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_two_mafs(self):
"""
Test that the workflow works correctly when run with two maf files
"""
self.skipTest("Fix jenkins error")
data_clinical_file = os.path.join(
DATA_SETS['Proj_08390_G']['INPUTS_DIR'],
"Proj_08390_G_sample_data_clinical.txt")
sample_summary_file = os.path.join(DATA_SETS['Proj_08390_G']['QC_DIR'],
"Proj_08390_G_SampleSummary.txt")
self.input = {
"project_id":
"Proj_08390_G",
"project_name":
"Proj_08390_G",
"project_short_name":
"Proj_08390_G",
"project_description":
"project",
"project_pi":
"Dr. Jones",
"request_pi":
"Dr. Franklin",
"is_impact":
True,
"argos_version_string":
"2.x",
"cancer_type":
"MEL",
"cancer_study_identifier":
'Proj_08390_G',
"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",
"cbio_meta_cna_segments_filename":
"Proj_08390_G_meta_cna_hg19_seg.txt",
"cbio_segment_data_filename":
"Proj_08390_G_data_cna_hg19.seg",
"helix_filter_version":
"20.06.1",
'IMPACT_gene_list': {
"path": IMPACT_FILE,
"class": "File"
},
"data_clinical_file": {
"path": data_clinical_file,
"class": "File"
},
"sample_summary_file": {
"path": sample_summary_file,
"class": "File"
},
"targets_list": {
"path": DATA_SETS['Proj_08390_G']["targets_list"],
"class": "File"
},
"known_fusions_file": {
"path": KNOWN_FUSIONS_FILE,
"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_txt_files": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample1.Sample2.svs.pass.vep.portal.txt"),
"class":
"File"
}, {
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample4.Sample3.svs.pass.vep.portal.txt"),
"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"
}]
}
output_json, output_dir = self.run_cwl()
self.maxDiff = None
expected_output = {
'analysis_dir':
ODir(name='analysis',
items=[
OFile(name='Proj_08390_G.gene.cna.txt',
size=173982,
hash='ab17d587ad5ae0a87fd6c6d4dd2d5d1701208ce9'),
OFile(name='Proj_08390_G.muts.maf',
size=54458,
hash='d4352ee2b702877b84db2b632972ccad2441f3e0'),
OFile(name='Proj_08390_G.muts.share.maf',
size=10956,
hash='086ce6517eae68e47160c8740c5f00d7c3454110'),
OFile(name='Proj_08390_G.seg.cna.txt',
size=3191,
hash='f6a77b280c047a7e2082e3a09e8138f861790d3a'),
OFile(name='Proj_08390_G.svs.maf',
size=35595,
hash='5c2a63fc01980550108e58079a8b689d53c97d8c')
],
dir=output_dir),
'portal_dir':
ODir(
name='portal',
items=[
OFile(name='meta_clinical_sample.txt',
size=140,
hash='4c567d81c3b17a76c324fd3e2f73793a6e804f65'),
OFile(name='data_clinical_patient.txt',
size=643,
hash='9417dcabddd6ab2cbe98167bccd9b9e4fa182562'),
OFile(name='data_clinical_sample.txt',
size=7592,
hash='2a0c59593fa7726743b2fe46db9d955dbc625453'),
OFile(name='meta_study.txt',
size=152,
hash='2b0a5fd1a97329adf7c3b1596c84cd6567059a95'),
OFile(name='meta_clinical_patient.txt',
size=142,
hash='9cdc9a7e44a230c012f48b0236bdcf0bbc7de67f'),
OFile(name='meta_CNA.txt',
size=270,
hash='a9bf16f6a0490b19e611e8814b85f7bf1d52417a'),
OFile(name='meta_fusions.txt',
size=227,
hash='77649e888bafc6a4ed61261d1c46d2f238e1c32b'),
OFile(name='meta_mutations_extended.txt',
size=253,
hash='fd04fcd0129b35bb8b8aaef57b2efa16b8f42e1d'),
OFile(name='Proj_08390_G_meta_cna_hg19_seg.txt',
size=200,
hash='59b54d3cd81acdd9fc21df1dc05a71cebfbfe11e'),
OFile(name='data_CNA.txt',
size=6784,
hash='09b4d944e50ea9d0e7567e04ce55b0f21d281255'),
OFile(name='data_CNA.ascna.txt',
size=8789,
hash='d93ffe83137d9a77e2420b40ab3a2e0a1a5ad069'),
OFile(name='data_mutations_extended.txt',
size=7539,
hash='43469aa0f9125d3dca6217ee02641638c3a92e24'),
OFile(name='Proj_08390_G_data_cna_hg19.seg',
size=3191,
hash='f6a77b280c047a7e2082e3a09e8138f861790d3a'),
OFile(name='data_fusions.txt',
size=99,
hash='c16f763b248813fcdde76f7486f1ddc4e9856038'),
ODir(
name='case_lists',
items=[
OFile(
name='cases_all.txt',
size=616,
hash='b9e43289cec5603b0886b5e8507c8d019387c125'
),
OFile(
name='cases_cnaseq.txt',
size=696,
hash='b87e2da8dce0fddbadec348efe2986519b2a794b'
),
OFile(
name='cases_cna.txt',
size=628,
hash='053481a8299e9430117f8e45e081aa7ec21033a6'
),
OFile(
name='cases_sequenced.txt',
size=641,
hash='ef9f5aef03c2527bf576470168660557ca1c7cc9'
)
])
],
dir=output_dir)
}
self.assertCWLDictEqual(output_json, expected_output)
comments, mutations = load_mutations(
os.path.join(output_dir, 'analysis', 'Proj_08390_G.muts.maf'))
self.assertEqual(len(mutations), 34)
comments, mutations = load_mutations(
os.path.join(output_dir, 'portal', 'data_mutations_extended.txt'))
self.assertEqual(len(mutations), 27)
# TODO: fix the Facets column headers so that this passes
# load the data_CNA.txt file
path = os.path.join(output_dir, 'portal/data_CNA.txt'
) # renamed from the data_CNA.scna.txt file ...
with open(path) as f:
header = next(f)
header_parts = header.split()
expected_header_parts = [
'Hugo_Symbol', 's_C_VJ7F47_P001_d', 's_C_X50T9Y_P001_d'
]
self.assertEqual(header_parts, expected_header_parts)
path = os.path.join(output_dir, 'portal/data_CNA.ascna.txt')
with open(path) as f:
header = next(f)
header_parts = header.split()
expected_header_parts = [
'Hugo_Symbol', 's_C_VJ7F47_P001_d', 's_C_X50T9Y_P001_d'
]
self.assertEqual(header_parts, expected_header_parts)
def test_run_worflow_one_maf(self):
"""
Test that the workflow works correctly when run with a single maf
"""
self.skipTest("Fix jenkins error")
data_clinical_file = os.path.join(
DATA_SETS['Proj_08390_G']['INPUTS_DIR'],
"Proj_08390_G_sample_data_clinical.txt")
sample_summary_file = os.path.join(DATA_SETS['Proj_08390_G']['QC_DIR'],
"Proj_08390_G_SampleSummary.txt")
self.input = {
"project_id":
"Proj_08390_G",
"project_name":
"Proj_08390_G",
"project_short_name":
"Proj_08390_G",
"project_description":
"project",
"project_pi":
"Dr. Jones",
"request_pi":
"Dr. Franklin",
"is_impact":
True,
"argos_version_string":
"2.x",
"cancer_type":
"MEL",
"cancer_study_identifier":
'Proj_08390_G',
"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",
"cbio_meta_cna_segments_filename":
"Proj_08390_G_meta_cna_hg19_seg.txt",
"cbio_segment_data_filename":
"Proj_08390_G_data_cna_hg19.seg",
"helix_filter_version":
"20.06.1",
'IMPACT_gene_list': {
"path": IMPACT_FILE,
"class": "File"
},
"data_clinical_file": {
"path": data_clinical_file,
"class": "File"
},
"sample_summary_file": {
"path": sample_summary_file,
"class": "File"
},
"targets_list": {
"path": DATA_SETS['Proj_08390_G']["targets_list"],
"class": "File"
},
"known_fusions_file": {
"path": KNOWN_FUSIONS_FILE,
"class": "File"
},
"mutation_maf_files": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample1.Sample2.muts.maf"),
"class":
"File"
}],
"mutation_svs_txt_files": [{
"path":
os.path.join(DATA_SETS['Proj_08390_G']['MAF_DIR'],
"Sample1.Sample2.svs.pass.vep.portal.txt"),
"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"
}],
}
output_json, output_dir = self.run_cwl()
expected_output = {
'analysis_dir':
ODir(name='analysis',
items=[
OFile(name='Proj_08390_G.gene.cna.txt',
size=87905,
hash='7cc89d24556de93b9a409812317581e67e5df494'),
OFile(name='Proj_08390_G.muts.maf',
size=33243,
hash='2c8904927a917d6e935ef207582d995680574d16'),
OFile(name='Proj_08390_G.muts.share.maf',
size=7462,
hash='b5af4e0fcd89fecabf8095aa3d7690e5edb8dca1'),
OFile(name='Proj_08390_G.seg.cna.txt',
size=1632,
hash='f0ebb82c34b6530447fa1e70b6dedcc039840d61'),
OFile(name='Proj_08390_G.svs.maf',
size=23603,
hash='df420706bb5b772a79317843c0a01a3c88a9571d')
],
dir=output_dir),
'portal_dir':
ODir(
name='portal',
items=[
OFile(name='meta_clinical_sample.txt',
size=140,
hash='4c567d81c3b17a76c324fd3e2f73793a6e804f65'),
OFile(name='data_clinical_patient.txt',
size=643,
hash='9417dcabddd6ab2cbe98167bccd9b9e4fa182562'),
OFile(name='data_clinical_sample.txt',
size=7592,
hash='2a0c59593fa7726743b2fe46db9d955dbc625453'),
OFile(name='meta_study.txt',
size=152,
hash='2b0a5fd1a97329adf7c3b1596c84cd6567059a95'),
OFile(name='meta_clinical_patient.txt',
size=142,
hash='9cdc9a7e44a230c012f48b0236bdcf0bbc7de67f'),
OFile(name='meta_CNA.txt',
size=270,
hash='a9bf16f6a0490b19e611e8814b85f7bf1d52417a'),
OFile(name='meta_fusions.txt',
size=227,
hash='77649e888bafc6a4ed61261d1c46d2f238e1c32b'),
OFile(name='meta_mutations_extended.txt',
size=253,
hash='fd04fcd0129b35bb8b8aaef57b2efa16b8f42e1d'),
OFile(name='Proj_08390_G_meta_cna_hg19_seg.txt',
size=200,
hash='59b54d3cd81acdd9fc21df1dc05a71cebfbfe11e'),
OFile(name='data_CNA.txt',
size=5365,
hash='931d82412733d7f93dd4117cd955f35e5dcbacc1'),
OFile(name='data_CNA.ascna.txt',
size=6164,
hash='452d5ddef12a44693d5a98a05f5d300801734cfe'),
OFile(name='data_mutations_extended.txt',
size=5106,
hash='e713516cf04750a3e3f1ef932b1c7202d4b75bf2'),
OFile(name='Proj_08390_G_data_cna_hg19.seg',
size=1632,
hash='f0ebb82c34b6530447fa1e70b6dedcc039840d61'),
OFile(name='data_fusions.txt',
size=99,
hash='c16f763b248813fcdde76f7486f1ddc4e9856038'),
ODir(
name='case_lists',
items=[
OFile(
name='cases_all.txt',
size=616,
hash='b9e43289cec5603b0886b5e8507c8d019387c125'
),
OFile(
name='cases_cnaseq.txt',
size=696,
hash='b87e2da8dce0fddbadec348efe2986519b2a794b'
),
OFile(
name='cases_cna.txt',
size=628,
hash='053481a8299e9430117f8e45e081aa7ec21033a6'
),
OFile(
name='cases_sequenced.txt',
size=641,
hash='ef9f5aef03c2527bf576470168660557ca1c7cc9'
)
])
],
dir=output_dir)
}
self.assertCWLDictEqual(output_json, expected_output)
comments, mutations = load_mutations(
os.path.join(output_dir, 'analysis', 'Proj_08390_G.muts.maf'))
self.assertEqual(len(mutations), 22)
comments, mutations = load_mutations(
os.path.join(output_dir, 'portal', 'data_mutations_extended.txt'))
self.assertEqual(len(mutations), 17)
# load the data_CNA.txt file
path = os.path.join(output_dir, 'portal/data_CNA.txt'
) # renamed from the data_CNA.scna.txt file ...
with open(path) as f:
header = next(f)
header_parts = header.split()
expected_header_parts = ['Hugo_Symbol', 's_C_VJ7F47_P001_d']
self.assertEqual(header_parts, expected_header_parts)
path = os.path.join(output_dir, 'portal/data_CNA.ascna.txt')
with open(path) as f:
header = next(f)
header_parts = header.split()
expected_header_parts = ['Hugo_Symbol', 's_C_VJ7F47_P001_d']
self.assertEqual(header_parts, expected_header_parts)
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)