def test_add_impact_1(self):
"""
Test that a maf file with is_in_IMPACT column comes out as expected
"""
self.maxDiff = None
input_maf = os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample1.Sample2.muts.maf")
with TemporaryDirectory() as tmpdir:
input_json = {
"input_file": {
"class": "File",
"path": input_maf
},
"output_filename": 'output.maf',
"IMPACT_file": {
"class": "File",
"path": IMPACT_FILE
},
}
output_json, output_dir = run_cwl(testcase=self,
tmpdir=tmpdir,
input_json=input_json,
cwl_file=cwl_file)
expected_output = {
'IMPACT_col_added_file': {
'location':
'file://' + os.path.join(output_dir, 'output.maf'),
'basename': 'output.maf',
'class': 'File',
'checksum':
'sha1$1397fade2f877c2bcfca791407e328c5c48e6ff0',
'size': 15629589,
'path': os.path.join(output_dir, 'output.maf')
}
}
self.assertDictEqual(output_json, expected_output)
# validate output mutation file contents
with open(output_json['IMPACT_col_added_file']['path']) as fin:
output_maf_lines = len(fin.readlines())
self.assertEqual(output_maf_lines, 12518)
input_comments, input_mutations = load_mutations(input_maf)
output_comments, output_mutations = load_mutations(
output_json['IMPACT_col_added_file']['path'])
true_count = [row['is_in_impact']
for row in output_mutations].count('True')
false_count = [row['is_in_impact']
for row in output_mutations].count('False')
self.assertTrue(true_count == 8367)
self.assertTrue(false_count == 4147)
# check that its got two extra columns in the output
self.assertTrue(
len(input_mutations[1]) + 2 == len(output_mutations[1]))
def test_add_af(self):
"""
Test IMPACT CWL with tiny dataset
"""
maf_lines = [
['# comment 1'],
['# comment 2'],
['Hugo_Symbol', 't_depth', 't_alt_count'],
['SUFU', '100', '75'],
['GOT1', '100', '1'],
['SOX9', '100', '0'],
]
with TemporaryDirectory() as tmpdir:
input_maf = write_table(tmpdir=tmpdir,
filename='input.maf',
lines=maf_lines)
input_json = {
"input_file": {
"class": "File",
"path": input_maf
},
"output_filename": 'output.maf',
}
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, 'output.maf'),
'basename': 'output.maf',
'class': 'File',
'checksum':
'sha1$39de59ad5d736db692504012ce86d3395685112e',
'size': 109,
'path': os.path.join(output_dir, 'output.maf')
}
}
self.assertDictEqual(output_json, expected_output)
comments, mutations = load_mutations(
output_json['output_file']['path'])
expected_comments = ['# comment 1', '# comment 2']
self.assertEqual(comments, expected_comments)
expected_mutations = [{
'Hugo_Symbol': 'SUFU',
't_depth': '100',
't_alt_count': '75',
't_af': '0.75'
}, {
'Hugo_Symbol': 'GOT1',
't_depth': '100',
't_alt_count': '1',
't_af': '0.01'
}, {
'Hugo_Symbol': 'SOX9',
't_depth': '100',
't_alt_count': '0',
't_af': '0.0'
}]
self.assertEqual(mutations, expected_mutations)
def test_add_impact_0(self):
"""
Test IMPACT CWL with tiny dataset
"""
maf_lines = [['# comment 1'], ['# comment 2'], ['Hugo_Symbol'],
['SUFU'], ['GOT1'], ['BRCA']]
impact_lines = [['BRCA', 'IMPACT468'], ['SUFU', 'IMPACT468'],
['SUFU', 'IMPACT505']]
with TemporaryDirectory() as tmpdir:
input_maf = write_table(tmpdir=tmpdir,
filename='input.maf',
lines=maf_lines)
impact_file = write_table(tmpdir=tmpdir,
filename='impact.txt',
lines=impact_lines)
input_json = {
"input_file": {
"class": "File",
"path": input_maf
},
"output_filename": 'output.maf',
"IMPACT_file": {
"class": "File",
"path": impact_file
},
}
output_json, output_dir = run_cwl(testcase=self,
tmpdir=tmpdir,
input_json=input_json,
cwl_file=cwl_file)
expected_output = {
'IMPACT_col_added_file': {
'location':
'file://' + os.path.join(output_dir, 'output.maf'),
'basename': 'output.maf',
'class': 'File',
'checksum':
'sha1$5c61f3977dad29ebc74966e8fc40a0278f9aab12',
'size': 126,
'path': os.path.join(output_dir, 'output.maf')
}
}
self.assertDictEqual(output_json, expected_output)
comments, mutations = load_mutations(
output_json['IMPACT_col_added_file']['path'])
expected_comments = ['# comment 1', '# comment 2']
self.assertEqual(comments, expected_comments)
expected_mutations = [{
'Hugo_Symbol': 'SUFU',
'is_in_impact': 'True',
'impact_assays': 'IMPACT468,IMPACT505'
}, {
'Hugo_Symbol': 'GOT1',
'is_in_impact': 'False',
'impact_assays': '.'
}, {
'Hugo_Symbol': 'BRCA',
'is_in_impact': 'True',
'impact_assays': 'IMPACT468'
}]
self.assertEqual(mutations, expected_mutations)
def test_run_worflow_mixed_mafs(self):
"""
Test that the workflow works correctly when run with a mix of Argos muts.maf files and Facets Suite annotated maf files
The Facets Suite maf files have extra columns that need to be retained in the output
"""
input_json = {
"is_impact":
True,
"argos_version_string":
"2.x",
"analysis_gene_cna_filename":
"Proj_08390_G.gene.cna.txt",
"analysis_mutations_filename":
"Proj_08390_G.muts.maf",
"analysis_mutations_share_filename":
"Proj_08390_G.muts.share.maf",
"analysis_segment_cna_filename":
"Proj_08390_G.seg.cna.txt",
"analysis_sv_filename":
"Proj_08390_G.svs.maf",
"helix_filter_version":
"20.06.1",
"IMPACT_gene_list": {
"class": "File",
"path": IMPACT_FILE
},
"targets_list": {
"path": DATA_SETS['Proj_08390_G']["targets_list"],
"class": "File"
},
"mutation_maf_files": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample1.Sample2.muts.maf"),
"class":
"File"
}, {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['FACETS_SUITE_DIR'],
"Sample4.Sample3_hisens.ccf.portal.maf"),
"class":
"File"
}],
"mutation_svs_maf_files": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample1.Sample2.svs.pass.vep.maf"),
"class":
"File"
}, {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample4.Sample3.svs.pass.vep.maf"),
"class":
"File"
}],
"facets_hisens_cncf_files": [{
"path":
os.path.join(
DATA_SETS['Proj_08390_G']['FACETS_DIR'],
"Sample2.rg.md.abra.printreads__Sample1.rg.md.abra.printreads_hisens.cncf.txt"
),
"class":
"File"
}, {
"path":
os.path.join(
DATA_SETS['Proj_08390_G']['FACETS_DIR'],
"Sample3.rg.md.abra.printreads__Sample4.rg.md.abra.printreads_hisens.cncf.txt"
),
"class":
"File"
}],
"facets_hisens_seg_files": [{
"path":
os.path.join(
DATA_SETS['Proj_08390_G']['FACETS_DIR'],
"Sample2.rg.md.abra.printreads__Sample1.rg.md.abra.printreads_hisens.seg"
),
"class":
"File"
}, {
"path":
os.path.join(
DATA_SETS['Proj_08390_G']['FACETS_DIR'],
"Sample3.rg.md.abra.printreads__Sample4.rg.md.abra.printreads_hisens.seg"
),
"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 = {
'analysis_dir': {
'class':
'Directory',
'basename':
'analysis',
'location':
'file://' + os.path.join(output_dir, 'analysis'),
'path':
os.path.join(output_dir, 'analysis'),
'listing': [{
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.gene.cna.txt'),
'basename':
'Proj_08390_G.gene.cna.txt',
'class':
'File',
'checksum':
'sha1$ab17d587ad5ae0a87fd6c6d4dd2d5d1701208ce9',
'size':
173982,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.gene.cna.txt')
}, {
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.muts.maf'),
'basename':
'Proj_08390_G.muts.maf',
'class':
'File',
'checksum':
'sha1$66a87cb8cc2eea31f490852d468bedd958c4ecc5',
'size':
59915,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.muts.maf')
}, {
'location':
'file://' +
os.path.join(output_dir,
'analysis/Proj_08390_G.muts.share.maf'),
'basename':
'Proj_08390_G.muts.share.maf',
'class':
'File',
'checksum':
'sha1$cbaa23bb848978cde135efd3870db8f35b3f2861',
'size':
10729,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.muts.share.maf')
}, {
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.seg.cna.txt'),
'basename':
'Proj_08390_G.seg.cna.txt',
'class':
'File',
'checksum':
'sha1$f6a77b280c047a7e2082e3a09e8138f861790d3a',
'size':
3191,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.seg.cna.txt')
}, {
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.svs.maf'),
'basename':
'Proj_08390_G.svs.maf',
'class':
'File',
'checksum':
'sha1$5c2a63fc01980550108e58079a8b689d53c97d8c',
'size':
35595,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.svs.maf')
}]
}
}
self.maxDiff = None
self.assertDictEqual(output_json, expected_output)
comments, mutations = load_mutations(
os.path.join(output_dir, 'analysis', 'Proj_08390_G.muts.maf'))
self.assertEqual(len(mutations), 34)
colnames = mutations[0].keys()
some_required_colnames = [
"ASCN.TOTAL_COPY_NUMBER", "ASCN.MINOR_COPY_NUMBER",
"ASCN.EXPECTED_ALT_COPIES", "ASCN.CCF_EXPECTED_COPIES",
"ASCN.CCF_EXPECTED_COPIES_LOWER",
"ASCN.CCF_EXPECTED_COPIES_UPPER", "ASCN.ASCN_METHOD",
"ASCN.ASCN_INTEGER_COPY_NUMBER"
]
for colname in some_required_colnames:
self.assertTrue(
colname in colnames,
"Column label {} not present in the mutation file. Missing columns: {}"
.format(colname, [
c for c in some_required_colnames if c not in colnames
]))
self.assertEqual(mutations[0]['t_af'], '0.42953020134228187')
self.assertEqual(mutations[0]['is_in_impact'], 'True')
self.assertEqual(mutations[0]['impact_assays'],
'IMPACT341,IMPACT410,IMPACT468,IMPACT505')
def test_run_worflow_one_maf(self):
"""
Test that the workflow works correctly when run with a single maf
"""
input_json = {
"is_impact":
True,
"argos_version_string":
"2.x",
"analysis_gene_cna_filename":
"Proj_08390_G.gene.cna.txt",
"analysis_mutations_filename":
"Proj_08390_G.muts.maf",
"analysis_mutations_share_filename":
"Proj_08390_G.muts.share.maf",
"analysis_segment_cna_filename":
"Proj_08390_G.seg.cna.txt",
"analysis_sv_filename":
"Proj_08390_G.svs.maf",
"helix_filter_version":
"20.06.1",
"IMPACT_gene_list": {
"class": "File",
"path": IMPACT_FILE
},
"targets_list": {
"path": DATA_SETS['Proj_08390_G']["targets_list"],
"class": "File"
},
"mutation_maf_files": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample1.Sample2.muts.maf"),
"class":
"File"
}],
"mutation_svs_maf_files": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample1.Sample2.svs.pass.vep.maf"),
"class":
"File"
}],
"facets_hisens_cncf_files": [{
"path":
os.path.join(
DATA_SETS['Proj_08390_G']['FACETS_DIR'],
"Sample2.rg.md.abra.printreads__Sample1.rg.md.abra.printreads_hisens.cncf.txt"
),
"class":
"File"
}],
"facets_hisens_seg_files": [{
"path":
os.path.join(
DATA_SETS['Proj_08390_G']['FACETS_DIR'],
"Sample2.rg.md.abra.printreads__Sample1.rg.md.abra.printreads_hisens.seg"
),
"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 = {
'analysis_dir': {
'class':
'Directory',
'basename':
'analysis',
'location':
'file://' + os.path.join(output_dir, 'analysis'),
'path':
os.path.join(output_dir, 'analysis'),
'listing': [{
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.gene.cna.txt'),
'basename':
'Proj_08390_G.gene.cna.txt',
'class':
'File',
'checksum':
'sha1$7cc89d24556de93b9a409812317581e67e5df494',
'size':
87905,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.gene.cna.txt')
}, {
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.muts.maf'),
'basename':
'Proj_08390_G.muts.maf',
'class':
'File',
'checksum':
'sha1$2c8904927a917d6e935ef207582d995680574d16',
'size':
33243,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.muts.maf')
}, {
'location':
'file://' +
os.path.join(output_dir,
'analysis/Proj_08390_G.muts.share.maf'),
'basename':
'Proj_08390_G.muts.share.maf',
'class':
'File',
'checksum':
'sha1$b5af4e0fcd89fecabf8095aa3d7690e5edb8dca1',
'size':
7462,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.muts.share.maf')
}, {
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.seg.cna.txt'),
'basename':
'Proj_08390_G.seg.cna.txt',
'class':
'File',
'checksum':
'sha1$f0ebb82c34b6530447fa1e70b6dedcc039840d61',
'size':
1632,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.seg.cna.txt')
}, {
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.svs.maf'),
'basename':
'Proj_08390_G.svs.maf',
'class':
'File',
'checksum':
'sha1$df420706bb5b772a79317843c0a01a3c88a9571d',
'size':
23603,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.svs.maf')
}]
}
}
self.maxDiff = None
self.assertDictEqual(output_json, expected_output)
comments, mutations = load_mutations(
os.path.join(output_dir, 'analysis', 'Proj_08390_G.muts.maf'))
self.assertEqual(len(mutations), 22)
def test_run_worflow_two_mafs(self):
"""
Test that the workflow works correctly when run with two maf files
"""
input_json = {
"is_impact":
True,
"argos_version_string":
"2.x",
"analysis_gene_cna_filename":
"Proj_08390_G.gene.cna.txt",
"analysis_mutations_filename":
"Proj_08390_G.muts.maf",
"analysis_mutations_share_filename":
"Proj_08390_G.muts.share.maf",
"analysis_segment_cna_filename":
"Proj_08390_G.seg.cna.txt",
"analysis_sv_filename":
"Proj_08390_G.svs.maf",
"helix_filter_version":
"20.06.1",
"IMPACT_gene_list": {
"class": "File",
"path": IMPACT_FILE
},
"targets_list": {
"path": DATA_SETS['Proj_08390_G']["targets_list"],
"class": "File"
},
"mutation_maf_files": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample1.Sample2.muts.maf"),
"class":
"File"
}, {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample4.Sample3.muts.maf"),
"class":
"File"
}],
"mutation_svs_maf_files": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample1.Sample2.svs.pass.vep.maf"),
"class":
"File"
}, {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample4.Sample3.svs.pass.vep.maf"),
"class":
"File"
}],
"facets_hisens_cncf_files": [{
"path":
os.path.join(
DATA_SETS['Proj_08390_G']['FACETS_DIR'],
"Sample2.rg.md.abra.printreads__Sample1.rg.md.abra.printreads_hisens.cncf.txt"
),
"class":
"File"
}, {
"path":
os.path.join(
DATA_SETS['Proj_08390_G']['FACETS_DIR'],
"Sample3.rg.md.abra.printreads__Sample4.rg.md.abra.printreads_hisens.cncf.txt"
),
"class":
"File"
}],
"facets_hisens_seg_files": [{
"path":
os.path.join(
DATA_SETS['Proj_08390_G']['FACETS_DIR'],
"Sample2.rg.md.abra.printreads__Sample1.rg.md.abra.printreads_hisens.seg"
),
"class":
"File"
}, {
"path":
os.path.join(
DATA_SETS['Proj_08390_G']['FACETS_DIR'],
"Sample3.rg.md.abra.printreads__Sample4.rg.md.abra.printreads_hisens.seg"
),
"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 = {
'analysis_dir': {
'class':
'Directory',
'basename':
'analysis',
'location':
'file://' + os.path.join(output_dir, 'analysis'),
'path':
os.path.join(output_dir, 'analysis'),
'listing': [{
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.gene.cna.txt'),
'basename':
'Proj_08390_G.gene.cna.txt',
'class':
'File',
'checksum':
'sha1$ab17d587ad5ae0a87fd6c6d4dd2d5d1701208ce9',
'size':
173982,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.gene.cna.txt')
}, {
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.muts.maf'),
'basename':
'Proj_08390_G.muts.maf',
'class':
'File',
'checksum':
'sha1$d4352ee2b702877b84db2b632972ccad2441f3e0',
'size':
54458,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.muts.maf')
}, {
'location':
'file://' +
os.path.join(output_dir,
'analysis/Proj_08390_G.muts.share.maf'),
'basename':
'Proj_08390_G.muts.share.maf',
'class':
'File',
'checksum':
'sha1$086ce6517eae68e47160c8740c5f00d7c3454110',
'size':
10956,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.muts.share.maf')
}, {
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.seg.cna.txt'),
'basename':
'Proj_08390_G.seg.cna.txt',
'class':
'File',
'checksum':
'sha1$f6a77b280c047a7e2082e3a09e8138f861790d3a',
'size':
3191,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.seg.cna.txt')
}, {
'location':
'file://' + os.path.join(
output_dir, 'analysis/Proj_08390_G.svs.maf'),
'basename':
'Proj_08390_G.svs.maf',
'class':
'File',
'checksum':
'sha1$5c2a63fc01980550108e58079a8b689d53c97d8c',
'size':
35595,
'path':
os.path.join(output_dir,
'analysis/Proj_08390_G.svs.maf')
}]
}
}
self.maxDiff = None
self.assertDictEqual(output_json, expected_output)
comments, mutations = load_mutations(
os.path.join(output_dir, 'analysis', 'Proj_08390_G.muts.maf'))
self.assertEqual(len(mutations), 34)