 in initial direction
Reading in data and feature calculation took 0.003579 cpu seconds or 0 seconds wall clock time.
---Training with Cpos selected by cross validation, Cneg selected by cross validation, initial_fdr=0.01, fdr=0.01
Iteration 1:	Estimated 105 PSMs with q<0.05
Iteration 2:	Estimated 105 PSMs with q<0.05
Iteration 3:	Estimated 105 PSMs with q<0.05
Iteration 4:	Estimated 105 PSMs with q<0.05
Iteration 5:	Estimated 105 PSMs with q<0.05
Iteration 6:	Estimated 105 PSMs with q<0.05
Iteration 7:	Estimated 105 PSMs with q<0.05
Iteration 8:	Estimated 105 PSMs with q<0.05
Iteration 9:	Estimated 105 PSMs with q<0.05
Iteration 10:	Estimated 105 PSMs with q<0.05
Learned normalized SVM weights for the 3 cross-validation splits:
 Split1	 Split2	 Split3	FeatureName
 0.0111	 0.0047	 0.0033	lnrSp
 0.0205	 0.0049	 0.0053	deltLCn
-0.0021	-0.0012	 0.0001	deltCn
 0.0001	 0.0040	-0.0008	XCorr
 0.0000	 0.0000	 0.0000	Sp
 0.0000	 0.0000	 0.0000	IonFrac
 0.0038	 0.0026	 0.0037	PepLen
 0.0000	 0.0000	 0.0000	Charge1
 0.0000	 0.0000	 0.0000	enzN
 0.0000	 0.0000	 0.0000	enzC
-0.0030	 0.0002	 0.0009	enzInt
-0.9588	-0.9616	-0.9617	lnNumDSP
-0.0008	-0.0027	-0.0026	dM
-0.0022	-0.0030	-0.0033	absdM
-0.0291	-0.0297	-0.0298	m0
Found 105 test set PSMs with q<0.05.
Selected best-scoring PSM per scan+expMass (target-decoy competition): 21 target PSMs and 0 decoy PSMs.
Tossing out "redundant" PSMs keeping only the best scoring PSM for each unique peptide.
Calculating q values.
Final list yields 0 target peptides with q<0.05.
Calculating posterior error probabilities (PEPs).
Processing took 0.1709 cpu seconds or 0 seconds wall clock time.
PSMId	score	q-value	posterior_error_prob	peptide	proteinIds
target_0_20_1_2	0	0.111111	6.30512e-16	K.Q[-16.04]QQRPGLSSK.V	AAK49468.1
target_0_8_1_1	0	0.111111	6.30512e-16	R.RGQPWVLTR.Y	AAK49467.1
target_0_12_1_5	-0.000516181	0.111111	6.30512e-16	R.N[0.98]GASYFAAAQ[-16.04]K.R	ALJ92831.1
target_0_4_1_2	-0.000764098	0.111111	6.30512e-16	K.RGPQ[-16.04]ETVADR.R	AAK49465.1
target_0_1_1_2	-0.00106162	0.111111	6.30512e-16	K.LVQGLLAVTAK.Q	AAK49470.1
target_0_17_1_3	-0.00262305	0.111111	6.30512e-16	K.KAAKIAAAGLAK.N	AAK49479.1
target_0_16_1_1	-0.00272363	0.111111	6.30512e-16	K.EES[79.97]LEDLAK.G	AAK49472.1
target_0_13_1_5	-0.00334685	0.111111	6.30512e-16	K.RN[0.98]T[79.97]EAVGKR.-	QEG99559.1
target_0_9_1_4	-0.016052	0.111111	6.30512e-16	K.VAGTGDN[0.98]Q[-16.04]HTK.I	AAK49470.1

target.tdc.txt

PSMId	score	q-value	posterior_error_prob	peptide	proteinIds
target_0_20_1_2	0	0.047619	6.30512e-16	K.Q[-16.04]QQRPGLSSK.V	AAK49468.1
target_0_8_1_1	0	0.047619	6.30512e-16	R.RGQPWVLTR.Y	AAK49467.1
target_0_2_1_1	0	0.047619	6.30512e-16	R.RGQPWVLTR.Y	AAK49467.1
target_0_12_1_5	-0.000516181	0.047619	6.30512e-16	R.N[0.98]GASYFAAAQ[-16.04]K.R	ALJ92831.1
target_0_4_1_2	-0.000764098	0.047619	6.30512e-16	K.RGPQ[-16.04]ETVADR.R	AAK49465.1
target_0_7_1_1	-0.000910821	0.047619	6.30512e-16	R.RGQPWVLTR.Y	AAK49467.1
target_0_11_1_1	-0.000961745	0.047619	6.30512e-16	R.RGQPWVLTR.Y	AAK49467.1
target_0_1_1_2	-0.00106162	0.047619	6.30512e-16	K.LVQGLLAVTAK.Q	AAK49470.1
target_0_10_1_1	-0.00106718	0.047619	6.30512e-16	R.RGQPWVLTR.Y	AAK49467.1
target_0_14_1_1	-0.00123665	0.047619	6.30512e-16	R.RGQPWVLTR.Y	AAK49467.1
target_0_17_1_3	-0.00262305	0.047619	6.30512e-16	K.KAAKIAAAGLAK.N	AAK49479.1
target_0_16_1_1	-0.00272363	0.047619	6.30512e-16	K.EES[79.97]LEDLAK.G	AAK49472.1
target_0_21_1_5	-0.00298902	0.047619	6.30512e-16	K.LVQGLLAVTAK.Q	AAK49470.1
target_0_13_1_5	-0.00334685	0.047619	6.30512e-16	K.RN[0.98]T[79.97]EAVGKR.-	QEG99559.1
target_0_3_1_1	-0.00422443	0.047619	6.30512e-16	K.EES[79.97]LEDLAK.G	AAK49472.1
target_0_18_1_1	-0.00467679	0.047619	6.30512e-16	K.EES[79.97]LEDLAK.G	AAK49472.1
target_0_15_1_2	-0.00559309	0.047619	6.30512e-16	K.EES[79.97]LEDLAK.G	AAK49472.1
target_0_5_1_1	-0.0103525	0.047619	6.30512e-16	R.RGQPWVLTR.Y	AAK49467.1
target_0_6_1_1	-0.0107748	0.047619	6.30512e-16	K.RN[0.98]T[79.97]EAVGKR.-	QEG99559.1
target_0_19_1_1	-0.011423	0.047619	6.30512e-16	R.RGQPWVLTR.Y	AAK49467.1
target_0_9_1_4	-0.016052	0.047619	6.30512e-16	K.VAGTGDN[0.98]Q[-16.04]HTK.I	AAK49470.1