Reorganized files

Author: Alex Smith

TrailMap_MergeCounts.py TrailMap/TrailMap_MergeCounts.pyTrailMap_MergeCounts.py renamed to TrailMap/TrailMap_MergeCounts.py

@@ -15,25 +15,33 @@
 
 
 # Base Directory, usually where your experiment is saved:
-baseDirectory = '/d2/studies/ClearMap/IA_iDISCO/analysisFiles'
-
+baseDirectory = '/d2/studies/ClearMap/ROC_iDISCO/batchDirectory'
+sampleName='ROC_iDISCO'
 
 
 # Voxel-based statistics:
 #########################
 
 #Load the data (heat maps generated previously )
-group1 = [os.path.join(baseDirectory, 'cells_heatmap_vox15_IA1_LB.tif'),
-          os.path.join(baseDirectory, 'cells_heatmap_vox15_IA2_NP.tif'),
-          os.path.join(baseDirectory, 'cells_heatmap_vox15_IA2_LT.tif'),
-          os.path.join(baseDirectory, 'cells_heatmap_vox15_IA2_LB.tif')
+group1 = [os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC9.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC10.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC11.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC12.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC13.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC14.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC15.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC16.tif')
           ]
 
-group2 = [os.path.join(baseDirectory, 'cells_heatmap_vox15_IA1_RT.tif'),
-          os.path.join(baseDirectory, 'cells_heatmap_vox15_IA1_RB.tif'),
-          os.path.join(baseDirectory, 'cells_heatmap_vox15_IA1_LT.tif'),
-          os.path.join(baseDirectory, 'cells_heatmap_vox15_IA2_RT.tif'),
-          os.path.join(baseDirectory, 'cells_heatmap_vox15_IA2_RB.tif')
+group2 = [os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC1.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC2.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC4.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC6.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC7.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC8.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC17.tif'),
+          os.path.join(baseDirectory, 'cells_heatmap_vox15_ROC18.tif'),
+          
           ]
 
 
@@ -50,14 +58,11 @@
 g2a = numpy.mean(g2,axis = 0);
 g2s = numpy.std(g2,axis = 0);
 
-io.writeData(os.path.join(baseDirectory, 'Yoked_full_mean_horizontal_vox15.mhd'), g1a)# rsp.sagittalToCoronalData(g1a));
-io.writeData(os.path.join(baseDirectory, 'Yoked_full_std_horizontal_vox15.mhd'), g1s)# rsp.sagittalToCoronalData(g1s));
-
-io.writeData(os.path.join(baseDirectory, 'IA30_full_mean_horizontal_vox15.mhd'), g2a)# rsp.sagittalToCoronalData(g2a));
-io.writeData(os.path.join(baseDirectory, 'IA30_full_std_horizontal_vox15.mhd'), g2s)# rsp.sagittalToCoronalData(g2s));
-
-
+io.writeData(os.path.join(baseDirectory, 'Group1_full_mean_horizontal.mhd'), g1a)# rsp.sagittalToCoronalData(g1a));
+io.writeData(os.path.join(baseDirectory, 'Group1_full_std_horizontal.mhd'), g1s)# rsp.sagittalToCoronalData(g1s));
 
+io.writeData(os.path.join(baseDirectory, 'Group2_full_mean_horizontal.mhd'), g2a)# rsp.sagittalToCoronalData(g2a));
+io.writeData(os.path.join(baseDirectory, 'Group2_full_std_horizontal.mhd'), g2s)# rsp.sagittalToCoronalData(g2s));
 
 
 #Generate the p-values map
@@ -66,13 +71,12 @@
 pvals, psign = stat.tTestVoxelization(g1.astype('float'), g2.astype('float'), signed = True, pcutoff = 0.05);
 
 #color the p-values according to their sign (defined by the sign of the difference of the means between the 2 groups)
-pvalsc = stat.colorPValues(pvals, psign, positive = [0,1], negative = [1,0]);
-io.writeData(os.path.join(baseDirectory, 'pvalues_full_vox15.tif'), pvalsc.astype('float32'))#, rsp.sagittalToCoronalData(pvalsc.astype('float32')));
+pvalsc = stat.colorPValues(pvals, psign, positive = [1,0], negative = [0,1]);
+io.writeData(os.path.join(baseDirectory, sampleName + '_pvalues.tif'), pvalsc.astype('float32'))#, rsp.sagittalToCoronalData(pvalsc.astype('float32')));
 
 
 
 #############################################################################
-
 # Regions-based statistics:
 ###########################
 
@@ -100,11 +104,7 @@
 PathReg        = '/d1/studies/ClearMap/ClearMap_resources/Parameter_files/';
 AnnotationFile = '/d2/studies/ClearMap/IA_iDISCO/Annotations_Horizontal_8-302_Full.tif';
 
-
-
-#ids, pc1, pc1i = stat.countPointsGroupInRegions(group1, intensityGroup = group1i, withIds = True, labeledImage = lbl.DefaultLabeledImageFile, withCounts = True);
-#pc2, pc2i = stat.countPointsGroupInRegions(group2, intensityGroup = group2i, withIds = False, labeledImage = lbl.DefaultLabeledImageFile, withCounts = True);
-
+#Calculate stats for regions. Set collapse=True to use collapsed regions (e.g. collapse cortical layers into single region)
 ids, pc1, pc1i = stat.countPointsGroupInRegions(group1, intensityGroup = group1i, returnIds = True, labeledImage = AnnotationFile, returnCounts = True, collapse=None);
 pc2, pc2i = stat.countPointsGroupInRegions(group2, intensityGroup = group2i, returnIds = False, labeledImage = AnnotationFile, returnCounts = True, collapse=None);
 
@@ -156,12 +156,12 @@
 table["name"] = lbl.labelToName(ids0);
 
 
-#sort by qvalue
+#sort by qvalue & write intensity table
 ii = numpy.argsort(pvalsi0);
 tableSorted = table.copy();
 tableSorted = tableSorted[ii];
 
-with open(os.path.join(baseDirectory, 'counts-intensity_table_6.csv'),'w') as f:
+with open(os.path.join(baseDirectory, 'counts-intensity_table.csv'),'w') as f:
     f.write(', '.join([str(item) for item in table.dtype.names]));
     f.write('\n');
     for sublist in tableSorted:
@@ -198,156 +198,16 @@
 table["name"] = lbl.labelToName(ids0);
 
 
-#sort by qvalue
+#sort by qvalue & write counts table
 ii = numpy.argsort(pvals0);
 tableSorted = table.copy();
 tableSorted = tableSorted[ii];
 
-with open(os.path.join(baseDirectory, 'counts_table_6.csv'),'w') as f:
+with open(os.path.join(baseDirectory, 'counts_table.csv'),'w') as f:
     f.write(', '.join([str(item) for item in table.dtype.names]));
     f.write('\n');
     for sublist in tableSorted:
         f.write(', '.join([str(item) for item in sublist]));
         f.write('\n');
     f.close();
 
-
-#############################################################################
-
-
-baseDirectory = '/home/mtllab/Documents/bicuculline'
-
-
-group2 = [#'/home/mtllab/Documents/bicuculline/2/cells_heatmap.tif',
-       #   '/home/mtllab/Documents/bicuculline/3/cells_transformed_to_Atlas.npy',
-          '/home/mtllab/Documents/bicuculline/7/cells_transformed_to_Atlas.npy',
-          '/home/mtllab/Documents/bicuculline/6/cells_transformed_to_Atlas.npy',
-       #   '/home/mtllab/Documents/bicuculline/8/cells_transformed_to_Atlas.npy',
-        #  '/home/mtllab/Documents/bicuculline/9/cells_transformed_to_Atlas.npy',
-       #   '/home/mtllab/Documents/bicuculline/13/cells_transformed_to_Atlas.npy']
-          '/home/mtllab/Documents/bicuculline/14/cells_transformed_to_Atlas.npy']
-          
-group1 = ['/home/mtllab/Documents/bicuculline/10/cells_transformed_to_Atlas.npy',
-          '/home/mtllab/Documents/bicuculline/11/cells_transformed_to_Atlas.npy',
-          '/home/mtllab/Documents/bicuculline/12/cells_transformed_to_Atlas.npy']
-
-
-
-group1i = [fn.replace('cells_transformed_to_Atlas', 'intensities') for fn in group1];
-group2i = [fn.replace('cells_transformed_to_Atlas', 'intensities') for fn in group2];
-
-
-
-
-AnnotationFile = os.path.join(PathReg, 'annotation_25_right_fullWD.tif');
-
-
-
-#ids, pc1, pc1i = stat.countPointsGroupInRegions(group1, intensityGroup = group1i, withIds = True, labeledImage = lbl.DefaultLabeledImageFile, withCounts = True);
-#pc2, pc2i = stat.countPointsGroupInRegions(group2, intensityGroup = group2i, withIds = False, labeledImage = lbl.DefaultLabeledImageFile, withCounts = True);
-
-ids, pc1, pc1i = stat.countPointsGroupInRegions(group1, intensityGroup = group1i, returnIds = True, labeledImage = AnnotationFile, returnCounts = True, collapse=True);
-pc2, pc2i = stat.countPointsGroupInRegions(group2, intensityGroup = group2i, returnIds = False, labeledImage = AnnotationFile, returnCounts = True, collapse=True);
-
-
-pvals, psign = stat.tTestPointsInRegions(pc1, pc2, pcutoff = None, signed = True);
-pvalsi, psigni = stat.tTestPointsInRegions(pc1i, pc2i, pcutoff = None, signed = True, equal_var = True);
-
-import ClearMap.Analysis.Tools.MultipleComparisonCorrection as FDR
-
-
-iid = pvalsi < 1;
-
-ids0 = ids[iid];
-pc1i0 = pc1i[iid];
-pc2i0 = pc2i[iid];
-pc10 = pc1[iid];
-pc20 = pc2[iid];
-psigni0 = psigni[iid];
-pvalsi0 = pvalsi[iid];
-qvalsi0 = FDR.estimateQValues(pvalsi0);
-psign0 = psign[iid];
-pvals0 = pvals[iid];
-qvals0 = FDR.estimateQValues(pvals0);
-
-
-#make table
-
-dtypes = [('id','int64'),('mean1','f8'),('std1','f8'),('mean2','f8'),('std2','f8'),('pvalue', 'f8'),('qvalue', 'f8'),('psign', 'int64')];
-for i in range(len(group1)):
-    dtypes.append(('count1_%d' % i, 'f8'));
-for i in range(len(group2)):
-    dtypes.append(('count2_%d' % i, 'f8'));   
-dtypes.append(('name', 'a256'));
-
-table = numpy.zeros(ids0.shape, dtype = dtypes)
-table["id"] = ids0;
-table["mean1"] = pc1i0.mean(axis = 1)/1000000;
-table["std1"] = pc1i0.std(axis = 1)/1000000;
-table["mean2"] = pc2i0.mean(axis = 1)/1000000;
-table["std2"] = pc2i0.std(axis = 1)/1000000;
-table["pvalue"] = pvalsi0;
-table["qvalue"] = qvalsi0;
-
-table["psign"] = psigni0;
-for i in range(len(group1)):
-    table["count1_%d" % i] = pc10[:,i];
-for i in range(len(group2)):
-    table["count2_%d" % i] = pc20[:,i];
-table["name"] = lbl.labelToName(ids0);
-
-
-#sort by qvalue
-ii = numpy.argsort(pvalsi0);
-tableSorted = table.copy();
-tableSorted = tableSorted[ii];
-
-with open(os.path.join(baseDirectory, 'counts-intensity_table_slow.csv'),'w') as f:
-    f.write(', '.join([str(item) for item in table.dtype.names]));
-    f.write('\n');
-    for sublist in tableSorted:
-        f.write(', '.join([str(item) for item in sublist]));
-        f.write('\n');
-    f.close();
-
-#############################
-
-
-#make table
-
-dtypes = [('id','int64'),('mean1','f8'),('std1','f8'),('mean2','f8'),('std2','f8'),('pvalue', 'f8'),('qvalue', 'f8'),('psign', 'int64')];
-for i in range(len(group1)):
-    dtypes.append(('count1_%d' % i, 'f8'));
-for i in range(len(group2)):
-    dtypes.append(('count2_%d' % i, 'f8'));   
-dtypes.append(('name', 'a256'));
-
-table = numpy.zeros(ids0.shape, dtype = dtypes)
-table["id"] = ids0;
-table["mean1"] = pc10.mean(axis = 1);
-table["std1"] = pc10.std(axis = 1);
-table["mean2"] = pc20.mean(axis = 1);
-table["std2"] = pc20.std(axis = 1);
-table["pvalue"] = pvals0;
-table["qvalue"] = qvals0;
-
-table["psign"] = psigni0;
-for i in range(len(group1)):
-    table["count1_%d" % i] = pc10[:,i];
-for i in range(len(group2)):
-    table["count2_%d" % i] = pc20[:,i];
-table["name"] = lbl.labelToName(ids0);
-
-
-#sort by qvalue
-ii = numpy.argsort(pvals0);
-tableSorted = table.copy();
-tableSorted = tableSorted[ii];
-
-with open(os.path.join(baseDirectory, 'counts_table_slow.csv','w') as f:
-    f.write(', '.join([str(item) for item in table.dtype.names]));
-    f.write('\n');
-    for sublist in tableSorted:
-        f.write(', '.join([str(item) for item in sublist]));
-        f.write('\n');
-    f.close();

TrailMap_TransformPointsClearMap.py …lMap/TrailMap_TransformPointsClearMap.pyTrailMap_TransformPointsClearMap.py renamed to TrailMap/TrailMap_TransformPointsClearMap.py TrailMap_TransformPointsClearMap.py renamed to TrailMap/TrailMap_TransformPointsClearMap.py

@@ -18,17 +18,18 @@
 import ClearMap.Analysis.Label as lbl
 import numpy as np
 
-sampleName = 'ROC_13'
-execfile('/d2/studies/ClearMap/IA_iDISCO/' + sampleName + '/parameter_file_' + sampleName + '.py')
-baseDirectory = '/d2/studies/ClearMap/IA_iDISCO/' + sampleName
-region = 'Caudoputamen'
+sampleName = 'LowNIC'
+#execfile('/d2/studies/ClearMap/IA_iDISCO/' + sampleName + '/parameter_file_' + sampleName + '.py')
+execfile('/d2/studies/ClearMap/Alex_Acute_iDISCO/3RB_HighNIC/parameter_file_template_3RB.py')
+baseDirectory = '/d2/studies/ClearMap/Alex_Acute_iDISCO/NIC_HeatMaps/FiguresForPaper/'
+region = 'MSC'
 
 
 points = io.readPoints(TransformedCellsFile)
 data = plt.overlayPoints(AnnotationFile, points.astype(int), pointColor = None)
-io.writeData(os.path.join(BaseDirectory, sampleName + '_Annotations_Points_Overlay.tif'), data)
+io.writeData(os.path.join(BaseDirectory, sampleName + '_Annotations_Points_Overlay_newAtlas2.tif'), data)
 data = data[:,:,:,1:]
-io.writeData(os.path.join(BaseDirectory, sampleName + '_Points_Transformed.tif'), data)
+io.writeData(os.path.join(BaseDirectory, sampleName + '_Points_Transformed_newAtlas2.tif'), data)
 
 label = io.readData(AnnotationFile)
 label = label.astype('int32')
@@ -47,14 +48,7 @@
     if not (lbl.labelAtLevel(l, 6) == 672):
        outside = np.logical_or(outside, label == l);
 
-#DP = 814 (level 6)
-#MHb = 483 (level 7)
-#Caudoputamen = 672 (level 6)
-#Accumbens = 56
-#CA3 = 463 (Level 8)
-#Prelimbic = 972 (Level 6)
 
-#heatmap = io.readData(AnnotationFile)
 heatmap = io.readData(os.path.join(baseDirectory, sampleName + '_Points_Transformed.tif'))
 heatmap[outside] = 0;