def main(args):
"""
Main entrypoint for merge_star_gene_counts.
"""
logger = get_logger("merge_star_junction_counts")
logger.info("Merging/Formatting {0} STAR junction counts files.".format(
len(args.input)))
process_files(args, logger)
def main():
"""Main entry point for CLI"""
logger = get_logger("gdc-rnaseq-tools")
args = load_args()
logger.info("Loading tool {0}".format(args.choice))
tool = None
if args.choice == "merge_star_gene_counts":
tool = merge_star_gene_counts
elif args.choice == "merge_star_junctions":
tool = merge_star_junctions
tool.main(args)
logger.info("Finished!")
class TestAugmentStarCounts(unittest.TestCase):
df1_raw = pd.DataFrame({
'gene_id': [
'ENSG00000000001.0',
'ENSG00000000002.0',
'ENSG00000000003.0',
'ENSG00000000004.0',
'ENSG00000000005.0',
'ENSG00000000006.0',
'ENSG00000000007.0',
],
'total_exon_length': [1000, 1000, 1000, 1000, 1000, 1000, 1000],
'gene_name': ['ONE', 'TWO', 'THREE', 'FOUR', 'FIVE', 'SIX', 'SEVEN'],
'gene_type': [
'protein_coding',
'protein_coding',
'protein_coding',
'protein_coding',
'protein_coding',
'protein_coding',
'protein_coding',
],
'chromosome':
['chrX', 'chr1', 'chr22', 'chr3', 'chr5', 'chrM', 'chr10'],
'unstranded': [1500, 0, 1500, 6000, 7500, 6000, 7500],
'stranded_first': [10, 10, 10, 10, 10, 10, 10],
'stranded_second': [10, 10, 10, 10, 10, 10, 10],
})
df1_tpm = pd.Series(
[50000.0, 0.0, 50000.0, 200000.0, 250000.0, 200000.0, 250000.0])
df1_fpkm = pd.Series(
[50000.0, 0.0, 50000.0, 200000.0, 250000.0, 200000.0, 250000.0])
df1_fpkm_uq = pd.Series(
[40000.0, 0.0, 40000.0, 160000.0, 200000.0, 160000.0, 200000.0])
ts1_counts_file = os.path.join(os.path.dirname(__file__),
"etc/test_set_1.counts.tsv.gz")
ts1_gene_info_file = os.path.join(os.path.dirname(__file__),
"etc/test_set_1.gene_info.tsv.gz")
ts1_annotated_file = os.path.join(os.path.dirname(__file__),
"etc/test_set_1.annotated.tsv.gz")
ts1_final_file = os.path.join(os.path.dirname(__file__),
"etc/test_set_1.final.tsv.gz")
simple_file = os.path.join(os.path.dirname(__file__),
"etc/simple_counts.tsv.gz")
to_remove = []
logger = get_logger("augment_counts_table.testing")
def test_calc_fpkm(self):
"""
Tests the `calc_fpkm` function
"""
fpkm = calc_fpkm(
expression=self.df1_raw.unstranded,
feature_effective_length=self.df1_raw.total_exon_length,
gene_type=self.df1_raw.gene_type,
)
self.assertTrue((fpkm == self.df1_fpkm).all())
def test_calc_fpkm_uq(self):
"""
Tests the `calc_fpkm_uq` function
"""
fpkm_uq = calc_fpkm_uq(
expression=self.df1_raw.unstranded,
feature_effective_length=self.df1_raw.total_exon_length,
gene_type=self.df1_raw.gene_type,
chromosome=self.df1_raw.chromosome,
)
self.assertTrue((fpkm_uq == self.df1_fpkm_uq).all())
def test_calc_tpm(self):
"""
Tests the `calc_tpm` function
"""
tpm = calc_tpm(
expression=self.df1_raw.unstranded,
feature_effective_length=self.df1_raw.total_exon_length,
)
# print(tpm)
# print(self.df1_tpm)
self.assertTrue((tpm == self.df1_tpm).all())
def test_load_table(self):
"""
Tests the `load_tables` function
"""
df1 = load_table(self.simple_file)
pd.testing.assert_frame_equal(df1, self.df1_raw)
def test_validate_table_good(self):
"""
Tests the `validate_tables` function
"""
validate_table(self.df1_raw, self.df1_raw.columns.tolist())
def test_validate_table_bad(self):
"""
Tests the `validate_tables` function with incorrect column names
"""
self.assertRaises(
DataFormatError,
validate_table,
df=self.df1_raw,
expected_columns=['not', 'in', 'the', 'table'],
)
def test_merge(self):
"""
Tests the `merge_tables` function
"""
df1 = pd.DataFrame({
'id': [1, 2, 3, 4],
'A': ['one', 'two', 'three', 'four']
})
df2 = pd.DataFrame({
'id': [2, 3, 4, 5],
'B': ['owt', 'eerht', 'ruof', 'evif']
})
res = pd.DataFrame({
'id': [2, 3, 4],
'A': ['two', 'three', 'four'],
'B': ['owt', 'eerht', 'ruof'],
})
pd.testing.assert_frame_equal(merge_tables(df1, df2, on='id'), res)
def test_get_extras(self):
"""
Tests the `get_extras` function
"""
test_answer = self.df1_raw.iloc[0:4].copy()
res = get_extras(self.df1_raw)
pd.testing.assert_frame_equal(res, test_answer)
def test_save_result(self):
"""
Test the `save_results` function
"""
outfile = 'save_results_output.tsv'
self.to_remove.append(outfile)
testdf = pd.DataFrame({
'id': [1, 2, 3, 4],
'A': ['one', 'two', 'three', 'four']
})
save_result(df=testdf, outfile=outfile, gencode_version=36)
with open(outfile, 'rt') as result:
res_lines = result.read()
expected = ('# gene-model: GENCODE v36\n'
'id\tA\n'
'1\tone\n'
'2\ttwo\n'
'3\tthree\n'
'4\tfour\n')
self.assertEqual(expected, res_lines)
def test_augment(self):
"""
Tests `augment` function
"""
outfile = 'augment_output.tsv'
self.to_remove.append(outfile)
augment(
counts_file=self.ts1_counts_file,
gene_info_file=self.ts1_gene_info_file,
outfile=outfile,
gencode_version=36,
logger=self.logger,
)
def test_full_run(self):
"""
Full end-to-end test
"""
outfile = 'main_output.tsv'
self.to_remove.append(outfile)
args = FakeArgs()
args.input = self.ts1_counts_file
args.gene_info = self.ts1_gene_info_file
args.output = outfile
args.gencode_version = 36
self.to_remove.append(args.output)
main(args)
result = pd.read_table(args.output, comment='#')
expected = pd.read_table(self.ts1_final_file, comment='#')
pd.testing.assert_frame_equal(result, expected)
def setUp(self):
pass
def tearDown(self):
for fil in self.to_remove:
if os.path.exists(fil):
os.remove(fil)