diff --git a/pwiz_tools/Skyline/Model/Results/ChromDataSet.cs b/pwiz_tools/Skyline/Model/Results/ChromDataSet.cs index 668f41918d5..b400a7e94c6 100644 --- a/pwiz_tools/Skyline/Model/Results/ChromDataSet.cs +++ b/pwiz_tools/Skyline/Model/Results/ChromDataSet.cs @@ -618,7 +618,7 @@ public void PickChromatogramPeaks(double[] retentionTimes, bool isAlignedTimes, } else if (retentionTimes.Length == 0) { - // Be sure not to remove anything with a higher combined score than + // Be sure not to remove anything with as high a combined score as // what happen to look visually like the biggest peaks. double minKeepScore = _listPeakSets.Take(iRemove).Min(peakSet => peakSet.CombinedScore); @@ -626,7 +626,7 @@ public void PickChromatogramPeaks(double[] retentionTimes, bool isAlignedTimes, // this sorting happened before peaks were extended. _listPeakSets.Sort(ComparePeakLists); - iRemove = Math.Max(iRemove, _listPeakSets.IndexOf(peakSet => peakSet.CombinedScore == minKeepScore)); + iRemove = Math.Max(iRemove, _listPeakSets.IndexOf(peakSet => peakSet.CombinedScore == minKeepScore) + 1); } else { diff --git a/pwiz_tools/Skyline/TestFunctional/PickChromatogramPeaksTest.cs b/pwiz_tools/Skyline/TestFunctional/PickChromatogramPeaksTest.cs new file mode 100644 index 00000000000..379175e3bd3 --- /dev/null +++ b/pwiz_tools/Skyline/TestFunctional/PickChromatogramPeaksTest.cs @@ -0,0 +1,70 @@ +/* + * Original author: Nicholas Shulman , + * MacCoss Lab, Department of Genome Sciences, UW + * + * Copyright 2025 University of Washington - Seattle, WA + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +using System; +using System.Linq; +using Microsoft.VisualStudio.TestTools.UnitTesting; +using pwiz.Common.DataBinding; +using pwiz.Skyline.Controls.Databinding; +using pwiz.Skyline.EditUI; +using pwiz.Skyline.Model.Databinding.Entities; +using pwiz.SkylineTestUtil; + +namespace pwiz.SkylineTestFunctional +{ + [TestClass] + public class PickChromatogramPeaksTest : AbstractFunctionalTest + { + [TestMethod] + public void TestPickChromatogramPeaks() + { + TestFilesZip = @"TestFunctional\PickChromatogramPeaksTest.zip"; + RunFunctionalTest(); + } + + protected override void DoTest() + { + RunUI(()=>SkylineWindow.OpenFile(TestFilesDir.GetTestPath("PickChromatogramPeaksTest.sky"))); + ImportResultsFile(TestFilesDir.GetTestPath("PickPeakTest.wiff")); + RunDlg(SkylineWindow.ShowFindNodeDlg, findNodeDlg => + { + findNodeDlg.SearchString = "GQLPSGSSQFPHGQK"; + findNodeDlg.FindNext(); + findNodeDlg.Close(); + }); + RunUI(()=>SkylineWindow.ShowCandidatePeaks()); + var candidatePeaks = FindOpenForm(); + WaitForConditionUI(() => candidatePeaks.IsComplete); + RunUI(() => + { + var databoundGridControl = candidatePeaks.DataboundGridControl; + Assert.AreEqual(4, databoundGridControl.RowCount); + var colPeakGroupRetentionTime = + databoundGridControl.FindColumn( + PropertyPath.Root.Property(nameof(CandidatePeakGroup.PeakGroupRetentionTime))); + Assert.IsNotNull(colPeakGroupRetentionTime); + var retentionTimes = Enumerable.Range(0, databoundGridControl.RowCount).Select(iRow => + Math.Round((double) databoundGridControl.DataGridView.Rows[iRow].Cells[colPeakGroupRetentionTime.Index].Value, 2)).ToList(); + Assert.AreEqual(7.2, retentionTimes[0]); + Assert.AreEqual(7.73, retentionTimes[1]); + Assert.AreEqual(8.44, retentionTimes[2]); + Assert.AreEqual(8.98, retentionTimes[3]); + }); + } + } +} diff --git a/pwiz_tools/Skyline/TestFunctional/PickChromatogramPeaksTest.zip b/pwiz_tools/Skyline/TestFunctional/PickChromatogramPeaksTest.zip new file mode 100644 index 00000000000..b5ca72e29ca Binary files /dev/null and b/pwiz_tools/Skyline/TestFunctional/PickChromatogramPeaksTest.zip differ diff --git a/pwiz_tools/Skyline/TestFunctional/TestFunctional.csproj b/pwiz_tools/Skyline/TestFunctional/TestFunctional.csproj index b988b9ace85..869b966d448 100644 --- a/pwiz_tools/Skyline/TestFunctional/TestFunctional.csproj +++ b/pwiz_tools/Skyline/TestFunctional/TestFunctional.csproj @@ -217,6 +217,7 @@ + diff --git a/pwiz_tools/Skyline/TestTutorial/ExistingExperimentsTutorialTest.cs b/pwiz_tools/Skyline/TestTutorial/ExistingExperimentsTutorialTest.cs index 39e1cd27bfa..8c5e73042b7 100644 --- a/pwiz_tools/Skyline/TestTutorial/ExistingExperimentsTutorialTest.cs +++ b/pwiz_tools/Skyline/TestTutorial/ExistingExperimentsTutorialTest.cs @@ -732,7 +732,7 @@ private static void TestApplyToAll() RunUI(() => { PeakMatcherTestUtil.SelectAndApplyPeak("ESDTSYVSLK", 564.7746, "A_02", false, false, 18.34195); - PeakMatcherTestUtil.VerifyPeaks(MakeVerificationDictionary(18.34, 18.34, 18.28, 18.28)); + PeakMatcherTestUtil.VerifyPeaks(MakeVerificationDictionary(18.3419, 18.34195, 18.27585, 23.481)); }); RunUI(() => { diff --git a/pwiz_tools/Skyline/TestTutorial/GroupedStudies1TutorialTest.cs b/pwiz_tools/Skyline/TestTutorial/GroupedStudies1TutorialTest.cs index 158a9a8c29d..3c677c8b3ce 100644 --- a/pwiz_tools/Skyline/TestTutorial/GroupedStudies1TutorialTest.cs +++ b/pwiz_tools/Skyline/TestTutorial/GroupedStudies1TutorialTest.cs @@ -368,7 +368,7 @@ private void ExploreTopPeptides() OkDialog(findDlg, findDlg.Close); var findView = WaitForOpenForm(); - int expectedItems = IsFullData ? 228 : 151; + int expectedItems = IsFullData ? 230 : 153; try { WaitForConditionUI(1000, () => findView.ItemCount == expectedItems); @@ -610,9 +610,9 @@ private void AddTruncatedPrecursorsView(DocumentGridForm documentGrid, bool init OkDialog(viewEditor, viewEditor.OkDialog); var pathTruncated = PropertyPath.Parse("Results!*.Value.CountTruncated"); - int expectedItems = 86; + int expectedItems = 88; if (IsFullData) - expectedItems = 129; + expectedItems = 131; try { WaitForConditionUI(1000, () => documentGrid.RowCount == expectedItems && @@ -1155,7 +1155,7 @@ private void PrepareForStatistics() PauseForScreenShot("Document Grid with MissingData field"); - int expectedRows = IsFullData ? 133 : 89; + int expectedRows = IsFullData ? 135 : 91; const int expectedRowsAbbreviated = 221; // When not all of the tests are run RunUI(() => { @@ -1650,10 +1650,10 @@ private static void TestApplyToAll() { PeakMatcherTestUtil.SelectAndApplyPeak("LNDGSQITFEK", null, "D_138_REP1", false, false, 23.5299); PeakMatcherTestUtil.VerifyPeaks(MakeVerificationDictionary( - 23.45410, 22.77782, 23.11210, 23.19398, 22.88790, 23.00840, - 23.52992, 23.57400, 23.19233, 23.45998, 22.81207, 22.81960, - 23.87478, 23.68238, 23.03755, 22.89255, 22.69688, 23.04172, - 22.85375, 23.04702, 22.85068, 22.88932, 22.70258, 23.19258)); + 23.4541, 22.77782, 23.1121, 23.19398, 22.8879, 23.0084, + 23.52992, 23.574, 23.19233, 23.45998, 22.81207, 22.8196, + 23.87478, 23.68238, 23.03755, 22.89255, 22.69688, 23.04172, + 22.85375, 25.51052, 22.85068, 22.88932, 22.70258, 23.19258)); }); // Apply to subsequent RunUI(() => @@ -1699,10 +1699,10 @@ private static void TestApplyToAll() { PeakMatcherTestUtil.SelectAndApplyPeak("LGGEEVSVAC[+57.0]K", null, "H_148_REP1", false, false, 13.6641); PeakMatcherTestUtil.VerifyPeaks(MakeVerificationDictionary( - 14.30043, 13.79685, 13.79692, 13.79708, 14.33403, 14.90242, - 13.83123, 14.03223, 13.66342, 13.76475, 13.83022, 13.73013, - 14.33438, 13.83052, 14.70115, 13.66408, 13.63018, 13.69645, - 13.73080, 13.52982, 13.69677, 13.83090, 13.56257, 13.76500)); + 14.30043, 13.79685, 13.79692, 13.79708, 14.33403, 14.90242, + 13.76423, 14.03223, 13.66342, 13.76475, 13.83022, 13.73013, + 14.33438, 13.83052, 14.70115, 13.66408, 13.63018, 13.69645, + 13.7308, 13.52982, 13.69677, 13.8309, 13.56257, 13.765)); }); // For each test, a peak was picked and applied - undo two actions per test diff --git a/pwiz_tools/Skyline/TestTutorial/PeakPickingTutorialTest.cs b/pwiz_tools/Skyline/TestTutorial/PeakPickingTutorialTest.cs index 8295d66e89e..94144d2ea5e 100644 --- a/pwiz_tools/Skyline/TestTutorial/PeakPickingTutorialTest.cs +++ b/pwiz_tools/Skyline/TestTutorial/PeakPickingTutorialTest.cs @@ -99,8 +99,8 @@ private string GetTestPath(string relativePath) private readonly string[] EXPECTED_COEFFICIENTS = { - "-0.1095|-0.7689|1.9147|0.9647|0.0265|0.1822|0.2229| null |0.5529|6.5433|-0.0357|0.5285|0.6585| null | null | null | null | null ", - "0.2900| null | null |5.9841|-0.0624|0.6681|0.7968| null | null | null | null | null | null | null | null | null | null | null ", + "-0.1178|-0.8087|1.9805|0.9576|0.0263|0.1984|0.2404| null |0.3943|6.6709|-0.0364|0.5226|0.6494| null | null | null | null | null ", + "0.2725| null | null |6.0285|-0.0646|0.6737|0.8121| null | null | null | null | null | null | null | null | null | null | null ", }; protected override void DoTest() @@ -200,7 +200,7 @@ protected override void DoTest() var editDlg = ShowDialog(reintegrateDlg.AddPeakScoringModel); RunUI(() => editDlg.TrainModel()); PauseForScreenShot("Edit Peak Scoring Model form trained model"); - RunUI(() => Assert.AreEqual(0.5893, editDlg.PeakCalculatorsGrid.Items[3].PercentContribution ?? 0, 0.005)); + RunUI(() => Assert.AreEqual(0.6065, editDlg.PeakCalculatorsGrid.Items[3].PercentContribution ?? 0, 0.005)); Control selectedGraphControl = null; RunUI(() => diff --git a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/en/TestGroupedStudies1Tutorial.log b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/en/TestGroupedStudies1Tutorial.log index 32f52d310d4..cd23295a9ef 100644 --- a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/en/TestGroupedStudies1Tutorial.log +++ b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/en/TestGroupedStudies1Tutorial.log @@ -162,8 +162,8 @@ Undo Redo : Changed peak bounds of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.31 Summary : Changed peak bounds of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ All Info : Changed peak bounds of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ -Changed start time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ in "D_108_REP2" from 27.79 to 26.86 -Changed end time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ in "D_108_REP2" from 28.72 to 27.40 +Changed start time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ in "D_108_REP2" from 27.13 to 26.86 +Changed end time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ in "D_108_REP2" from 27.86 to 27.40 Undo Redo : Deleted target NP_036870 > K.FGLYSDQMR.E [182, 190] Summary : Deleted target NP_036870 > K.FGLYSDQMR.E [182, 190] @@ -338,10 +338,10 @@ Summary : Set MissingData of DFATVYVDAVK to True All Info : Set MissingData of DFATVYVDAVK to True -Undo Redo : Pasted 89 values into the document grid -Summary : Pasted 89 values into the document grid +Undo Redo : Pasted 91 values into the document grid +Summary : Pasted 91 values into the document grid All Info : -Pasted 89 values into the document grid +Pasted 91 values into the document grid Document grid > Report name is "Truncated Precursors" Document grid > Column settings > Column Sorts : contains { Column = "Count Truncated", Direction = "Descending" } Set MissingData of DFATVYVDAVK to True @@ -349,6 +349,7 @@ Set MissingData of DFATVYVDAVK to True Set MissingData of DFATVYVDAVK to True Set MissingData of DFATVYVDAVK to True Set MissingData of DFATVYVDAVK to True +Set MissingData of DFATVYVDAVK to True Set MissingData of DYVSQFESSTLGK to True Set MissingData of DYVSQFESSTLGK to True Set MissingData of DYVSQFESSTLGK to True @@ -358,6 +359,7 @@ Set MissingData of C[+57.021464]SLPRPWALTFSYGR to True Set MissingData of TGTNLMDFLSR to True Set MissingData of TGTNLMDFLSR to True Set MissingData of TGTNLMDFLSR to True +Set MissingData of TGTNLMDFLSR to True Set MissingData of ASGIIDTLFQDR to True Set MissingData of ASGIIDTLFQDR to True Set MissingData of SDFQVPC[+57.021464]QYSQQLK to True @@ -522,6 +524,8 @@ TRUE TRUE TRUE TRUE +TRUE +TRUE Report name = "Truncated Precursors", Column settings = diff --git a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/en/TestPeakPickingTutorial.log b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/en/TestPeakPickingTutorial.log index e04377fee3d..ad64ae960a1 100644 --- a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/en/TestPeakPickingTutorial.log +++ b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/en/TestPeakPickingTutorial.log @@ -66,18 +66,18 @@ Summary : Reintegrated peaks using "test1" All Info : Reintegrated peaks using "test1" Reintegrate > Peak scoring model is "test1" -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0.109478368757285", Percentage Contribution = "-0.03" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0.768944435803533", Percentage Contribution = "0.067" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1.91473907688983", Percentage Contribution = "0.071" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0.964669685201429", Percentage Contribution = "0.064" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0.02650942606722", Percentage Contribution = "-0.023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0.182160083950605", Percentage Contribution = "0.062" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0.222905461284023", Percentage Contribution = "0.053" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0.552864109927935", Percentage Contribution = "0.023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6.54328846588002", Percentage Contribution = "0.406" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0.0356701324564961", Percentage Contribution = "0.028" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0.528459143213594", Percentage Contribution = "0.099" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0.65850313905602", Percentage Contribution = "0.181" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0.117780784704465", Percentage Contribution = "-0.033" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0.808657987543547", Percentage Contribution = "0.048" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1.98045625169655", Percentage Contribution = "0.075" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0.957566675745166", Percentage Contribution = "0.064" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0.0262546721248633", Percentage Contribution = "-0.022" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0.19835359905492", Percentage Contribution = "0.063" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0.240439149219498", Percentage Contribution = "0.058" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0.394262692010798", Percentage Contribution = "0.017" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6.67093073538981", Percentage Contribution = "0.415" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0.0364048216861083", Percentage Contribution = "0.027" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0.522562212303566", Percentage Contribution = "0.107" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0.649409943962926", Percentage Contribution = "0.181" } Reintegrate > Peak scoring model > Use decoys is True Reintegrate > Peak scoring model > Uses second best peaks is False Reintegrate > Integrate all peaks is True @@ -88,62 +88,62 @@ Extra Info: Peak scoring model = "test1": [ { Score Name = "Intensity", - Weight = "-0.109478368757285", - Percentage Contribution = "-0.03" + Weight = "-0.117780784704465", + Percentage Contribution = "-0.033" }, { Score Name = "Retention time difference", - Weight = "-0.768944435803533", - Percentage Contribution = "0.067" + Weight = "-0.808657987543547", + Percentage Contribution = "0.048" }, { Score Name = "Library intensity dot-product", - Weight = "1.91473907688983", - Percentage Contribution = "0.071" + Weight = "1.98045625169655", + Percentage Contribution = "0.075" }, { Score Name = "Shape (weighted)", - Weight = "0.964669685201429", + Weight = "0.957566675745166", Percentage Contribution = "0.064" }, { Score Name = "Co-elution (weighted)", - Weight = "0.02650942606722", - Percentage Contribution = "-0.023" + Weight = "0.0262546721248633", + Percentage Contribution = "-0.022" }, { Score Name = "Co-elution count", - Weight = "0.182160083950605", - Percentage Contribution = "0.062" + Weight = "0.19835359905492", + Percentage Contribution = "0.063" }, { Score Name = "Signal to noise", - Weight = "0.222905461284023", - Percentage Contribution = "0.053" + Weight = "0.240439149219498", + Percentage Contribution = "0.058" }, { Score Name = "Reference intensity dot-product", - Weight = "0.552864109927935", - Percentage Contribution = "0.023" + Weight = "0.394262692010798", + Percentage Contribution = "0.017" }, { Score Name = "Reference shape (weighted)", - Weight = "6.54328846588002", - Percentage Contribution = "0.406" + Weight = "6.67093073538981", + Percentage Contribution = "0.415" }, { Score Name = "Reference co-elution (weighted)", - Weight = "-0.0356701324564961", - Percentage Contribution = "0.028" + Weight = "-0.0364048216861083", + Percentage Contribution = "0.027" }, { Score Name = "Reference co-elution count", - Weight = "0.528459143213594", - Percentage Contribution = "0.099" + Weight = "0.522562212303566", + Percentage Contribution = "0.107" }, { Score Name = "Standard Intensity", - Weight = "0.65850313905602", + Weight = "0.649409943962926", Percentage Contribution = "0.181" } ], @@ -158,18 +158,18 @@ Summary : Reintegrated peaks using "test1" All Info : Reintegrated peaks using "test1" Reintegrate > Peak scoring model is "test1" -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0.109478368757285", Percentage Contribution = "-0.03" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0.768944435803533", Percentage Contribution = "0.067" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1.91473907688983", Percentage Contribution = "0.071" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0.964669685201429", Percentage Contribution = "0.064" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0.02650942606722", Percentage Contribution = "-0.023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0.182160083950605", Percentage Contribution = "0.062" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0.222905461284023", Percentage Contribution = "0.053" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0.552864109927935", Percentage Contribution = "0.023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6.54328846588002", Percentage Contribution = "0.406" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0.0356701324564961", Percentage Contribution = "0.028" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0.528459143213594", Percentage Contribution = "0.099" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0.65850313905602", Percentage Contribution = "0.181" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0.117780784704465", Percentage Contribution = "-0.033" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0.808657987543547", Percentage Contribution = "0.048" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1.98045625169655", Percentage Contribution = "0.075" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0.957566675745166", Percentage Contribution = "0.064" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0.0262546721248633", Percentage Contribution = "-0.022" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0.19835359905492", Percentage Contribution = "0.063" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0.240439149219498", Percentage Contribution = "0.058" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0.394262692010798", Percentage Contribution = "0.017" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6.67093073538981", Percentage Contribution = "0.415" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0.0364048216861083", Percentage Contribution = "0.027" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0.522562212303566", Percentage Contribution = "0.107" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0.649409943962926", Percentage Contribution = "0.181" } Reintegrate > Peak scoring model > Use decoys is True Reintegrate > Peak scoring model > Uses second best peaks is False Reintegrate > Only integrate significant q values is True @@ -181,62 +181,62 @@ Extra Info: Peak scoring model = "test1": [ { Score Name = "Intensity", - Weight = "-0.109478368757285", - Percentage Contribution = "-0.03" + Weight = "-0.117780784704465", + Percentage Contribution = "-0.033" }, { Score Name = "Retention time difference", - Weight = "-0.768944435803533", - Percentage Contribution = "0.067" + Weight = "-0.808657987543547", + Percentage Contribution = "0.048" }, { Score Name = "Library intensity dot-product", - Weight = "1.91473907688983", - Percentage Contribution = "0.071" + Weight = "1.98045625169655", + Percentage Contribution = "0.075" }, { Score Name = "Shape (weighted)", - Weight = "0.964669685201429", + Weight = "0.957566675745166", Percentage Contribution = "0.064" }, { Score Name = "Co-elution (weighted)", - Weight = "0.02650942606722", - Percentage Contribution = "-0.023" + Weight = "0.0262546721248633", + Percentage Contribution = "-0.022" }, { Score Name = "Co-elution count", - Weight = "0.182160083950605", - Percentage Contribution = "0.062" + Weight = "0.19835359905492", + Percentage Contribution = "0.063" }, { Score Name = "Signal to noise", - Weight = "0.222905461284023", - Percentage Contribution = "0.053" + Weight = "0.240439149219498", + Percentage Contribution = "0.058" }, { Score Name = "Reference intensity dot-product", - Weight = "0.552864109927935", - Percentage Contribution = "0.023" + Weight = "0.394262692010798", + Percentage Contribution = "0.017" }, { Score Name = "Reference shape (weighted)", - Weight = "6.54328846588002", - Percentage Contribution = "0.406" + Weight = "6.67093073538981", + Percentage Contribution = "0.415" }, { Score Name = "Reference co-elution (weighted)", - Weight = "-0.0356701324564961", - Percentage Contribution = "0.028" + Weight = "-0.0364048216861083", + Percentage Contribution = "0.027" }, { Score Name = "Reference co-elution count", - Weight = "0.528459143213594", - Percentage Contribution = "0.099" + Weight = "0.522562212303566", + Percentage Contribution = "0.107" }, { Score Name = "Standard Intensity", - Weight = "0.65850313905602", + Weight = "0.649409943962926", Percentage Contribution = "0.181" } ], @@ -252,11 +252,11 @@ Summary : Reintegrated peaks using "testDIA" All Info : Reintegrated peaks using "testDIA" Reintegrate > Peak scoring model is "testDIA" -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "0.290033468085838", Percentage Contribution = "0.067" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "5.98411479906022", Percentage Contribution = "0.519" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "-0.0623805713290799", Percentage Contribution = "0.103" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0.668140904775243", Percentage Contribution = "0.15" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0.796767785174247", Percentage Contribution = "0.161" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "0.272541894208857", Percentage Contribution = "0.062" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "6.02846861183062", Percentage Contribution = "0.513" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "-0.0646032707611339", Percentage Contribution = "0.104" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0.673690244719619", Percentage Contribution = "0.159" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0.812093607032469", Percentage Contribution = "0.161" } Reintegrate > Peak scoring model > Use decoys is False Reintegrate > Peak scoring model > Uses second best peaks is True Reintegrate > Integrate all peaks is True @@ -267,27 +267,27 @@ Extra Info: Peak scoring model = "testDIA": [ { Score Name = "Intensity", - Weight = "0.290033468085838", - Percentage Contribution = "0.067" + Weight = "0.272541894208857", + Percentage Contribution = "0.062" }, { Score Name = "Shape (weighted)", - Weight = "5.98411479906022", - Percentage Contribution = "0.519" + Weight = "6.02846861183062", + Percentage Contribution = "0.513" }, { Score Name = "Co-elution (weighted)", - Weight = "-0.0623805713290799", - Percentage Contribution = "0.103" + Weight = "-0.0646032707611339", + Percentage Contribution = "0.104" }, { Score Name = "Co-elution count", - Weight = "0.668140904775243", - Percentage Contribution = "0.15" + Weight = "0.673690244719619", + Percentage Contribution = "0.159" }, { Score Name = "Signal to noise", - Weight = "0.796767785174247", + Weight = "0.812093607032469", Percentage Contribution = "0.161" } ], diff --git a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/fr/TestGroupedStudies1Tutorial.log b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/fr/TestGroupedStudies1Tutorial.log index 4a21f8d537d..8b858f2b130 100644 --- a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/fr/TestGroupedStudies1Tutorial.log +++ b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/fr/TestGroupedStudies1Tutorial.log @@ -162,8 +162,8 @@ Undo Redo : Changed peak bounds of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,31 Summary : Changed peak bounds of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ All Info : Changed peak bounds of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ -Changed start time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ in "D_108_REP2" from 27,79 to 26,86 -Changed end time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ in "D_108_REP2" from 28,72 to 27,40 +Changed start time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ in "D_108_REP2" from 27,13 to 26,86 +Changed end time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ in "D_108_REP2" from 27,86 to 27,40 Undo Redo : Deleted target NP_036870 > K.FGLYSDQMR.E [182, 190] Summary : Deleted target NP_036870 > K.FGLYSDQMR.E [182, 190] @@ -338,10 +338,10 @@ Summary : Set MissingData of DFATVYVDAVK to True All Info : Set MissingData of DFATVYVDAVK to True -Undo Redo : Pasted 89 values into the document grid -Summary : Pasted 89 values into the document grid +Undo Redo : Pasted 91 values into the document grid +Summary : Pasted 91 values into the document grid All Info : -Pasted 89 values into the document grid +Pasted 91 values into the document grid Document grid > Report name is "Truncated Precursors" Document grid > Column settings > Column Sorts : contains { Column = "Count Truncated", Direction = "Descending" } Set MissingData of DFATVYVDAVK to True @@ -349,6 +349,7 @@ Set MissingData of DFATVYVDAVK to True Set MissingData of DFATVYVDAVK to True Set MissingData of DFATVYVDAVK to True Set MissingData of DFATVYVDAVK to True +Set MissingData of DFATVYVDAVK to True Set MissingData of DYVSQFESSTLGK to True Set MissingData of DYVSQFESSTLGK to True Set MissingData of DYVSQFESSTLGK to True @@ -358,6 +359,7 @@ Set MissingData of C[+57.021464]SLPRPWALTFSYGR to True Set MissingData of TGTNLMDFLSR to True Set MissingData of TGTNLMDFLSR to True Set MissingData of TGTNLMDFLSR to True +Set MissingData of TGTNLMDFLSR to True Set MissingData of ASGIIDTLFQDR to True Set MissingData of ASGIIDTLFQDR to True Set MissingData of SDFQVPC[+57.021464]QYSQQLK to True @@ -522,6 +524,8 @@ TRUE TRUE TRUE TRUE +TRUE +TRUE Report name = "Truncated Precursors", Column settings = diff --git a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/fr/TestPeakPickingTutorial.log b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/fr/TestPeakPickingTutorial.log index e7ad74ca97a..716c466449a 100644 --- a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/fr/TestPeakPickingTutorial.log +++ b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/fr/TestPeakPickingTutorial.log @@ -66,18 +66,18 @@ Summary : Reintegrated peaks using "test1" All Info : Reintegrated peaks using "test1" Reintegrate > Peak scoring model is "test1" -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0,109478368757285", Percentage Contribution = "-0,03" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0,768944435803533", Percentage Contribution = "0,067" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1,91473907688983", Percentage Contribution = "0,071" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0,964669685201429", Percentage Contribution = "0,064" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0,02650942606722", Percentage Contribution = "-0,023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,182160083950605", Percentage Contribution = "0,062" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,222905461284023", Percentage Contribution = "0,053" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0,552864109927935", Percentage Contribution = "0,023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6,54328846588002", Percentage Contribution = "0,406" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0,0356701324564961", Percentage Contribution = "0,028" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0,528459143213594", Percentage Contribution = "0,099" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0,65850313905602", Percentage Contribution = "0,181" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0,117780784704465", Percentage Contribution = "-0,033" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0,808657987543547", Percentage Contribution = "0,048" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1,98045625169655", Percentage Contribution = "0,075" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0,957566675745166", Percentage Contribution = "0,064" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0,0262546721248633", Percentage Contribution = "-0,022" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,19835359905492", Percentage Contribution = "0,063" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,240439149219498", Percentage Contribution = "0,058" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0,394262692010798", Percentage Contribution = "0,017" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6,67093073538981", Percentage Contribution = "0,415" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0,0364048216861083", Percentage Contribution = "0,027" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0,522562212303566", Percentage Contribution = "0,107" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0,649409943962926", Percentage Contribution = "0,181" } Reintegrate > Peak scoring model > Use decoys is True Reintegrate > Peak scoring model > Uses second best peaks is False Reintegrate > Integrate all peaks is True @@ -88,62 +88,62 @@ Extra Info: Peak scoring model = "test1": [ { Score Name = "Intensity", - Weight = "-0,109478368757285", - Percentage Contribution = "-0,03" + Weight = "-0,117780784704465", + Percentage Contribution = "-0,033" }, { Score Name = "Retention time difference", - Weight = "-0,768944435803533", - Percentage Contribution = "0,067" + Weight = "-0,808657987543547", + Percentage Contribution = "0,048" }, { Score Name = "Library intensity dot-product", - Weight = "1,91473907688983", - Percentage Contribution = "0,071" + Weight = "1,98045625169655", + Percentage Contribution = "0,075" }, { Score Name = "Shape (weighted)", - Weight = "0,964669685201429", + Weight = "0,957566675745166", Percentage Contribution = "0,064" }, { Score Name = "Co-elution (weighted)", - Weight = "0,02650942606722", - Percentage Contribution = "-0,023" + Weight = "0,0262546721248633", + Percentage Contribution = "-0,022" }, { Score Name = "Co-elution count", - Weight = "0,182160083950605", - Percentage Contribution = "0,062" + Weight = "0,19835359905492", + Percentage Contribution = "0,063" }, { Score Name = "Signal to noise", - Weight = "0,222905461284023", - Percentage Contribution = "0,053" + Weight = "0,240439149219498", + Percentage Contribution = "0,058" }, { Score Name = "Reference intensity dot-product", - Weight = "0,552864109927935", - Percentage Contribution = "0,023" + Weight = "0,394262692010798", + Percentage Contribution = "0,017" }, { Score Name = "Reference shape (weighted)", - Weight = "6,54328846588002", - Percentage Contribution = "0,406" + Weight = "6,67093073538981", + Percentage Contribution = "0,415" }, { Score Name = "Reference co-elution (weighted)", - Weight = "-0,0356701324564961", - Percentage Contribution = "0,028" + Weight = "-0,0364048216861083", + Percentage Contribution = "0,027" }, { Score Name = "Reference co-elution count", - Weight = "0,528459143213594", - Percentage Contribution = "0,099" + Weight = "0,522562212303566", + Percentage Contribution = "0,107" }, { Score Name = "Standard Intensity", - Weight = "0,65850313905602", + Weight = "0,649409943962926", Percentage Contribution = "0,181" } ], @@ -158,18 +158,18 @@ Summary : Reintegrated peaks using "test1" All Info : Reintegrated peaks using "test1" Reintegrate > Peak scoring model is "test1" -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0,109478368757285", Percentage Contribution = "-0,03" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0,768944435803533", Percentage Contribution = "0,067" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1,91473907688983", Percentage Contribution = "0,071" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0,964669685201429", Percentage Contribution = "0,064" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0,02650942606722", Percentage Contribution = "-0,023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,182160083950605", Percentage Contribution = "0,062" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,222905461284023", Percentage Contribution = "0,053" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0,552864109927935", Percentage Contribution = "0,023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6,54328846588002", Percentage Contribution = "0,406" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0,0356701324564961", Percentage Contribution = "0,028" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0,528459143213594", Percentage Contribution = "0,099" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0,65850313905602", Percentage Contribution = "0,181" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0,117780784704465", Percentage Contribution = "-0,033" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0,808657987543547", Percentage Contribution = "0,048" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1,98045625169655", Percentage Contribution = "0,075" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0,957566675745166", Percentage Contribution = "0,064" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0,0262546721248633", Percentage Contribution = "-0,022" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,19835359905492", Percentage Contribution = "0,063" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,240439149219498", Percentage Contribution = "0,058" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0,394262692010798", Percentage Contribution = "0,017" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6,67093073538981", Percentage Contribution = "0,415" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0,0364048216861083", Percentage Contribution = "0,027" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0,522562212303566", Percentage Contribution = "0,107" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0,649409943962926", Percentage Contribution = "0,181" } Reintegrate > Peak scoring model > Use decoys is True Reintegrate > Peak scoring model > Uses second best peaks is False Reintegrate > Only integrate significant q values is True @@ -181,62 +181,62 @@ Extra Info: Peak scoring model = "test1": [ { Score Name = "Intensity", - Weight = "-0,109478368757285", - Percentage Contribution = "-0,03" + Weight = "-0,117780784704465", + Percentage Contribution = "-0,033" }, { Score Name = "Retention time difference", - Weight = "-0,768944435803533", - Percentage Contribution = "0,067" + Weight = "-0,808657987543547", + Percentage Contribution = "0,048" }, { Score Name = "Library intensity dot-product", - Weight = "1,91473907688983", - Percentage Contribution = "0,071" + Weight = "1,98045625169655", + Percentage Contribution = "0,075" }, { Score Name = "Shape (weighted)", - Weight = "0,964669685201429", + Weight = "0,957566675745166", Percentage Contribution = "0,064" }, { Score Name = "Co-elution (weighted)", - Weight = "0,02650942606722", - Percentage Contribution = "-0,023" + Weight = "0,0262546721248633", + Percentage Contribution = "-0,022" }, { Score Name = "Co-elution count", - Weight = "0,182160083950605", - Percentage Contribution = "0,062" + Weight = "0,19835359905492", + Percentage Contribution = "0,063" }, { Score Name = "Signal to noise", - Weight = "0,222905461284023", - Percentage Contribution = "0,053" + Weight = "0,240439149219498", + Percentage Contribution = "0,058" }, { Score Name = "Reference intensity dot-product", - Weight = "0,552864109927935", - Percentage Contribution = "0,023" + Weight = "0,394262692010798", + Percentage Contribution = "0,017" }, { Score Name = "Reference shape (weighted)", - Weight = "6,54328846588002", - Percentage Contribution = "0,406" + Weight = "6,67093073538981", + Percentage Contribution = "0,415" }, { Score Name = "Reference co-elution (weighted)", - Weight = "-0,0356701324564961", - Percentage Contribution = "0,028" + Weight = "-0,0364048216861083", + Percentage Contribution = "0,027" }, { Score Name = "Reference co-elution count", - Weight = "0,528459143213594", - Percentage Contribution = "0,099" + Weight = "0,522562212303566", + Percentage Contribution = "0,107" }, { Score Name = "Standard Intensity", - Weight = "0,65850313905602", + Weight = "0,649409943962926", Percentage Contribution = "0,181" } ], @@ -252,11 +252,11 @@ Summary : Reintegrated peaks using "testDIA" All Info : Reintegrated peaks using "testDIA" Reintegrate > Peak scoring model is "testDIA" -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "0,290033468085838", Percentage Contribution = "0,067" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "5,98411479906022", Percentage Contribution = "0,519" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "-0,0623805713290799", Percentage Contribution = "0,103" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,668140904775243", Percentage Contribution = "0,15" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,796767785174247", Percentage Contribution = "0,161" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "0,272541894208857", Percentage Contribution = "0,062" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "6,02846861183062", Percentage Contribution = "0,513" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "-0,0646032707611339", Percentage Contribution = "0,104" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,673690244719619", Percentage Contribution = "0,159" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,812093607032469", Percentage Contribution = "0,161" } Reintegrate > Peak scoring model > Use decoys is False Reintegrate > Peak scoring model > Uses second best peaks is True Reintegrate > Integrate all peaks is True @@ -267,27 +267,27 @@ Extra Info: Peak scoring model = "testDIA": [ { Score Name = "Intensity", - Weight = "0,290033468085838", - Percentage Contribution = "0,067" + Weight = "0,272541894208857", + Percentage Contribution = "0,062" }, { Score Name = "Shape (weighted)", - Weight = "5,98411479906022", - Percentage Contribution = "0,519" + Weight = "6,02846861183062", + Percentage Contribution = "0,513" }, { Score Name = "Co-elution (weighted)", - Weight = "-0,0623805713290799", - Percentage Contribution = "0,103" + Weight = "-0,0646032707611339", + Percentage Contribution = "0,104" }, { Score Name = "Co-elution count", - Weight = "0,668140904775243", - Percentage Contribution = "0,15" + Weight = "0,673690244719619", + Percentage Contribution = "0,159" }, { Score Name = "Signal to noise", - Weight = "0,796767785174247", + Weight = "0,812093607032469", Percentage Contribution = "0,161" } ], diff --git a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/ja/TestGroupedStudies1Tutorial.log b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/ja/TestGroupedStudies1Tutorial.log index 9b67a86c88f..c0b64af3c42 100644 --- a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/ja/TestGroupedStudies1Tutorial.log +++ b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/ja/TestGroupedStudies1Tutorial.log @@ -162,8 +162,8 @@ Undo Redo : NP_036870>K.DFATVYVDAVK.D [36, 46]>614.3164++のピーク境界が Summary : NP_036870>K.DFATVYVDAVK.D [36, 46]>614.3164++のピーク境界が変更されました All Info : NP_036870>K.DFATVYVDAVK.D [36, 46]>614.3164++のピーク境界が変更されました -「D_108_REP2」にあるNP_036870>K.DFATVYVDAVK.D [36, 46]>614.3164++の全ピークの開始時間が27.79から26.86に変更されました -「D_108_REP2」にあるNP_036870>K.DFATVYVDAVK.D [36, 46]>614.3164++の全ピークの終了時間が28.72から27.40に変更されました +「D_108_REP2」にあるNP_036870>K.DFATVYVDAVK.D [36, 46]>614.3164++の全ピークの開始時間が27.13から26.86に変更されました +「D_108_REP2」にあるNP_036870>K.DFATVYVDAVK.D [36, 46]>614.3164++の全ピークの終了時間が27.86から27.40に変更されました Undo Redo : ターゲットNP_036870>K.FGLYSDQMR.E [182, 190]が削除されました Summary : ターゲットNP_036870>K.FGLYSDQMR.E [182, 190]が削除されました @@ -338,10 +338,10 @@ Summary : DFATVYVDAVKのMissingDataが真に設定されました All Info : DFATVYVDAVKのMissingDataが真に設定されました -Undo Redo : ドキュメントグリッドに89個の値が貼り付けられました -Summary : ドキュメントグリッドに89個の値が貼り付けられました +Undo Redo : ドキュメントグリッドに91個の値が貼り付けられました +Summary : ドキュメントグリッドに91個の値が貼り付けられました All Info : -ドキュメントグリッドに89個の値が貼り付けられました +ドキュメントグリッドに91個の値が貼り付けられました ドキュメントグリッド>レポート名は"Truncated Precursors"です ドキュメントグリッド>列の設定>列のソート：{ 列 = "切断数", 方向 = "降順" }が含まれています DFATVYVDAVKのMissingDataが真に設定されました @@ -349,6 +349,7 @@ DFATVYVDAVKのMissingDataが真に設定されました DFATVYVDAVKのMissingDataが真に設定されました DFATVYVDAVKのMissingDataが真に設定されました DFATVYVDAVKのMissingDataが真に設定されました +DFATVYVDAVKのMissingDataが真に設定されました DYVSQFESSTLGKのMissingDataが真に設定されました DYVSQFESSTLGKのMissingDataが真に設定されました DYVSQFESSTLGKのMissingDataが真に設定されました @@ -358,6 +359,7 @@ C[+57.021464]SLPRPWALTFSYGRのMissingDataが真に設定されました TGTNLMDFLSRのMissingDataが真に設定されました TGTNLMDFLSRのMissingDataが真に設定されました TGTNLMDFLSRのMissingDataが真に設定されました +TGTNLMDFLSRのMissingDataが真に設定されました ASGIIDTLFQDRのMissingDataが真に設定されました ASGIIDTLFQDRのMissingDataが真に設定されました SDFQVPC[+57.021464]QYSQQLKのMissingDataが真に設定されました @@ -522,6 +524,8 @@ TRUE TRUE TRUE TRUE +TRUE +TRUE レポート名 = "Truncated Precursors", 列の設定 = diff --git a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/ja/TestPeakPickingTutorial.log b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/ja/TestPeakPickingTutorial.log index 555b6421a38..eb6a6de8fb1 100644 --- a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/ja/TestPeakPickingTutorial.log +++ b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/ja/TestPeakPickingTutorial.log @@ -66,18 +66,18 @@ Summary : 「test1」を使用してピークが再積分されました All Info : 「test1」を使用してピークが再積分されました 再積分>ピークスコアモデルは"test1"です -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "強度", 加重 = "-0.109478368757285", 貢献度 = "-0.03" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "保持時間の差異", 加重 = "-0.768944435803533", 貢献度 = "0.067" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "ライブラリ強度内積", 加重 = "1.91473907688983", 貢献度 = "0.071" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "形（加重）", 加重 = "0.964669685201429", 貢献度 = "0.064" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出（加重）", 加重 = "0.02650942606722", 貢献度 = "-0.023" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出数", 加重 = "0.182160083950605", 貢献度 = "0.062" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "信号対ノイズ比", 加重 = "0.222905461284023", 貢献度 = "0.053" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照強度内積", 加重 = "0.552864109927935", 貢献度 = "0.023" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照形（加重）", 加重 = "6.54328846588002", 貢献度 = "0.406" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照共溶出（加重）", 加重 = "-0.0356701324564961", 貢献度 = "0.028" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照共溶出数", 加重 = "0.528459143213594", 貢献度 = "0.099" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "標準強度", 加重 = "0.65850313905602", 貢献度 = "0.181" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "強度", 加重 = "-0.117780784704465", 貢献度 = "-0.033" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "保持時間の差異", 加重 = "-0.808657987543547", 貢献度 = "0.048" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "ライブラリ強度内積", 加重 = "1.98045625169655", 貢献度 = "0.075" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "形（加重）", 加重 = "0.957566675745166", 貢献度 = "0.064" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出（加重）", 加重 = "0.0262546721248633", 貢献度 = "-0.022" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出数", 加重 = "0.19835359905492", 貢献度 = "0.063" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "信号対ノイズ比", 加重 = "0.240439149219498", 貢献度 = "0.058" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照強度内積", 加重 = "0.394262692010798", 貢献度 = "0.017" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照形（加重）", 加重 = "6.67093073538981", 貢献度 = "0.415" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照共溶出（加重）", 加重 = "-0.0364048216861083", 貢献度 = "0.027" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照共溶出数", 加重 = "0.522562212303566", 貢献度 = "0.107" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "標準強度", 加重 = "0.649409943962926", 貢献度 = "0.181" }が含まれています 再積分>ピークスコアモデル>デコイを使用するは真です 再積分>ピークスコアモデル>2番目に良いピークを使用は偽です 再積分>すべてのピークを積分は真です @@ -88,62 +88,62 @@ Extra Info: ピークスコアモデル = "test1": [ { スコア名 = "強度", - 加重 = "-0.109478368757285", - 貢献度 = "-0.03" + 加重 = "-0.117780784704465", + 貢献度 = "-0.033" }, { スコア名 = "保持時間の差異", - 加重 = "-0.768944435803533", - 貢献度 = "0.067" + 加重 = "-0.808657987543547", + 貢献度 = "0.048" }, { スコア名 = "ライブラリ強度内積", - 加重 = "1.91473907688983", - 貢献度 = "0.071" + 加重 = "1.98045625169655", + 貢献度 = "0.075" }, { スコア名 = "形（加重）", - 加重 = "0.964669685201429", + 加重 = "0.957566675745166", 貢献度 = "0.064" }, { スコア名 = "共溶出（加重）", - 加重 = "0.02650942606722", - 貢献度 = "-0.023" + 加重 = "0.0262546721248633", + 貢献度 = "-0.022" }, { スコア名 = "共溶出数", - 加重 = "0.182160083950605", - 貢献度 = "0.062" + 加重 = "0.19835359905492", + 貢献度 = "0.063" }, { スコア名 = "信号対ノイズ比", - 加重 = "0.222905461284023", - 貢献度 = "0.053" + 加重 = "0.240439149219498", + 貢献度 = "0.058" }, { スコア名 = "参照強度内積", - 加重 = "0.552864109927935", - 貢献度 = "0.023" + 加重 = "0.394262692010798", + 貢献度 = "0.017" }, { スコア名 = "参照形（加重）", - 加重 = "6.54328846588002", - 貢献度 = "0.406" + 加重 = "6.67093073538981", + 貢献度 = "0.415" }, { スコア名 = "参照共溶出（加重）", - 加重 = "-0.0356701324564961", - 貢献度 = "0.028" + 加重 = "-0.0364048216861083", + 貢献度 = "0.027" }, { スコア名 = "参照共溶出数", - 加重 = "0.528459143213594", - 貢献度 = "0.099" + 加重 = "0.522562212303566", + 貢献度 = "0.107" }, { スコア名 = "標準強度", - 加重 = "0.65850313905602", + 加重 = "0.649409943962926", 貢献度 = "0.181" } ], @@ -158,18 +158,18 @@ Summary : 「test1」を使用してピークが再積分されました All Info : 「test1」を使用してピークが再積分されました 再積分>ピークスコアモデルは"test1"です -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "強度", 加重 = "-0.109478368757285", 貢献度 = "-0.03" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "保持時間の差異", 加重 = "-0.768944435803533", 貢献度 = "0.067" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "ライブラリ強度内積", 加重 = "1.91473907688983", 貢献度 = "0.071" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "形（加重）", 加重 = "0.964669685201429", 貢献度 = "0.064" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出（加重）", 加重 = "0.02650942606722", 貢献度 = "-0.023" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出数", 加重 = "0.182160083950605", 貢献度 = "0.062" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "信号対ノイズ比", 加重 = "0.222905461284023", 貢献度 = "0.053" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照強度内積", 加重 = "0.552864109927935", 貢献度 = "0.023" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照形（加重）", 加重 = "6.54328846588002", 貢献度 = "0.406" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照共溶出（加重）", 加重 = "-0.0356701324564961", 貢献度 = "0.028" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照共溶出数", 加重 = "0.528459143213594", 貢献度 = "0.099" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "標準強度", 加重 = "0.65850313905602", 貢献度 = "0.181" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "強度", 加重 = "-0.117780784704465", 貢献度 = "-0.033" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "保持時間の差異", 加重 = "-0.808657987543547", 貢献度 = "0.048" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "ライブラリ強度内積", 加重 = "1.98045625169655", 貢献度 = "0.075" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "形（加重）", 加重 = "0.957566675745166", 貢献度 = "0.064" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出（加重）", 加重 = "0.0262546721248633", 貢献度 = "-0.022" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出数", 加重 = "0.19835359905492", 貢献度 = "0.063" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "信号対ノイズ比", 加重 = "0.240439149219498", 貢献度 = "0.058" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照強度内積", 加重 = "0.394262692010798", 貢献度 = "0.017" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照形（加重）", 加重 = "6.67093073538981", 貢献度 = "0.415" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照共溶出（加重）", 加重 = "-0.0364048216861083", 貢献度 = "0.027" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "参照共溶出数", 加重 = "0.522562212303566", 貢献度 = "0.107" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "標準強度", 加重 = "0.649409943962926", 貢献度 = "0.181" }が含まれています 再積分>ピークスコアモデル>デコイを使用するは真です 再積分>ピークスコアモデル>2番目に良いピークを使用は偽です 再積分>有意なQ値のみを積分は真です @@ -181,62 +181,62 @@ Extra Info: ピークスコアモデル = "test1": [ { スコア名 = "強度", - 加重 = "-0.109478368757285", - 貢献度 = "-0.03" + 加重 = "-0.117780784704465", + 貢献度 = "-0.033" }, { スコア名 = "保持時間の差異", - 加重 = "-0.768944435803533", - 貢献度 = "0.067" + 加重 = "-0.808657987543547", + 貢献度 = "0.048" }, { スコア名 = "ライブラリ強度内積", - 加重 = "1.91473907688983", - 貢献度 = "0.071" + 加重 = "1.98045625169655", + 貢献度 = "0.075" }, { スコア名 = "形（加重）", - 加重 = "0.964669685201429", + 加重 = "0.957566675745166", 貢献度 = "0.064" }, { スコア名 = "共溶出（加重）", - 加重 = "0.02650942606722", - 貢献度 = "-0.023" + 加重 = "0.0262546721248633", + 貢献度 = "-0.022" }, { スコア名 = "共溶出数", - 加重 = "0.182160083950605", - 貢献度 = "0.062" + 加重 = "0.19835359905492", + 貢献度 = "0.063" }, { スコア名 = "信号対ノイズ比", - 加重 = "0.222905461284023", - 貢献度 = "0.053" + 加重 = "0.240439149219498", + 貢献度 = "0.058" }, { スコア名 = "参照強度内積", - 加重 = "0.552864109927935", - 貢献度 = "0.023" + 加重 = "0.394262692010798", + 貢献度 = "0.017" }, { スコア名 = "参照形（加重）", - 加重 = "6.54328846588002", - 貢献度 = "0.406" + 加重 = "6.67093073538981", + 貢献度 = "0.415" }, { スコア名 = "参照共溶出（加重）", - 加重 = "-0.0356701324564961", - 貢献度 = "0.028" + 加重 = "-0.0364048216861083", + 貢献度 = "0.027" }, { スコア名 = "参照共溶出数", - 加重 = "0.528459143213594", - 貢献度 = "0.099" + 加重 = "0.522562212303566", + 貢献度 = "0.107" }, { スコア名 = "標準強度", - 加重 = "0.65850313905602", + 加重 = "0.649409943962926", 貢献度 = "0.181" } ], @@ -252,11 +252,11 @@ Summary : 「testDIA」を使用してピークが再積分されました All Info : 「testDIA」を使用してピークが再積分されました 再積分>ピークスコアモデルは"testDIA"です -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "強度", 加重 = "0.290033468085838", 貢献度 = "0.067" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "形（加重）", 加重 = "5.98411479906022", 貢献度 = "0.519" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出（加重）", 加重 = "-0.0623805713290799", 貢献度 = "0.103" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出数", 加重 = "0.668140904775243", 貢献度 = "0.15" }が含まれています -再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "信号対ノイズ比", 加重 = "0.796767785174247", 貢献度 = "0.161" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "強度", 加重 = "0.272541894208857", 貢献度 = "0.062" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "形（加重）", 加重 = "6.02846861183062", 貢献度 = "0.513" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出（加重）", 加重 = "-0.0646032707611339", 貢献度 = "0.104" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "共溶出数", 加重 = "0.673690244719619", 貢献度 = "0.159" }が含まれています +再積分>ピークスコアモデル>フィーチャースコア：{ スコア名 = "信号対ノイズ比", 加重 = "0.812093607032469", 貢献度 = "0.161" }が含まれています 再積分>ピークスコアモデル>デコイを使用するは偽です 再積分>ピークスコアモデル>2番目に良いピークを使用は真です 再積分>すべてのピークを積分は真です @@ -267,27 +267,27 @@ Extra Info: ピークスコアモデル = "testDIA": [ { スコア名 = "強度", - 加重 = "0.290033468085838", - 貢献度 = "0.067" + 加重 = "0.272541894208857", + 貢献度 = "0.062" }, { スコア名 = "形（加重）", - 加重 = "5.98411479906022", - 貢献度 = "0.519" + 加重 = "6.02846861183062", + 貢献度 = "0.513" }, { スコア名 = "共溶出（加重）", - 加重 = "-0.0623805713290799", - 貢献度 = "0.103" + 加重 = "-0.0646032707611339", + 貢献度 = "0.104" }, { スコア名 = "共溶出数", - 加重 = "0.668140904775243", - 貢献度 = "0.15" + 加重 = "0.673690244719619", + 貢献度 = "0.159" }, { スコア名 = "信号対ノイズ比", - 加重 = "0.796767785174247", + 加重 = "0.812093607032469", 貢献度 = "0.161" } ], diff --git a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/tr/TestGroupedStudies1Tutorial.log b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/tr/TestGroupedStudies1Tutorial.log index 4a21f8d537d..8b858f2b130 100644 --- a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/tr/TestGroupedStudies1Tutorial.log +++ b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/tr/TestGroupedStudies1Tutorial.log @@ -162,8 +162,8 @@ Undo Redo : Changed peak bounds of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,31 Summary : Changed peak bounds of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ All Info : Changed peak bounds of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ -Changed start time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ in "D_108_REP2" from 27,79 to 26,86 -Changed end time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ in "D_108_REP2" from 28,72 to 27,40 +Changed start time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ in "D_108_REP2" from 27,13 to 26,86 +Changed end time of all peaks of NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614,3164++ in "D_108_REP2" from 27,86 to 27,40 Undo Redo : Deleted target NP_036870 > K.FGLYSDQMR.E [182, 190] Summary : Deleted target NP_036870 > K.FGLYSDQMR.E [182, 190] @@ -338,10 +338,10 @@ Summary : Set MissingData of DFATVYVDAVK to True All Info : Set MissingData of DFATVYVDAVK to True -Undo Redo : Pasted 89 values into the document grid -Summary : Pasted 89 values into the document grid +Undo Redo : Pasted 91 values into the document grid +Summary : Pasted 91 values into the document grid All Info : -Pasted 89 values into the document grid +Pasted 91 values into the document grid Document grid > Report name is "Truncated Precursors" Document grid > Column settings > Column Sorts : contains { Column = "Count Truncated", Direction = "Descending" } Set MissingData of DFATVYVDAVK to True @@ -349,6 +349,7 @@ Set MissingData of DFATVYVDAVK to True Set MissingData of DFATVYVDAVK to True Set MissingData of DFATVYVDAVK to True Set MissingData of DFATVYVDAVK to True +Set MissingData of DFATVYVDAVK to True Set MissingData of DYVSQFESSTLGK to True Set MissingData of DYVSQFESSTLGK to True Set MissingData of DYVSQFESSTLGK to True @@ -358,6 +359,7 @@ Set MissingData of C[+57.021464]SLPRPWALTFSYGR to True Set MissingData of TGTNLMDFLSR to True Set MissingData of TGTNLMDFLSR to True Set MissingData of TGTNLMDFLSR to True +Set MissingData of TGTNLMDFLSR to True Set MissingData of ASGIIDTLFQDR to True Set MissingData of ASGIIDTLFQDR to True Set MissingData of SDFQVPC[+57.021464]QYSQQLK to True @@ -522,6 +524,8 @@ TRUE TRUE TRUE TRUE +TRUE +TRUE Report name = "Truncated Precursors", Column settings = diff --git a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/tr/TestPeakPickingTutorial.log b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/tr/TestPeakPickingTutorial.log index e7ad74ca97a..716c466449a 100644 --- a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/tr/TestPeakPickingTutorial.log +++ b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/tr/TestPeakPickingTutorial.log @@ -66,18 +66,18 @@ Summary : Reintegrated peaks using "test1" All Info : Reintegrated peaks using "test1" Reintegrate > Peak scoring model is "test1" -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0,109478368757285", Percentage Contribution = "-0,03" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0,768944435803533", Percentage Contribution = "0,067" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1,91473907688983", Percentage Contribution = "0,071" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0,964669685201429", Percentage Contribution = "0,064" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0,02650942606722", Percentage Contribution = "-0,023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,182160083950605", Percentage Contribution = "0,062" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,222905461284023", Percentage Contribution = "0,053" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0,552864109927935", Percentage Contribution = "0,023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6,54328846588002", Percentage Contribution = "0,406" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0,0356701324564961", Percentage Contribution = "0,028" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0,528459143213594", Percentage Contribution = "0,099" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0,65850313905602", Percentage Contribution = "0,181" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0,117780784704465", Percentage Contribution = "-0,033" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0,808657987543547", Percentage Contribution = "0,048" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1,98045625169655", Percentage Contribution = "0,075" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0,957566675745166", Percentage Contribution = "0,064" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0,0262546721248633", Percentage Contribution = "-0,022" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,19835359905492", Percentage Contribution = "0,063" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,240439149219498", Percentage Contribution = "0,058" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0,394262692010798", Percentage Contribution = "0,017" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6,67093073538981", Percentage Contribution = "0,415" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0,0364048216861083", Percentage Contribution = "0,027" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0,522562212303566", Percentage Contribution = "0,107" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0,649409943962926", Percentage Contribution = "0,181" } Reintegrate > Peak scoring model > Use decoys is True Reintegrate > Peak scoring model > Uses second best peaks is False Reintegrate > Integrate all peaks is True @@ -88,62 +88,62 @@ Extra Info: Peak scoring model = "test1": [ { Score Name = "Intensity", - Weight = "-0,109478368757285", - Percentage Contribution = "-0,03" + Weight = "-0,117780784704465", + Percentage Contribution = "-0,033" }, { Score Name = "Retention time difference", - Weight = "-0,768944435803533", - Percentage Contribution = "0,067" + Weight = "-0,808657987543547", + Percentage Contribution = "0,048" }, { Score Name = "Library intensity dot-product", - Weight = "1,91473907688983", - Percentage Contribution = "0,071" + Weight = "1,98045625169655", + Percentage Contribution = "0,075" }, { Score Name = "Shape (weighted)", - Weight = "0,964669685201429", + Weight = "0,957566675745166", Percentage Contribution = "0,064" }, { Score Name = "Co-elution (weighted)", - Weight = "0,02650942606722", - Percentage Contribution = "-0,023" + Weight = "0,0262546721248633", + Percentage Contribution = "-0,022" }, { Score Name = "Co-elution count", - Weight = "0,182160083950605", - Percentage Contribution = "0,062" + Weight = "0,19835359905492", + Percentage Contribution = "0,063" }, { Score Name = "Signal to noise", - Weight = "0,222905461284023", - Percentage Contribution = "0,053" + Weight = "0,240439149219498", + Percentage Contribution = "0,058" }, { Score Name = "Reference intensity dot-product", - Weight = "0,552864109927935", - Percentage Contribution = "0,023" + Weight = "0,394262692010798", + Percentage Contribution = "0,017" }, { Score Name = "Reference shape (weighted)", - Weight = "6,54328846588002", - Percentage Contribution = "0,406" + Weight = "6,67093073538981", + Percentage Contribution = "0,415" }, { Score Name = "Reference co-elution (weighted)", - Weight = "-0,0356701324564961", - Percentage Contribution = "0,028" + Weight = "-0,0364048216861083", + Percentage Contribution = "0,027" }, { Score Name = "Reference co-elution count", - Weight = "0,528459143213594", - Percentage Contribution = "0,099" + Weight = "0,522562212303566", + Percentage Contribution = "0,107" }, { Score Name = "Standard Intensity", - Weight = "0,65850313905602", + Weight = "0,649409943962926", Percentage Contribution = "0,181" } ], @@ -158,18 +158,18 @@ Summary : Reintegrated peaks using "test1" All Info : Reintegrated peaks using "test1" Reintegrate > Peak scoring model is "test1" -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0,109478368757285", Percentage Contribution = "-0,03" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0,768944435803533", Percentage Contribution = "0,067" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1,91473907688983", Percentage Contribution = "0,071" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0,964669685201429", Percentage Contribution = "0,064" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0,02650942606722", Percentage Contribution = "-0,023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,182160083950605", Percentage Contribution = "0,062" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,222905461284023", Percentage Contribution = "0,053" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0,552864109927935", Percentage Contribution = "0,023" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6,54328846588002", Percentage Contribution = "0,406" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0,0356701324564961", Percentage Contribution = "0,028" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0,528459143213594", Percentage Contribution = "0,099" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0,65850313905602", Percentage Contribution = "0,181" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "-0,117780784704465", Percentage Contribution = "-0,033" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Retention time difference", Weight = "-0,808657987543547", Percentage Contribution = "0,048" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Library intensity dot-product", Weight = "1,98045625169655", Percentage Contribution = "0,075" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "0,957566675745166", Percentage Contribution = "0,064" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "0,0262546721248633", Percentage Contribution = "-0,022" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,19835359905492", Percentage Contribution = "0,063" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,240439149219498", Percentage Contribution = "0,058" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference intensity dot-product", Weight = "0,394262692010798", Percentage Contribution = "0,017" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference shape (weighted)", Weight = "6,67093073538981", Percentage Contribution = "0,415" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution (weighted)", Weight = "-0,0364048216861083", Percentage Contribution = "0,027" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Reference co-elution count", Weight = "0,522562212303566", Percentage Contribution = "0,107" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Standard Intensity", Weight = "0,649409943962926", Percentage Contribution = "0,181" } Reintegrate > Peak scoring model > Use decoys is True Reintegrate > Peak scoring model > Uses second best peaks is False Reintegrate > Only integrate significant q values is True @@ -181,62 +181,62 @@ Extra Info: Peak scoring model = "test1": [ { Score Name = "Intensity", - Weight = "-0,109478368757285", - Percentage Contribution = "-0,03" + Weight = "-0,117780784704465", + Percentage Contribution = "-0,033" }, { Score Name = "Retention time difference", - Weight = "-0,768944435803533", - Percentage Contribution = "0,067" + Weight = "-0,808657987543547", + Percentage Contribution = "0,048" }, { Score Name = "Library intensity dot-product", - Weight = "1,91473907688983", - Percentage Contribution = "0,071" + Weight = "1,98045625169655", + Percentage Contribution = "0,075" }, { Score Name = "Shape (weighted)", - Weight = "0,964669685201429", + Weight = "0,957566675745166", Percentage Contribution = "0,064" }, { Score Name = "Co-elution (weighted)", - Weight = "0,02650942606722", - Percentage Contribution = "-0,023" + Weight = "0,0262546721248633", + Percentage Contribution = "-0,022" }, { Score Name = "Co-elution count", - Weight = "0,182160083950605", - Percentage Contribution = "0,062" + Weight = "0,19835359905492", + Percentage Contribution = "0,063" }, { Score Name = "Signal to noise", - Weight = "0,222905461284023", - Percentage Contribution = "0,053" + Weight = "0,240439149219498", + Percentage Contribution = "0,058" }, { Score Name = "Reference intensity dot-product", - Weight = "0,552864109927935", - Percentage Contribution = "0,023" + Weight = "0,394262692010798", + Percentage Contribution = "0,017" }, { Score Name = "Reference shape (weighted)", - Weight = "6,54328846588002", - Percentage Contribution = "0,406" + Weight = "6,67093073538981", + Percentage Contribution = "0,415" }, { Score Name = "Reference co-elution (weighted)", - Weight = "-0,0356701324564961", - Percentage Contribution = "0,028" + Weight = "-0,0364048216861083", + Percentage Contribution = "0,027" }, { Score Name = "Reference co-elution count", - Weight = "0,528459143213594", - Percentage Contribution = "0,099" + Weight = "0,522562212303566", + Percentage Contribution = "0,107" }, { Score Name = "Standard Intensity", - Weight = "0,65850313905602", + Weight = "0,649409943962926", Percentage Contribution = "0,181" } ], @@ -252,11 +252,11 @@ Summary : Reintegrated peaks using "testDIA" All Info : Reintegrated peaks using "testDIA" Reintegrate > Peak scoring model is "testDIA" -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "0,290033468085838", Percentage Contribution = "0,067" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "5,98411479906022", Percentage Contribution = "0,519" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "-0,0623805713290799", Percentage Contribution = "0,103" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,668140904775243", Percentage Contribution = "0,15" } -Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,796767785174247", Percentage Contribution = "0,161" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Intensity", Weight = "0,272541894208857", Percentage Contribution = "0,062" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Shape (weighted)", Weight = "6,02846861183062", Percentage Contribution = "0,513" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution (weighted)", Weight = "-0,0646032707611339", Percentage Contribution = "0,104" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Co-elution count", Weight = "0,673690244719619", Percentage Contribution = "0,159" } +Reintegrate > Peak scoring model > Feature scores : contains { Score Name = "Signal to noise", Weight = "0,812093607032469", Percentage Contribution = "0,161" } Reintegrate > Peak scoring model > Use decoys is False Reintegrate > Peak scoring model > Uses second best peaks is True Reintegrate > Integrate all peaks is True @@ -267,27 +267,27 @@ Extra Info: Peak scoring model = "testDIA": [ { Score Name = "Intensity", - Weight = "0,290033468085838", - Percentage Contribution = "0,067" + Weight = "0,272541894208857", + Percentage Contribution = "0,062" }, { Score Name = "Shape (weighted)", - Weight = "5,98411479906022", - Percentage Contribution = "0,519" + Weight = "6,02846861183062", + Percentage Contribution = "0,513" }, { Score Name = "Co-elution (weighted)", - Weight = "-0,0623805713290799", - Percentage Contribution = "0,103" + Weight = "-0,0646032707611339", + Percentage Contribution = "0,104" }, { Score Name = "Co-elution count", - Weight = "0,668140904775243", - Percentage Contribution = "0,15" + Weight = "0,673690244719619", + Percentage Contribution = "0,159" }, { Score Name = "Signal to noise", - Weight = "0,796767785174247", + Weight = "0,812093607032469", Percentage Contribution = "0,161" } ], diff --git a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/zh/TestGroupedStudies1Tutorial.log b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/zh/TestGroupedStudies1Tutorial.log index 49a2532ca14..fc2c19c60f4 100644 --- a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/zh/TestGroupedStudies1Tutorial.log +++ b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/zh/TestGroupedStudies1Tutorial.log @@ -162,8 +162,8 @@ Undo Redo : 已更改 NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ 的峰 Summary : 已更改 NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ 的峰界限 All Info : 已更改 NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ 的峰界限 -已将“D_108_REP2”中的 NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ 所有峰的开始时间从 27.79 更改到 26.86 -已将“D_108_REP2”中的 NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ 所有峰的结束时间从 28.72 更改到 27.40 +已将“D_108_REP2”中的 NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ 所有峰的开始时间从 27.13 更改到 26.86 +已将“D_108_REP2”中的 NP_036870 > K.DFATVYVDAVK.D [36, 46] > 614.3164++ 所有峰的结束时间从 27.86 更改到 27.40 Undo Redo : 已删除目标 NP_036870 > K.FGLYSDQMR.E [182, 190] Summary : 已删除目标 NP_036870 > K.FGLYSDQMR.E [182, 190] @@ -338,10 +338,10 @@ Summary : 将 DFATVYVDAVK 的 MissingData 设定为 真 All Info : 将 DFATVYVDAVK 的 MissingData 设定为 真 -Undo Redo : 已将 89 值粘贴到文档网格 -Summary : 已将 89 值粘贴到文档网格 +Undo Redo : 已将 91 值粘贴到文档网格 +Summary : 已将 91 值粘贴到文档网格 All Info : -已将 89 值粘贴到文档网格 +已将 91 值粘贴到文档网格 文档网格 > 报告名称 是 "Truncated Precursors" 文档网格 > 列设置 > 列分类 : 包含 { 列 = "截尾计数", 方位 = "降序" } 将 DFATVYVDAVK 的 MissingData 设定为 真 @@ -349,6 +349,7 @@ All Info : 将 DFATVYVDAVK 的 MissingData 设定为 真 将 DFATVYVDAVK 的 MissingData 设定为 真 将 DFATVYVDAVK 的 MissingData 设定为 真 +将 DFATVYVDAVK 的 MissingData 设定为 真 将 DYVSQFESSTLGK 的 MissingData 设定为 真 将 DYVSQFESSTLGK 的 MissingData 设定为 真 将 DYVSQFESSTLGK 的 MissingData 设定为 真 @@ -358,6 +359,7 @@ All Info : 将 TGTNLMDFLSR 的 MissingData 设定为 真 将 TGTNLMDFLSR 的 MissingData 设定为 真 将 TGTNLMDFLSR 的 MissingData 设定为 真 +将 TGTNLMDFLSR 的 MissingData 设定为 真 将 ASGIIDTLFQDR 的 MissingData 设定为 真 将 ASGIIDTLFQDR 的 MissingData 设定为 真 将 SDFQVPC[+57.021464]QYSQQLK 的 MissingData 设定为 真 @@ -522,6 +524,8 @@ TRUE TRUE TRUE TRUE +TRUE +TRUE 报告名称 = "Truncated Precursors", 列设置 = diff --git a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/zh/TestPeakPickingTutorial.log b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/zh/TestPeakPickingTutorial.log index 52b3d641a18..2d1af43c000 100644 --- a/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/zh/TestPeakPickingTutorial.log +++ b/pwiz_tools/Skyline/TestTutorial/TutorialAuditLogs/zh/TestPeakPickingTutorial.log @@ -66,18 +66,18 @@ Summary : 已使用“test1”重新合并峰 All Info : 已使用“test1”重新合并峰 重新合并 > 峰得分模型 是 "test1" -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "强度", 权重 = "-0.109478368757285", 贡献百分比 = "-0.03" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "保留时间差异", 权重 = "-0.768944435803533", 贡献百分比 = "0.067" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "库强度点积", 权重 = "1.91473907688983", 贡献百分比 = "0.071" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "形状（权重）", 权重 = "0.964669685201429", 贡献百分比 = "0.064" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱（权重）", 权重 = "0.02650942606722", 贡献百分比 = "-0.023" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱计数", 权重 = "0.182160083950605", 贡献百分比 = "0.062" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "噪声信号", 权重 = "0.222905461284023", 贡献百分比 = "0.053" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考强度点积", 权重 = "0.552864109927935", 贡献百分比 = "0.023" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考形状（权重）", 权重 = "6.54328846588002", 贡献百分比 = "0.406" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考共洗脱（权重）", 权重 = "-0.0356701324564961", 贡献百分比 = "0.028" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考共洗脱计数", 权重 = "0.528459143213594", 贡献百分比 = "0.099" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "标准强度", 权重 = "0.65850313905602", 贡献百分比 = "0.181" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "强度", 权重 = "-0.117780784704465", 贡献百分比 = "-0.033" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "保留时间差异", 权重 = "-0.808657987543547", 贡献百分比 = "0.048" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "库强度点积", 权重 = "1.98045625169655", 贡献百分比 = "0.075" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "形状（权重）", 权重 = "0.957566675745166", 贡献百分比 = "0.064" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱（权重）", 权重 = "0.0262546721248633", 贡献百分比 = "-0.022" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱计数", 权重 = "0.19835359905492", 贡献百分比 = "0.063" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "噪声信号", 权重 = "0.240439149219498", 贡献百分比 = "0.058" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考强度点积", 权重 = "0.394262692010798", 贡献百分比 = "0.017" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考形状（权重）", 权重 = "6.67093073538981", 贡献百分比 = "0.415" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考共洗脱（权重）", 权重 = "-0.0364048216861083", 贡献百分比 = "0.027" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考共洗脱计数", 权重 = "0.522562212303566", 贡献百分比 = "0.107" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "标准强度", 权重 = "0.649409943962926", 贡献百分比 = "0.181" } 重新合并 > 峰得分模型 > 采用诱饵 是 真 重新合并 > 峰得分模型 > 采用次优峰值 是 假 重新合并 > 积分所有峰 是 真 @@ -88,62 +88,62 @@ Extra Info: 峰得分模型 = "test1": [ { 得分名称 = "强度", - 权重 = "-0.109478368757285", - 贡献百分比 = "-0.03" + 权重 = "-0.117780784704465", + 贡献百分比 = "-0.033" }, { 得分名称 = "保留时间差异", - 权重 = "-0.768944435803533", - 贡献百分比 = "0.067" + 权重 = "-0.808657987543547", + 贡献百分比 = "0.048" }, { 得分名称 = "库强度点积", - 权重 = "1.91473907688983", - 贡献百分比 = "0.071" + 权重 = "1.98045625169655", + 贡献百分比 = "0.075" }, { 得分名称 = "形状（权重）", - 权重 = "0.964669685201429", + 权重 = "0.957566675745166", 贡献百分比 = "0.064" }, { 得分名称 = "共洗脱（权重）", - 权重 = "0.02650942606722", - 贡献百分比 = "-0.023" + 权重 = "0.0262546721248633", + 贡献百分比 = "-0.022" }, { 得分名称 = "共洗脱计数", - 权重 = "0.182160083950605", - 贡献百分比 = "0.062" + 权重 = "0.19835359905492", + 贡献百分比 = "0.063" }, { 得分名称 = "噪声信号", - 权重 = "0.222905461284023", - 贡献百分比 = "0.053" + 权重 = "0.240439149219498", + 贡献百分比 = "0.058" }, { 得分名称 = "参考强度点积", - 权重 = "0.552864109927935", - 贡献百分比 = "0.023" + 权重 = "0.394262692010798", + 贡献百分比 = "0.017" }, { 得分名称 = "参考形状（权重）", - 权重 = "6.54328846588002", - 贡献百分比 = "0.406" + 权重 = "6.67093073538981", + 贡献百分比 = "0.415" }, { 得分名称 = "参考共洗脱（权重）", - 权重 = "-0.0356701324564961", - 贡献百分比 = "0.028" + 权重 = "-0.0364048216861083", + 贡献百分比 = "0.027" }, { 得分名称 = "参考共洗脱计数", - 权重 = "0.528459143213594", - 贡献百分比 = "0.099" + 权重 = "0.522562212303566", + 贡献百分比 = "0.107" }, { 得分名称 = "标准强度", - 权重 = "0.65850313905602", + 权重 = "0.649409943962926", 贡献百分比 = "0.181" } ], @@ -158,18 +158,18 @@ Summary : 已使用“test1”重新合并峰 All Info : 已使用“test1”重新合并峰 重新合并 > 峰得分模型 是 "test1" -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "强度", 权重 = "-0.109478368757285", 贡献百分比 = "-0.03" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "保留时间差异", 权重 = "-0.768944435803533", 贡献百分比 = "0.067" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "库强度点积", 权重 = "1.91473907688983", 贡献百分比 = "0.071" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "形状（权重）", 权重 = "0.964669685201429", 贡献百分比 = "0.064" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱（权重）", 权重 = "0.02650942606722", 贡献百分比 = "-0.023" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱计数", 权重 = "0.182160083950605", 贡献百分比 = "0.062" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "噪声信号", 权重 = "0.222905461284023", 贡献百分比 = "0.053" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考强度点积", 权重 = "0.552864109927935", 贡献百分比 = "0.023" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考形状（权重）", 权重 = "6.54328846588002", 贡献百分比 = "0.406" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考共洗脱（权重）", 权重 = "-0.0356701324564961", 贡献百分比 = "0.028" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考共洗脱计数", 权重 = "0.528459143213594", 贡献百分比 = "0.099" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "标准强度", 权重 = "0.65850313905602", 贡献百分比 = "0.181" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "强度", 权重 = "-0.117780784704465", 贡献百分比 = "-0.033" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "保留时间差异", 权重 = "-0.808657987543547", 贡献百分比 = "0.048" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "库强度点积", 权重 = "1.98045625169655", 贡献百分比 = "0.075" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "形状（权重）", 权重 = "0.957566675745166", 贡献百分比 = "0.064" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱（权重）", 权重 = "0.0262546721248633", 贡献百分比 = "-0.022" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱计数", 权重 = "0.19835359905492", 贡献百分比 = "0.063" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "噪声信号", 权重 = "0.240439149219498", 贡献百分比 = "0.058" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考强度点积", 权重 = "0.394262692010798", 贡献百分比 = "0.017" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考形状（权重）", 权重 = "6.67093073538981", 贡献百分比 = "0.415" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考共洗脱（权重）", 权重 = "-0.0364048216861083", 贡献百分比 = "0.027" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "参考共洗脱计数", 权重 = "0.522562212303566", 贡献百分比 = "0.107" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "标准强度", 权重 = "0.649409943962926", 贡献百分比 = "0.181" } 重新合并 > 峰得分模型 > 采用诱饵 是 真 重新合并 > 峰得分模型 > 采用次优峰值 是 假 重新合并 > 仅积分有显著性 q 值的峰 是 真 @@ -181,62 +181,62 @@ Extra Info: 峰得分模型 = "test1": [ { 得分名称 = "强度", - 权重 = "-0.109478368757285", - 贡献百分比 = "-0.03" + 权重 = "-0.117780784704465", + 贡献百分比 = "-0.033" }, { 得分名称 = "保留时间差异", - 权重 = "-0.768944435803533", - 贡献百分比 = "0.067" + 权重 = "-0.808657987543547", + 贡献百分比 = "0.048" }, { 得分名称 = "库强度点积", - 权重 = "1.91473907688983", - 贡献百分比 = "0.071" + 权重 = "1.98045625169655", + 贡献百分比 = "0.075" }, { 得分名称 = "形状（权重）", - 权重 = "0.964669685201429", + 权重 = "0.957566675745166", 贡献百分比 = "0.064" }, { 得分名称 = "共洗脱（权重）", - 权重 = "0.02650942606722", - 贡献百分比 = "-0.023" + 权重 = "0.0262546721248633", + 贡献百分比 = "-0.022" }, { 得分名称 = "共洗脱计数", - 权重 = "0.182160083950605", - 贡献百分比 = "0.062" + 权重 = "0.19835359905492", + 贡献百分比 = "0.063" }, { 得分名称 = "噪声信号", - 权重 = "0.222905461284023", - 贡献百分比 = "0.053" + 权重 = "0.240439149219498", + 贡献百分比 = "0.058" }, { 得分名称 = "参考强度点积", - 权重 = "0.552864109927935", - 贡献百分比 = "0.023" + 权重 = "0.394262692010798", + 贡献百分比 = "0.017" }, { 得分名称 = "参考形状（权重）", - 权重 = "6.54328846588002", - 贡献百分比 = "0.406" + 权重 = "6.67093073538981", + 贡献百分比 = "0.415" }, { 得分名称 = "参考共洗脱（权重）", - 权重 = "-0.0356701324564961", - 贡献百分比 = "0.028" + 权重 = "-0.0364048216861083", + 贡献百分比 = "0.027" }, { 得分名称 = "参考共洗脱计数", - 权重 = "0.528459143213594", - 贡献百分比 = "0.099" + 权重 = "0.522562212303566", + 贡献百分比 = "0.107" }, { 得分名称 = "标准强度", - 权重 = "0.65850313905602", + 权重 = "0.649409943962926", 贡献百分比 = "0.181" } ], @@ -252,11 +252,11 @@ Summary : 已使用“testDIA”重新合并峰 All Info : 已使用“testDIA”重新合并峰 重新合并 > 峰得分模型 是 "testDIA" -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "强度", 权重 = "0.290033468085838", 贡献百分比 = "0.067" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "形状（权重）", 权重 = "5.98411479906022", 贡献百分比 = "0.519" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱（权重）", 权重 = "-0.0623805713290799", 贡献百分比 = "0.103" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱计数", 权重 = "0.668140904775243", 贡献百分比 = "0.15" } -重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "噪声信号", 权重 = "0.796767785174247", 贡献百分比 = "0.161" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "强度", 权重 = "0.272541894208857", 贡献百分比 = "0.062" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "形状（权重）", 权重 = "6.02846861183062", 贡献百分比 = "0.513" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱（权重）", 权重 = "-0.0646032707611339", 贡献百分比 = "0.104" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "共洗脱计数", 权重 = "0.673690244719619", 贡献百分比 = "0.159" } +重新合并 > 峰得分模型 > 特征得分 : 包含 { 得分名称 = "噪声信号", 权重 = "0.812093607032469", 贡献百分比 = "0.161" } 重新合并 > 峰得分模型 > 采用诱饵 是 假 重新合并 > 峰得分模型 > 采用次优峰值 是 真 重新合并 > 积分所有峰 是 真 @@ -267,27 +267,27 @@ Extra Info: 峰得分模型 = "testDIA": [ { 得分名称 = "强度", - 权重 = "0.290033468085838", - 贡献百分比 = "0.067" + 权重 = "0.272541894208857", + 贡献百分比 = "0.062" }, { 得分名称 = "形状（权重）", - 权重 = "5.98411479906022", - 贡献百分比 = "0.519" + 权重 = "6.02846861183062", + 贡献百分比 = "0.513" }, { 得分名称 = "共洗脱（权重）", - 权重 = "-0.0623805713290799", - 贡献百分比 = "0.103" + 权重 = "-0.0646032707611339", + 贡献百分比 = "0.104" }, { 得分名称 = "共洗脱计数", - 权重 = "0.668140904775243", - 贡献百分比 = "0.15" + 权重 = "0.673690244719619", + 贡献百分比 = "0.159" }, { 得分名称 = "噪声信号", - 权重 = "0.796767785174247", + 权重 = "0.812093607032469", 贡献百分比 = "0.161" } ], diff --git a/pwiz_tools/Skyline/TestUtil/PeakMatcherTestUtil.cs b/pwiz_tools/Skyline/TestUtil/PeakMatcherTestUtil.cs index 46525a9f360..07867fb1f5c 100644 --- a/pwiz_tools/Skyline/TestUtil/PeakMatcherTestUtil.cs +++ b/pwiz_tools/Skyline/TestUtil/PeakMatcherTestUtil.cs @@ -1,7 +1,26 @@ -using System; +/* + * Original author: Kaipo Tamura , + * MacCoss Lab, Department of Genome Sciences, UW + * + * Copyright 2015 University of Washington - Seattle, WA + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +using System; using System.Collections.Generic; +using System.Globalization; using System.Linq; -using System.Text; using Microsoft.VisualStudio.TestTools.UnitTesting; using pwiz.Skyline; using pwiz.Skyline.Controls.SeqNode; @@ -70,8 +89,8 @@ public static void VerifyPeaks(IReadOnlyDictionary expected) var skylineWindow = Program.MainWindow; bool fail = false; - var expectedBuilder = new StringBuilder(); - var observedBuilder = new StringBuilder(); + var expectedList = new List(); + var observedList = new List(); var selectedTreeNode = skylineWindow.SelectedNode as PeptideTreeNode; TransitionGroupDocNode nodeTranGroup = selectedTreeNode != null @@ -92,16 +111,21 @@ public static void VerifyPeaks(IReadOnlyDictionary expected) var chromName = chromSet.Name; Assert.IsTrue(expected.ContainsKey(chromName)); var expectedRt = expected[chromName]; - expectedBuilder.AppendLine(string.Format("{0}", expectedRt)); - observedBuilder.AppendLine(string.Format("{0}", rt.Value)); + expectedList.Add(expectedRt); + observedList.Add(rt.Value); if (Math.Abs(expectedRt - rt.Value) > 0.01) fail = true; } - Assert.IsFalse(fail, TextUtil.LineSeparate( - string.Format("{0}", nodeTranGroup), - "Expected RTs:", expectedBuilder.ToString(), - "but found RTs:", observedBuilder.ToString()) - ); + + if (fail) + { + var message = TextUtil.LineSeparate(string.Format("{0}", nodeTranGroup), + "Expected RTs:", + string.Join(",", expectedList.Select(v => v.ToString(CultureInfo.InvariantCulture))), + "but found RTs:", + string.Join(",", observedList.Select(v => v.ToString("0.#####", CultureInfo.InvariantCulture)))); + Assert.Fail(message); + } } } } diff --git a/pwiz_tools/Skyline/TestUtil/Schemas/Skyline_24.11.xsd b/pwiz_tools/Skyline/TestUtil/Schemas/Skyline_24.11.xsd index 417f96fbce7..8914b96c2f5 100644 --- a/pwiz_tools/Skyline/TestUtil/Schemas/Skyline_24.11.xsd +++ b/pwiz_tools/Skyline/TestUtil/Schemas/Skyline_24.11.xsd @@ -568,6 +568,7 @@ + @@ -575,7 +576,6 @@ - diff --git a/pwiz_tools/Skyline/TestUtil/Schemas/Skyline_Current.xsd b/pwiz_tools/Skyline/TestUtil/Schemas/Skyline_Current.xsd index 417f96fbce7..8914b96c2f5 100644 --- a/pwiz_tools/Skyline/TestUtil/Schemas/Skyline_Current.xsd +++ b/pwiz_tools/Skyline/TestUtil/Schemas/Skyline_Current.xsd @@ -568,6 +568,7 @@ + @@ -575,7 +576,6 @@ -