Merge pull request #69 from CenterForMedicalGeneticsGhent/v1.2.1

V1.2.1

README.md

@@ -171,9 +171,9 @@ technique (the test case vs the reference cases).
 This file contains aberrant segments, defined by the [`--beta`](#stage-3-predict-copy-number-alterations) or 
 [`--zscore`](#stage-3-predict-copy-number-alterations) parameters.  
 
-### ID_chr_statistics.bed
+### ID_statistics.bed
 
-This file contains some interesting statistics for each chromosome. The definition of the Z-scores matches the one from 
+This file contains some interesting statistics (per chromosome and overall). The definition of the Z-scores matches the one from 
 the 'ID_segments.bed'. Particularly interesting for NIPT.  
 
 # Dependencies

setup.py

@@ -1,7 +1,7 @@
 #! /usr/bin/env python
 from setuptools import setup, find_packages
 
-version = '1.2.0'
+version = '1.2.1'
 dl_version = 'master' if 'dev' in version else '{}'.format(version)
 
 setup(

wisecondorX/main.py

@@ -152,37 +152,36 @@ def tool_test(args):
     sample = scale_sample(sample, int(
         sample_file['binsize'].item()), int(ref_file['binsize']))
 
+    gender = predict_gender(sample, ref_file['trained_cutoff'])
     if not ref_file['is_nipt']:
-        actual_gender = predict_gender(sample, ref_file['trained_cutoff'])
         if args.gender:
-            actual_gender = args.gender
-        sample = gender_correct(sample, actual_gender)
+            gender = args.gender
+        sample = gender_correct(sample, gender)
+        ref_gender = gender
     else:
-        actual_gender = 'F'
-
-    if args.gender:
-        actual_gender = args.gender
-
-    ref_gender = actual_gender
+        if args.gender:
+            gender = args.gender
+        ref_gender = 'F'
 
     logging.info('Normalizing autosomes ...')
 
     results_r, results_z, results_w, ref_sizes, m_lr, m_z = normalize(
         args, sample, ref_file, 'A')
 
-    if not ref_file['has_male'] and actual_gender == 'M':
-        logging.warning('This sample is male, whilst the reference is created with fewer than 5 males. '
-                        'The female gonosomal reference will be used for X predictions. Note that these might '
-                        'not be accurate. If the latter is desired, create a new reference and include more '
-                        'male samples.')
-        ref_gender = 'F'
-
-    elif not ref_file['has_female'] and actual_gender == 'F':
-        logging.warning('This sample is female, whilst the reference is created with fewer than 5 females. '
-                        'The male gonosomal reference will be used for XY predictions. Note that these might '
-                        'not be accurate. If the latter is desired, create a new reference and include more '
-                        'female samples.')
-        ref_gender = 'M'
+    if not ref_file['is_nipt']:
+        if not ref_file['has_male'] and gender == 'M':
+            logging.warning('This sample is male, whilst the reference is created with fewer than 5 males. '
+                            'The female gonosomal reference will be used for X predictions. Note that these might '
+                            'not be accurate. If the latter is desired, create a new reference and include more '
+                            'male samples.')
+            ref_gender = 'F'
+
+        elif not ref_file['has_female'] and gender == 'F':
+            logging.warning('This sample is female, whilst the reference is created with fewer than 5 females. '
+                            'The male gonosomal reference will be used for XY predictions. Note that these might '
+                            'not be accurate. If the latter is desired, create a new reference and include more '
+                            'female samples.')
+            ref_gender = 'M'
 
     logging.info('Normalizing gonosomes ...')
 
@@ -198,7 +197,7 @@ def tool_test(args):
                  'binsize': int(ref_file['binsize']),
                  'n_reads': n_reads,
                  'ref_gender': ref_gender,
-                 'actual_gender': actual_gender,
+                 'gender': gender,
                  'mask': ref_file['mask.{}'.format(ref_gender)],
                  'bins_per_chr': ref_file['bins_per_chr.{}'.format(ref_gender)],
                  'masked_bins_per_chr': ref_file['masked_bins_per_chr.{}'.format(ref_gender)],

wisecondorX/newref_tools.py

@@ -33,7 +33,7 @@ def train_gender_model(args, samples):
     if args.plotyfrac is not None:
         import matplotlib.pyplot as plt
         fig, ax = plt.subplots(figsize=(16, 6))
-        ax.hist(y_fractions, bins=100, normed=True)
+        ax.hist(y_fractions, bins=100, density=True)
         ax.plot(gmm_x, gmm_y, 'r-', label='Gaussian mixture fit')
         ax.set_xlim([0, 0.02])
         ax.legend(loc='best')

wisecondorX/predict_output.py

@@ -88,9 +88,9 @@ def _generate_segments_and_aberrations_bed(rem_input, results):
                int(segment[2] * rem_input['binsize']),
                segment[4], segment[3]]
         segments_file.write('{}\n'.format('\t'.join([str(x) for x in row])))
-        ploidy = 2
 
-        if (chr_name == 'X' or chr_name == 'Y') and rem_input['actual_gender'] == 'M':
+        ploidy = 2
+        if (chr_name == 'X' or chr_name == 'Y') and rem_input['ref_gender'] == 'M':
             ploidy = 1
         if rem_input['args'].beta is not None:
             if float(segment[4]) > __get_aberration_cutoff(rem_input['args'].beta, ploidy)[1]:
@@ -146,7 +146,7 @@ def _generate_chr_statistics_file(rem_input, results):
         stats_file.write('\t'.join([str(x) for x in row]) + '\n')
 
     stats_file.write('Gender based on --yfrac (or manually overridden by --gender): {}\n'
-                     .format(str(rem_input['actual_gender'])))
+                     .format(str(rem_input['gender'])))
 
     stats_file.write('Number of reads: {}\n'
                      .format(str(rem_input['n_reads'])))