def test_put_one_file1_in_dir(self):
"""
Test that one file is put in the dir correctly
"""
with TemporaryDirectory() as tmpdir, NamedTemporaryFile() as file1:
output_dir = os.path.join(tmpdir, "output")
input_json = {
"output_directory_name": output_dir,
"files": [
{
"class": "File",
"path": file1.name
},
]
}
input_json_file = os.path.join(tmpdir, "input.json")
with open(input_json_file, "w") as input_json_file_data:
json.dump(input_json, input_json_file_data)
output_json, output_dir = run_cwl(
testcase = self,
tmpdir = tmpdir,
input_json = input_json,
cwl_file = cwl_file)
# make sure the dir exists
self.assertTrue(os.path.exists( output_json['directory']['path'] ))
self.assertTrue(os.path.isdir( output_json['directory']['path'] ))
self.assertEqual(output_dir, output_json['directory']['path'])
# make sure both files were output to the dir
self.assertEqual(len(os.listdir(output_json['directory']['path'])), 1)
self.assertTrue(os.path.basename(file1.name)in os.listdir(output_json['directory']['path']) )
def test_snp_pileup2(self):
"""
"""
self.maxDiff = None
input_json = {
"bam_file": {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['BAM_DIR'],
"Sample24.bam"),
"class":
"File"
},
"ref_fasta": {
"path": REF_FASTA,
"class": "File"
},
"regions_bed": {
"path": CONPAIR_MARKERS_BED,
"class": "File"
},
"output_filename": "Sample24.pileup"
}
with TemporaryDirectory() as tmpdir:
output_json, output_dir = run_cwl(testcase=self,
tmpdir=tmpdir,
input_json=input_json,
cwl_file=cwl_file,
CWL_ARGS=CWL_ARGS)
expected_output = {
'output_file': {
'location':
'file://' + os.path.join(output_dir, "Sample24.pileup"),
'basename':
"Sample24.pileup",
'class':
'File',
'checksum':
'sha1$a27039b65de9272e9a7b180e1b0e911c6484efbe',
'size':
2358580,
'path':
os.path.join(output_dir, "Sample24.pileup")
}
}
self.assertDictEqual(output_json, expected_output)
pileup_path = os.path.join(output_dir, "Sample24.pileup")
# shutil.copyfile(pileup_path, "Sample24.pileup")
lines = num_lines(pileup_path)
self.assertEqual(lines, 1024)
fields = num_fields(pileup_path, delimiter=' ')
self.assertEqual(fields, 8)
def test_put_two_files_in_dir(self):
"""
Test that two files are put in the dir correctly
"""
with TemporaryDirectory() as tmpdir, NamedTemporaryFile() as file1, NamedTemporaryFile() as file2:
# set path to the dir which this CWL should to output to
output_dir = os.path.join(tmpdir, "output")
# create input data
input_json = {
"output_directory_name": output_dir,
"files": [
{
"class": "File",
"path": file1.name
},
{
"class": "File",
"path": file2.name
}
]
}
# write input data
input_json_file = os.path.join(tmpdir, "input.json")
with open(input_json_file, "w") as input_json_file_data:
json.dump(input_json, input_json_file_data)
output_json, output_dir = run_cwl(
testcase = self,
tmpdir = tmpdir,
input_json = input_json,
cwl_file = cwl_file)
# make sure the output is a dir
self.assertTrue("directory" in output_json)
# make sure there's only one element output
self.assertEqual(len(output_json), 1)
# make sure the dir exists
self.assertTrue(os.path.exists( output_json['directory']['path'] ))
self.assertTrue(os.path.isdir( output_json['directory']['path'] ))
self.assertEqual(output_dir, output_json['directory']['path'])
# make sure both files were output to the dir
self.assertEqual(len(os.listdir(output_json['directory']['path'])), 2)
self.assertTrue(os.path.basename(file1.name)in os.listdir(output_json['directory']['path']) )
self.assertTrue(os.path.basename(file2.name)in os.listdir(output_json['directory']['path']) )
def test_extrace2(self):
"""
"""
self.maxDiff = None
input_json = {
"bam_file": {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['BAM_DIR'],
"Sample24.bam"),
"class":
"File"
},
"ref_fasta": {
"path": REF_FASTA,
"class": "File"
},
"sites": {
"path": SOMALIER_SITES,
"class": "File"
}
}
with TemporaryDirectory() as tmpdir:
output_json, output_dir = run_cwl(testcase=self,
tmpdir=tmpdir,
input_json=input_json,
cwl_file=cwl_file)
expected_output = {
'output_file': {
'location':
'file://' + os.path.join(output_dir, "Sample24.somalier"),
'basename':
"Sample24.somalier",
'class':
'File',
'checksum':
'sha1$e912f8368502325be0bae87e8b6205ff627ec8d8',
'size':
12292,
'path':
os.path.join(output_dir, "Sample24.somalier")
}
}
self.assertDictEqual(output_json, expected_output)
def test_extrace1(self):
"""
"""
self.maxDiff = None
input_json = {
"bam_file": {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['BAM_DIR'],
"Sample23.bam"),
"class":
"File"
},
"ref_fasta": {
"path": REF_FASTA,
"class": "File"
},
"sites": {
"path": SOMALIER_SITES,
"class": "File"
}
}
with TemporaryDirectory() as tmpdir:
output_json, output_dir = run_cwl(testcase=self,
tmpdir=tmpdir,
input_json=input_json,
cwl_file=cwl_file)
expected_output = {
'output_file': {
'location':
'file://' + os.path.join(output_dir, "Sample23.somalier"),
'basename':
"Sample23.somalier",
'class':
'File',
'checksum':
'sha1$159fa91beded446440962f211b3fa771e5764559',
'size':
12292,
'path':
os.path.join(output_dir, "Sample23.somalier")
}
}
self.assertDictEqual(output_json, expected_output)
def test_workflow1(self):
input_json = {
"tumor_bam": {
"path": os.path.join(DATA_SETS['Proj_08390_G']['BAM_DIR'], "Sample23.bam"),
"class": "File"
},
"ref_fasta": {
"path": REF_FASTA,
"class": "File"
},
"conpair_markers_bed": {
"path": CONPAIR_MARKERS_BED,
"class": "File"
},
"conpair_markers_txt": {
"path": CONPAIR_MARKERS_TXT,
"class": "File"
},
"somalier_sites": {
"path": SOMALIER_SITES,
"class": "File"
},
"conpair_normal_genotypes": [
{
"path": os.path.join(DATA_SETS['Proj_08390_G']['LIKELIHOODS_DIR'], "Sample24.pickle"),
"class": "File"
}
],
"somalier_normal_genotypes": [
{
"path": os.path.join(DATA_SETS['Proj_08390_G']['GENOTYPES_DIR'], "Sample24.somalier"),
"class": "File"
}
]
}
with TemporaryDirectory() as tmpdir:
output_json, output_dir = run_cwl(
testcase = self,
tmpdir = tmpdir,
input_json = input_json,
cwl_file = cwl_file)
expected_output = {
'conpair_tsv': {
'location': 'file://' + os.path.join(output_dir, "Sample23.pickle.concordance.tsv"),
'basename': "Sample23.pickle.concordance.tsv",
'class': 'File',
# 'checksum': 'sha1$e7f75336ea679447df581c4e2edc7339bae9a186', # dont use the checksum or size because the file contains the filepaths and those change each time
# 'size': 279,
'path': os.path.join(output_dir, "Sample23.pickle.concordance.tsv")
},
'somalier_html': {
'location': 'file://' + os.path.join(output_dir, "somalier.html"),
'basename': "somalier.html",
'class': 'File',
'checksum': 'sha1$d78cc873ba6219ab57d5d76836eed8212cd96ce6',
'size': 22950,
'path': os.path.join(output_dir, "somalier.html")
},
'somalier_pairs_tsv': {
'location': 'file://' + os.path.join(output_dir, "somalier.pairs.tsv"),
'basename': "somalier.pairs.tsv",
'class': 'File',
'checksum': 'sha1$fdb140d0db51b0ede23208210ae59d03e496c5f6',
'size': 244,
'path': os.path.join(output_dir, "somalier.pairs.tsv")
},
'somalier_samples_tsv': {
'location': 'file://' + os.path.join(output_dir, "somalier.samples.tsv"),
'basename': "somalier.samples.tsv",
'class': 'File',
'checksum': 'sha1$c98bd07ac5a3a90c1386438171fa1113a06a14e8',
'size': 468,
'path': os.path.join(output_dir, "somalier.samples.tsv")
},
}
output_json['conpair_tsv'].pop('checksum')
output_json['conpair_tsv'].pop('size')
self.assertDictEqual(output_json, expected_output)
#
# Validate Conpair output
#
concordance_file = os.path.join(output_dir, "Sample23.pickle.concordance.tsv")
with open(concordance_file) as f:
reader = csv.DictReader(f, delimiter = '\t')
rows = [ row for row in reader ]
expected_rows = [
{
'concordance': '0.9885297184567258',
'num_markers_used': '959',
'num_total_markers': '1024',
'tumor': 'Sample23',
'normal': 'Sample24',
# 'tumor_pileup': '/var/lib/cwl/stg35428a9e-1166-4a1f-87ad-70697da56267/Sample23.pickle',
# 'normal_pileup': '/var/lib/cwl/stgc3f9e453-b2d4-4085-95df-07686d22f8ff/normals/Sample24.pickle'
}
]
# remove the file paths from the results because they always change
rows[0].pop('tumor_pileup')
rows[0].pop('normal_pileup')
self.assertEqual(rows, expected_rows)
#
# Validate Somalier output
#
pairs_tsv = os.path.join(output_dir, "somalier.pairs.tsv")
self.assertEqual(num_lines(pairs_tsv), 2)
self.assertEqual(num_fields(pairs_tsv), 17)
with open(pairs_tsv) as f:
reader = csv.DictReader(f, delimiter = '\t')
rows = [ row for row in reader ]
expected_rows = [
{'#sample_a': 'Sample23', 'sample_b': 'Sample24', 'relatedness': '1.000', 'ibs0': '0', 'ibs2': '627', 'hom_concordance': '0.870', 'hets_a': '403', 'hets_b': '370', 'hets_ab': '694', 'shared_hets': '347', 'hom_alts_a': '207', 'hom_alts_b': '146', 'shared_hom_alts': '127', 'n': '627', 'x_ibs0': '0', 'x_ibs2': '17', 'expected_relatedness': '-1.0'}
]
self.assertEqual(rows, expected_rows)
samples_tsv = os.path.join(output_dir, "somalier.samples.tsv")
self.assertEqual(num_lines(samples_tsv), 3)
self.assertEqual(num_fields(samples_tsv), 25)
with open(samples_tsv) as f:
reader = csv.DictReader(f, delimiter = '\t')
rows = [ row for row in reader ]
expected_rows = [
{'#family_id': 'Sample23', 'sample_id': 'Sample23', 'paternal_id': '-9', 'maternal_id': '-9', 'sex': '-9', 'phenotype': '-9', 'original_pedigree_sex': '-9', 'gt_depth_mean': '31.5', 'gt_depth_sd': '21.3', 'depth_mean': '30.8', 'depth_sd': '21.5', 'ab_mean': '0.44', 'ab_std': '0.41', 'n_hom_ref': '258', 'n_het': '403', 'n_hom_alt': '207', 'n_unknown': '122', 'p_middling_ab': '0.048', 'X_depth_mean': '16.28', 'X_n': '25', 'X_hom_ref': '7', 'X_het': '1', 'X_hom_alt': '17', 'Y_depth_mean': '0.00', 'Y_n': '0'},
{'#family_id': 'Sample24', 'sample_id': 'Sample24', 'paternal_id': '-9', 'maternal_id': '-9', 'sex': '-9', 'phenotype': '-9', 'original_pedigree_sex': '-9', 'gt_depth_mean': '25.8', 'gt_depth_sd': '13.8', 'depth_mean': '24.8', 'depth_sd': '14.2', 'ab_mean': '0.43', 'ab_std': '0.44', 'n_hom_ref': '174', 'n_het': '370', 'n_hom_alt': '146', 'n_unknown': '300', 'p_middling_ab': '0.147', 'X_depth_mean': '15.44', 'X_n': '18', 'X_hom_ref': '7', 'X_het': '1', 'X_hom_alt': '10', 'Y_depth_mean': '0.00', 'Y_n': '0'}
]
self.assertEqual(rows, expected_rows)
def test_workflow1(self):
input_json = {
"tumor_bam": {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['BAM_DIR'],
"Sample23.bam"),
"class":
"File"
},
"ref_fasta": {
"path": REF_FASTA,
"class": "File"
},
"markers_bed": {
"path": CONPAIR_MARKERS_BED,
"class": "File"
},
"markers_txt": {
"path": CONPAIR_MARKERS_TXT,
"class": "File"
},
"normal_genotypes": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['LIKELIHOODS_DIR'],
"Sample24.pickle"),
"class":
"File"
}]
}
with TemporaryDirectory() as tmpdir:
output_json, output_dir = run_cwl(testcase=self,
tmpdir=tmpdir,
input_json=input_json,
cwl_file=cwl_file)
expected_output = {
'concordance_tsv': {
'location':
'file://' + os.path.join(
output_dir, "Sample23.pickle.concordance.tsv"),
'basename':
"Sample23.pickle.concordance.tsv",
'class':
'File',
# 'checksum': 'sha1$e7f75336ea679447df581c4e2edc7339bae9a186', # dont use the checksum or size because the file contains the filepaths and those change each time
# 'size': 279,
'path':
os.path.join(output_dir, "Sample23.pickle.concordance.tsv")
}
}
output_json['concordance_tsv'].pop('checksum')
output_json['concordance_tsv'].pop('size')
self.assertDictEqual(output_json, expected_output)
concordance_file = os.path.join(output_dir,
"Sample23.pickle.concordance.tsv")
with open(concordance_file) as f:
reader = csv.DictReader(f, delimiter='\t')
rows = [row for row in reader]
expected_rows = [{
'concordance': '0.9885297184567258',
'num_markers_used': '959',
'num_total_markers': '1024',
'tumor': 'Sample23',
'normal': 'Sample24',
# 'tumor_pileup': '/var/lib/cwl/stg35428a9e-1166-4a1f-87ad-70697da56267/Sample23.pickle',
# 'normal_pileup': '/var/lib/cwl/stgc3f9e453-b2d4-4085-95df-07686d22f8ff/normals/Sample24.pickle'
}]
# remove the file paths from the results because they always change
rows[0].pop('tumor_pileup')
rows[0].pop('normal_pileup')
self.assertEqual(rows, expected_rows)
def test_workflow1(self):
input_json = {
"tumor_bam": {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['BAM_DIR'],
"Sample23.bam"),
"class":
"File"
},
"ref_fasta": {
"path": REF_FASTA,
"class": "File"
},
"sites": {
"path": SOMALIER_SITES,
"class": "File"
},
"normal_genotypes": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['GENOTYPES_DIR'],
"Sample24.somalier"),
"class":
"File"
}]
}
with TemporaryDirectory() as tmpdir:
output_json, output_dir = run_cwl(testcase=self,
tmpdir=tmpdir,
input_json=input_json,
cwl_file=cwl_file)
expected_output = {
'somalier_html': {
'location':
'file://' + os.path.join(output_dir, "somalier.html"),
'basename':
"somalier.html",
'class':
'File',
'checksum':
'sha1$d78cc873ba6219ab57d5d76836eed8212cd96ce6',
'size':
22950,
'path':
os.path.join(output_dir, "somalier.html")
},
'somalier_pairs_tsv': {
'location':
'file://' + os.path.join(output_dir, "somalier.pairs.tsv"),
'basename':
"somalier.pairs.tsv",
'class':
'File',
'checksum':
'sha1$fdb140d0db51b0ede23208210ae59d03e496c5f6',
'size':
244,
'path':
os.path.join(output_dir, "somalier.pairs.tsv")
},
'somalier_samples_tsv': {
'location':
'file://' +
os.path.join(output_dir, "somalier.samples.tsv"),
'basename':
"somalier.samples.tsv",
'class':
'File',
'checksum':
'sha1$c98bd07ac5a3a90c1386438171fa1113a06a14e8',
'size':
468,
'path':
os.path.join(output_dir, "somalier.samples.tsv")
},
}
self.assertDictEqual(output_json, expected_output)
pairs_tsv = os.path.join(output_dir, "somalier.pairs.tsv")
self.assertEqual(num_lines(pairs_tsv), 2)
self.assertEqual(num_fields(pairs_tsv), 17)
with open(pairs_tsv) as f:
reader = csv.DictReader(f, delimiter='\t')
rows = [row for row in reader]
expected_rows = [{
'#sample_a': 'Sample23',
'sample_b': 'Sample24',
'relatedness': '1.000',
'ibs0': '0',
'ibs2': '627',
'hom_concordance': '0.870',
'hets_a': '403',
'hets_b': '370',
'hets_ab': '694',
'shared_hets': '347',
'hom_alts_a': '207',
'hom_alts_b': '146',
'shared_hom_alts': '127',
'n': '627',
'x_ibs0': '0',
'x_ibs2': '17',
'expected_relatedness': '-1.0'
}]
self.assertEqual(rows, expected_rows)
samples_tsv = os.path.join(output_dir, "somalier.samples.tsv")
self.assertEqual(num_lines(samples_tsv), 3)
self.assertEqual(num_fields(samples_tsv), 25)
with open(samples_tsv) as f:
reader = csv.DictReader(f, delimiter='\t')
rows = [row for row in reader]
expected_rows = [{
'#family_id': 'Sample23',
'sample_id': 'Sample23',
'paternal_id': '-9',
'maternal_id': '-9',
'sex': '-9',
'phenotype': '-9',
'original_pedigree_sex': '-9',
'gt_depth_mean': '31.5',
'gt_depth_sd': '21.3',
'depth_mean': '30.8',
'depth_sd': '21.5',
'ab_mean': '0.44',
'ab_std': '0.41',
'n_hom_ref': '258',
'n_het': '403',
'n_hom_alt': '207',
'n_unknown': '122',
'p_middling_ab': '0.048',
'X_depth_mean': '16.28',
'X_n': '25',
'X_hom_ref': '7',
'X_het': '1',
'X_hom_alt': '17',
'Y_depth_mean': '0.00',
'Y_n': '0'
}, {
'#family_id': 'Sample24',
'sample_id': 'Sample24',
'paternal_id': '-9',
'maternal_id': '-9',
'sex': '-9',
'phenotype': '-9',
'original_pedigree_sex': '-9',
'gt_depth_mean': '25.8',
'gt_depth_sd': '13.8',
'depth_mean': '24.8',
'depth_sd': '14.2',
'ab_mean': '0.43',
'ab_std': '0.44',
'n_hom_ref': '174',
'n_het': '370',
'n_hom_alt': '146',
'n_unknown': '300',
'p_middling_ab': '0.147',
'X_depth_mean': '15.44',
'X_n': '18',
'X_hom_ref': '7',
'X_het': '1',
'X_hom_alt': '10',
'Y_depth_mean': '0.00',
'Y_n': '0'
}]
self.assertEqual(rows, expected_rows)
def test_concordance1(self):
self.maxDiff = None
with TemporaryDirectory() as tmpdir:
# copy over a Normal genotype file to the temporary dir
normals_dir_path = os.path.join(tmpdir, "normals")
os.mkdir(normals_dir_path)
shutil.copyfile(
os.path.join(DATA_SETS['Proj_08390_G']['GENOTYPES_DIR'],
"Sample24.somalier"),
os.path.join(normals_dir_path, "Sample24.somalier"))
input_json = {
"tumor_genotype_file": {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['GENOTYPES_DIR'],
"Sample23.somalier"),
"class":
"File"
},
"cohort_genotype_dir": {
"path": normals_dir_path,
"class": "Directory"
}
}
output_json, output_dir = run_cwl(testcase=self,
tmpdir=tmpdir,
input_json=input_json,
CWL_ARGS=CWL_ARGS,
cwl_file=cwl_file,
print_stdout=False,
print_command=False,
check_returncode=True)
expected_output = {
'html': {
'location':
'file://' + os.path.join(output_dir, "somalier.html"),
'basename':
"somalier.html",
'class':
'File',
'checksum':
'sha1$d78cc873ba6219ab57d5d76836eed8212cd96ce6',
'size':
22950,
'path':
os.path.join(output_dir, "somalier.html")
},
'pairs_tsv': {
'location':
'file://' + os.path.join(output_dir, "somalier.pairs.tsv"),
'basename':
"somalier.pairs.tsv",
'class':
'File',
'checksum':
'sha1$fdb140d0db51b0ede23208210ae59d03e496c5f6',
'size':
244,
'path':
os.path.join(output_dir, "somalier.pairs.tsv")
},
'samples_tsv': {
'location':
'file://' +
os.path.join(output_dir, "somalier.samples.tsv"),
'basename':
"somalier.samples.tsv",
'class':
'File',
'checksum':
'sha1$c98bd07ac5a3a90c1386438171fa1113a06a14e8',
'size':
468,
'path':
os.path.join(output_dir, "somalier.samples.tsv")
},
}
self.assertDictEqual(output_json, expected_output)
pairs_tsv = os.path.join(output_dir, "somalier.pairs.tsv")
self.assertEqual(num_lines(pairs_tsv), 2)
self.assertEqual(num_fields(pairs_tsv), 17)
with open(pairs_tsv) as f:
reader = csv.DictReader(f, delimiter='\t')
rows = [row for row in reader]
expected_rows = [{
'#sample_a': 'Sample23',
'sample_b': 'Sample24',
'relatedness': '1.000',
'ibs0': '0',
'ibs2': '627',
'hom_concordance': '0.870',
'hets_a': '403',
'hets_b': '370',
'hets_ab': '694',
'shared_hets': '347',
'hom_alts_a': '207',
'hom_alts_b': '146',
'shared_hom_alts': '127',
'n': '627',
'x_ibs0': '0',
'x_ibs2': '17',
'expected_relatedness': '-1.0'
}]
self.assertEqual(rows, expected_rows)
samples_tsv = os.path.join(output_dir, "somalier.samples.tsv")
self.assertEqual(num_lines(samples_tsv), 3)
self.assertEqual(num_fields(samples_tsv), 25)
with open(samples_tsv) as f:
reader = csv.DictReader(f, delimiter='\t')
rows = [row for row in reader]
expected_rows = [{
'#family_id': 'Sample23',
'sample_id': 'Sample23',
'paternal_id': '-9',
'maternal_id': '-9',
'sex': '-9',
'phenotype': '-9',
'original_pedigree_sex': '-9',
'gt_depth_mean': '31.5',
'gt_depth_sd': '21.3',
'depth_mean': '30.8',
'depth_sd': '21.5',
'ab_mean': '0.44',
'ab_std': '0.41',
'n_hom_ref': '258',
'n_het': '403',
'n_hom_alt': '207',
'n_unknown': '122',
'p_middling_ab': '0.048',
'X_depth_mean': '16.28',
'X_n': '25',
'X_hom_ref': '7',
'X_het': '1',
'X_hom_alt': '17',
'Y_depth_mean': '0.00',
'Y_n': '0'
}, {
'#family_id': 'Sample24',
'sample_id': 'Sample24',
'paternal_id': '-9',
'maternal_id': '-9',
'sex': '-9',
'phenotype': '-9',
'original_pedigree_sex': '-9',
'gt_depth_mean': '25.8',
'gt_depth_sd': '13.8',
'depth_mean': '24.8',
'depth_sd': '14.2',
'ab_mean': '0.43',
'ab_std': '0.44',
'n_hom_ref': '174',
'n_het': '370',
'n_hom_alt': '146',
'n_unknown': '300',
'p_middling_ab': '0.147',
'X_depth_mean': '15.44',
'X_n': '18',
'X_hom_ref': '7',
'X_het': '1',
'X_hom_alt': '10',
'Y_depth_mean': '0.00',
'Y_n': '0'
}]
self.assertEqual(rows, expected_rows)