removed deprecated code commands and fixed indices in augment.py

Author: panushri25

chrombpnet/parsers.py

@@ -36,11 +36,11 @@ def read_parser():
         # downstream tool parsers
         preds_parser = subparsers.add_parser("pred_bw", help="Get model prediction bigwigs (Metrics calculated if observed bigwig provided)")
         contribs_parser = subparsers.add_parser("contribs_bw", help="Get contribution score bigwigs")
-        motifs_parser = subparsers.add_parser("modisco_motifs", help="(Will soon be deprecated: use modisco motifs from tfmodisco lite) Summarize motifs from contribution scores with TFModisco")
+        #motifs_parser = subparsers.add_parser("modisco_motifs", help="(Will soon be deprecated: use modisco motifs from tfmodisco lite) Summarize motifs from contribution scores with TFModisco")
         #custom_preds_parser = subparsers.add_parser("pred_custom", help="Make model predictions on custom sequences and output to .h5 file")
         #custom_contribs_parser = subparsers.add_parser("contribs_custom", help="Get contribution on custom sequences and output to .h5 file")
         footprints_parser = subparsers.add_parser("footprints", help="Get marginal footprinting for given model and given motifs")
-        variants_parser = subparsers.add_parser("snp_score", help="Score SNPs with model")
+        #variants_parser = subparsers.add_parser("snp_score", help="Score SNPs with model")
 
         def general_training_args(required_train, optional_train):
 
@@ -262,31 +262,31 @@ def general_training_args(required_train, optional_train):
 
         # Do variant scoring
 
-        variants_parser._action_groups.pop()
-        required_ves =  variants_parser.add_argument_group('required arguments')
-        optional_ves =  variants_parser.add_argument_group('optional arguments')
-        required_ves.add_argument("-snps", "--snp-data", type=str, required=True, help="Path to a tsv output with the following information in columns - chr, position to insert allele (0-based), ref allele, alt allele")
-        required_ves.add_argument("-m", "--model-h5", type=str, required=True, help="Path model .h5 file")
-        required_ves.add_argument("-g", "--genome", type=str, required=True, help="Genome fasta")
-        required_ves.add_argument("-op", "--output-prefix", type=str, required=True, help="Output prefix for bigwig files")
+        #variants_parser._action_groups.pop()
+        #required_ves =  variants_parser.add_argument_group('required arguments')
+        #optional_ves =  variants_parser.add_argument_group('optional arguments')
+        #required_ves.add_argument("-snps", "--snp-data", type=str, required=True, help="Path to a tsv output with the following information in columns - chr, position to insert allele (0-based), ref allele, alt allele")
+        #required_ves.add_argument("-m", "--model-h5", type=str, required=True, help="Path model .h5 file")
+        #required_ves.add_argument("-g", "--genome", type=str, required=True, help="Genome fasta")
+        #required_ves.add_argument("-op", "--output-prefix", type=str, required=True, help="Output prefix for bigwig files")
 
-        optional_ves.add_argument("-bs", "--batch-size", type=int, default=64, help="batch size to use for prediction")
-        optional_ves.add_argument("-dm","--debug-mode-on", type=int, default=0, help="Use this mode to print the flanks of first five SNP insert locations")
+        #optional_ves.add_argument("-bs", "--batch-size", type=int, default=64, help="batch size to use for prediction")
+        #optional_ves.add_argument("-dm","--debug-mode-on", type=int, default=0, help="Use this mode to print the flanks of first five SNP insert locations")
 
 
         # Run TF-Modisco
 
-        motifs_parser._action_groups.pop()
-        required_tfm =  motifs_parser.add_argument_group('required arguments')
-        optional_tfm =  motifs_parser.add_argument_group('optional arguments')
+        #motifs_parser._action_groups.pop()
+        #required_tfm =  motifs_parser.add_argument_group('required arguments')
+        #optional_tfm =  motifs_parser.add_argument_group('optional arguments')
 
-        required_tfm.add_argument("-i", "--h5py", type=str, required=True, help="A legacy h5py file containing the one-hot encoded sequences and shap scores.")
-        required_tfm.add_argument("-n", "--max-seqlets", type=int, required=True, help="The maximum number of seqlets per metacluster.")
-        required_tfm.add_argument("-op", "--output-prefix", type=str, required=True, help="The path to the output file.")
+        #required_tfm.add_argument("-i", "--h5py", type=str, required=True, help="A legacy h5py file containing the one-hot encoded sequences and shap scores.")
+        #required_tfm.add_argument("-n", "--max-seqlets", type=int, required=True, help="The maximum number of seqlets per metacluster.")
+        #required_tfm.add_argument("-op", "--output-prefix", type=str, required=True, help="The path to the output file.")
 
-        optional_tfm.add_argument("-l", "--n-leiden", type=int, default=2, help="The number of Leiden clusterings to perform with different random seeds.")
-        optional_tfm.add_argument("-w", "--window", type=int, default=500, help="The window surrounding the peak center that will be considered for motif discovery.")
-        optional_tfm.add_argument("-v", "--verbose", action="store_true", default=False, help="Controls the amount of output from the code.")
+        #optional_tfm.add_argument("-l", "--n-leiden", type=int, default=2, help="The number of Leiden clusterings to perform with different random seeds.")
+        #optional_tfm.add_argument("-w", "--window", type=int, default=500, help="The window surrounding the peak center that will be considered for motif discovery.")
+        #optional_tfm.add_argument("-v", "--verbose", action="store_true", default=False, help="Controls the amount of output from the code.")
 
 
         # Pull the arguments

chrombpnet/training/utils/augment.py

@@ -31,7 +31,8 @@ def random_crop(seqs, labels, seq_crop_width, label_crop_width, coords):
     starts = np.random.choice(range(max_start+1), size=seqs.shape[0], replace=True)
 
     new_coords = coords.copy()
-    new_coords[:,1] = new_coords[:,1].astype(int) - (seqs.shape[1]//2) + starts
+    #new_coords[:,1] = new_coords[:,1].astype(int) - (seqs.shape[1]//2) + starts
+    new_coords[:,1] = new_coords[:,1].astype(int) + starts
 
     return take_per_row(seqs, starts, seq_crop_width), take_per_row(labels, starts, label_crop_width), new_coords
 

setup.py

@@ -10,10 +10,11 @@
     'name': 'chrombpnet',
     'author_email': 'anusri @ stanford.edu',
     'license': 'MIT',
+    'license_files': ('LICENSE.txt',),
     'include_package_data': True,
     'description': 'chrombpnet predicts chromatin accessibility from sequence',
     'download_url': 'https://github.com/kundajelab/chrombpnet',
-    'version': '0.1.7',
+    'version': '1.0.1',
     'packages': find_packages(),
     'python_requires': '>=3.8',
     'install_requires': install_requires,