fixed batch and run order correction and tests

phenomecentre · Aug 7, 2024 · 1ec5dc4 · 1ec5dc4
1 parent 47e737a
commit 1ec5dc4
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Showing 2 changed files with 25 additions and 14 deletions.
diff --git a/Tests/test_reports.py b/Tests/test_reports.py
@@ -295,24 +295,33 @@ def test_reports_ms_correlationtodilution(self):
 
     def test_reports_ms_batchcorrectiontest(self):
 
+#        data = nPYc.MSDataset(
+#            os.path.join('..', '..', 'npc-standard-project', 'Derived_Data', 'UnitTest1_PCSOP.069_QI.csv'),
+#            fileType='QI')
         data = nPYc.MSDataset(
-            os.path.join('..', '..', 'npc-standard-project', 'Derived_Data', 'UnitTest1_PCSOP.069_QI.csv'),
-            fileType='QI')
+            os.path.join('..', '..', 'npc-standard-project', 'Derived_Data', 'UnitTest1_PCSOP.069_xcms_peakTable.csv'),
+            fileType='XCMS',
+            noFeatureParams=8
+        )
+
+        #data.excludeSamples(['UnitTest1_LPOS_ToF02_ERROR'], on='Sample File Name') ### CAROLINE 070824
+        #data.applyMasks()
         data.addSampleInfo(descriptionFormat='Filenames')
         data.addSampleInfo(descriptionFormat='Raw Data',
                            filePath=os.path.join('..', '..', 'npc-standard-project', 'Raw_Data', 'ms',
                                                  'parameters_data'))
-        # data.sampleMetadata['Correction Batch'] = data.sampleMetadata['Batch']
+        data.addSampleInfo(descriptionFormat='Infer Batches')
+#        data.sampleMetadata['Correction Batch'] = data.sampleMetadata['Batch']
 
         with tempfile.TemporaryDirectory() as tmpdirname:
             nPYc.reports.generateReport(data, 'batch correction assessment', destinationPath=tmpdirname)
 
-            expectedPath = os.path.join(tmpdirname, 'UnitTest1_PCSOP.069_QI_report_batchCorrectionAssessment.html')
+            expectedPath = os.path.join(tmpdirname, 'UnitTest1_PCSOP.069_xcms_peakTable_report_batchCorrectionAssessment.html')
             self.assertTrue(os.path.exists(expectedPath))
 
-            testFiles = ['UnitTest1_PCSOP.069_QI_batchPlotFeature_3.17_145.0686m-z.png',
-                         'UnitTest1_PCSOP.069_QI_batchPlotFeature_3.17_262.0378m-z.png',
-                         'UnitTest1_PCSOP.069_QI_TICdetectorBatches.png']
+            testFiles = ['UnitTest1_PCSOP.069_xcms_peakTable_batchPlotFeature_3.17_145.0686m-z.png',
+                         'UnitTest1_PCSOP.069_xcms_peakTable_batchPlotFeature_3.17_262.0378m-z.png',
+                         'UnitTest1_PCSOP.069_xcms_peakTable_TICdetectorBatches.png']
 
             for testFile in testFiles:
                 expectedPath = os.path.join(tmpdirname, 'graphics', 'report_batchCorrectionAssessment', testFile)

diff --git a/nPYc/batchAndROCorrection/_batchAndROCorrection.py b/nPYc/batchAndROCorrection/_batchAndROCorrection.py
@@ -100,25 +100,27 @@ def correctMSdataset(data,
 
 	# For each `Correction Batch`, check all samples for correction have at least one `correctionSampleType`
 	batches = numpy.unique(data.sampleMetadata['Correction Batch'])
+	batches = batches[~numpy.isnan(batches)]
 	for batch in batches:
-		if numpy.any((data.sampleMetadata.loc[data.sampleMetadata['Correction Batch'] == batch, 'SampleType'] == correctionSampleType) & (data.sampleMetadata.loc[data.sampleMetadata['Correction Batch'] == batch, 'AssayRole'] == AssayRole.PrecisionReference)):
+		if not numpy.any((data.sampleMetadata.loc[data.sampleMetadata['Correction Batch'] == batch, 'SampleType'] == correctionSampleType) & (data.sampleMetadata.loc[data.sampleMetadata['Correction Batch'] == batch, 'AssayRole'] == AssayRole.PrecisionReference)):
 			raise npycToolboxError("Unable to run batch and run order correction without at least one " + str(correctionSampleType) + " sample in each `Correction Batch`, please check and update dataset accordingly.")
 
 	with warnings.catch_warnings():
 		warnings.simplefilter('ignore', category=RuntimeWarning)
 
-		correctedP = _batchCorrectionHead(data.intensityData,
-									 data.sampleMetadata['Run Order'].values,
-									 (data.sampleMetadata['SampleType'].values == correctionSampleType) & (data.sampleMetadata['AssayRole'].values == AssayRole.PrecisionReference),
-									 data.sampleMetadata.loc[samplesForCorrection==True, 'Correction Batch'],
+		correctedP = _batchCorrectionHead(data.intensityData[samplesForCorrection==True,:],
+									 data.sampleMetadata.loc[samplesForCorrection==True, 'Run Order'].values,
+									 (data.sampleMetadata.loc[samplesForCorrection==True, 'SampleType'].values == correctionSampleType) & (data.sampleMetadata.loc[samplesForCorrection==True, 'AssayRole'].values == AssayRole.PrecisionReference),
+									 data.sampleMetadata.loc[samplesForCorrection==True, 'Correction Batch'].values,
 									 window=window,
 									 method=method,
 									 align=align,
 									 parallelise=parallelise)
 
 	correctedData = copy.deepcopy(data)
-	correctedData.intensityData = correctedP[0]
-	correctedData.fit = correctedP[1]
+	correctedData.intensityData[samplesForCorrection==True,:] = correctedP[0]
+	correctedData.fit = numpy.full(correctedData.intensityData.shape, numpy.nan)
+	correctedData.fit[samplesForCorrection==True,:] = correctedP[1]
 	correctedData.Attributes['Log'].append([datetime.now(),'Batch and run order correction applied'])
 
 	return correctedData