def test_get_frequently_mutated_co_default_cutoff():
co = cptac.Colon()
print('Running get_frequently_mutated...')
df = ut.get_frequently_mutated(co)
dimensions = (612, 4)
headers = ['Gene', 'Unique_Samples_Mut', 'Missense_Mut', 'Truncation_Mut']
# test gene names
test_coord_names = ((90, 0), (284, 0), (499, 0))
test_vals_names = ('CASP5', 'KRAS', 'SPINK5')
total_tumors = 97
# test when there are no missense type mutatations
test_coord_CASP5 = ((90, 1), (90, 2), (90, 3))
test_vals_CASP5 = (19 / total_tumors, 0 / total_tumors, 19 / total_tumors)
# test when there are no truncation type mutatations
test_coord_KRAS = ((284, 1), (284, 2), (284, 3))
test_vals_KRAS = (35 / total_tumors, 35 / total_tumors, 0 / total_tumors)
# test when missense and trucation don't add up to equal the fraction mutated
#(miss and trunc in same sample)
test_coord_ANK2 = ((34, 1), (34, 2), (34, 3))
test_vals_ANK2 = (15 / total_tumors, 13 / total_tumors, 4 / total_tumors)
# test when miss and trunc count are the same
test_coord_ATM = ((56, 1), (56, 2), (56, 3))
test_vals_ATM = (10 / total_tumors, 7 / total_tumors, 7 / total_tumors)
# test close to the cutoff
test_coord_SPINK5 = ((499, 1), (499, 2), (499, 3))
test_vals_SPINK5 = (10 / total_tumors, 5 / total_tumors, 7 / total_tumors)
# common test
test_coord_TP53 = ((554, 1), (554, 2), (554, 3))
test_vals_TP53 = (56 / total_tumors, 38 / total_tumors, 21 / total_tumors)
test_coord_vals = [(test_coord_names, test_vals_names),
(test_coord_CASP5, test_vals_CASP5),
(test_coord_KRAS, test_vals_KRAS),
(test_coord_ANK2, test_vals_ANK2),
(test_coord_ATM, test_vals_ATM),
(test_coord_SPINK5, test_vals_SPINK5),
(test_coord_TP53, test_vals_TP53)]
for coord, vals in test_coord_vals:
PASS = check_getter(df, dimensions, headers, coord, vals)
print_test_result(PASS)
def test_get_frequently_mutated_co_15_cutoff():
co = cptac.Colon()
print('Running get_frequently_mutated...')
df = ut.get_frequently_mutated(co, 0.15)
dimensions = (138, 4)
headers = ['Gene', 'Unique_Samples_Mut', 'Missense_Mut', 'Truncation_Mut']
# test gene names
test_coord_names = ((15, 0), (66, 0), (102, 0))
test_vals_names = ('CASP5', 'KRAS', 'RYR2')
total_tumors = 97
# test no missense type mutatations
test_coord_CASP5 = ((15, 1), (15, 2), (15, 3))
test_vals_CASP5 = (19 / total_tumors, 0 / total_tumors, 19 / total_tumors)
# test no truncation type mutatations
test_coord_KRAS = ((66, 1), (66, 2), (66, 3))
test_vals_KRAS = (35 / total_tumors, 35 / total_tumors, 0 / total_tumors)
# test missense and truncation equal fraction mutated
test_coord_PIK3CA = ((92, 1), (92, 2), (92, 3))
test_vals_PIK3CA = (24 / total_tumors, 23 / total_tumors, 1 / total_tumors)
# test missense and trucation don't equal unique_samples_mutated (miss and trunc in same sample)
test_coord_RYR2 = ((102, 1), (102, 2), (102, 3))
test_vals_RYR2 = (21 / total_tumors, 19 / total_tumors, 7 / total_tumors)
# test close to the cutoff
test_coord_ANK2 = ((6, 1), (6, 2), (6, 3))
test_vals_ANK2 = (15 / total_tumors, 13 / total_tumors, 4 / total_tumors)
# common test
test_coord_TP53 = ((123, 1), (123, 2), (123, 3))
test_vals_TP53 = (56 / total_tumors, 38 / total_tumors, 21 / total_tumors)
test_coord_vals = [(test_coord_names, test_vals_names),
(test_coord_CASP5, test_vals_CASP5),
(test_coord_KRAS, test_vals_KRAS),
(test_coord_PIK3CA, test_vals_PIK3CA),
(test_coord_RYR2, test_vals_RYR2),
(test_coord_ANK2, test_vals_ANK2),
(test_coord_TP53, test_vals_TP53)]
for coord, vals in test_coord_vals:
PASS = check_getter(df, dimensions, headers, coord, vals)
print_test_result(PASS)
def test_get_frequently_mutated_en_default_cutoff():
en = cptac.Endometrial()
print('Running get_frequently_mutated...')
df = ut.get_frequently_mutated(en)
name = "frequently_mutated"
dimensions = (232, 4)
headers = ['Gene', 'Unique_Samples_Mut', 'Missense_Mut', 'Truncation_Mut']
# test gene names
test_coord_names = ((53, 0), (32, 0), (227, 0))
test_vals_names = ('CTCF', 'CCDC168', 'ZNF536')
total_tumors = 95
# test missense and trucation don't equal the unique_sample_mutated
#(miss and trunc in same sample)
test_coord_CTCF = ((53, 1), (53, 2), (53, 3))
test_vals_CTCF = (27 / total_tumors, 9 / total_tumors, 23 / total_tumors)
# testmissense and trucation values are equal
test_coord_CCDC168 = ((32, 1), (32, 2), (32, 3))
test_vals_CCDC168 = (16 / total_tumors, 11 / total_tumors,
11 / total_tumors)
# test no truncation type mutatations
test_coord_ZNF536 = ((227, 1), (227, 2), (227, 3))
test_vals_ZNF536 = (12 / total_tumors, 12 / total_tumors, 0 / total_tumors)
# test close to cutoff
test_coord_DICER1 = ((61, 1), (61, 2), (61, 3))
test_vals_DICER1 = (10 / total_tumors, 10 / total_tumors, 1 / total_tumors)
# common test
test_coord_TP53 = ((205, 1), (205, 2), (205, 3))
test_vals_TP53 = (21 / total_tumors, 15 / total_tumors, 7 / total_tumors)
test_coord_vals = [(test_coord_names, test_vals_names),
(test_coord_CTCF, test_vals_CTCF),
(test_coord_CCDC168, test_vals_CCDC168),
(test_coord_ZNF536, test_vals_ZNF536),
(test_coord_DICER1, test_vals_DICER1),
(test_coord_TP53, test_vals_TP53)]
for coord, vals in test_coord_vals:
PASS = check_getter(df, dimensions, headers, coord, vals)
print_test_result(PASS)
def test_get_frequently_mutated_ov_05_cutoff():
ov = cptac.Ovarian()
print('Running get_frequently_mutated...')
df = ut.get_frequently_mutated(ov, 0.05)
dimensions = (142, 4)
headers = ['Gene', 'Unique_Samples_Mut', 'Missense_Mut', 'Truncation_Mut']
# test genes names
test_coord_names = ((133, 0), (127, 0), (141, 0))
test_vals_names = ('WDFY4', 'TP53', 'ZNF865')
total_tumors = 83
#test missense and trucation not equal to unique_samples_mutated
#(miss and trunc in same sample)
test_coord_WDFY4 = ((133, 1), (133, 2), (133, 3))
test_vals_WDFY4 = (10 / total_tumors, 8 / total_tumors, 3 / total_tumors)
# test miss and trunc almost equal
test_coord_CDK12 = ((11, 1), (11, 2), (11, 3))
test_vals_CDK12 = (6 / total_tumors, 4 / total_tumors, 3 / total_tumors)
# test no truncation mutations
test_coord_ZNF865 = ((141, 1), (141, 2), (141, 3))
test_vals_ZNF865 = (5 / total_tumors, 5 / total_tumors, 0 / total_tumors)
# test close to cutoff
test_coord_SYNE1 = ((122, 1), (122, 2), (122, 3))
test_vals_SYNE1 = (5 / total_tumors, 5 / total_tumors, 1 / total_tumors)
# common test and highest count
test_coord_TP53 = ((127, 1), (127, 2), (127, 3))
test_vals_TP53 = (77 / total_tumors, 50 / total_tumors, 27 / total_tumors)
#CHECK silent mut not counted
test_coord_vals = [(test_coord_names, test_vals_names),
(test_coord_WDFY4, test_vals_WDFY4),
(test_coord_CDK12, test_vals_CDK12),
(test_coord_ZNF865, test_vals_ZNF865),
(test_coord_SYNE1, test_vals_SYNE1),
(test_coord_TP53, test_vals_TP53)]
for coord, vals in test_coord_vals:
PASS = check_getter(df, dimensions, headers, coord, vals)
print_test_result(PASS)
def test_get_frequently_mutated_en_cutoff_20_cutoff():
en = cptac.Endometrial()
print('Running get_frequently_mutated...')
df = ut.get_frequently_mutated(en, cutoff=0.2)
dimensions = (10, 4)
headers = ['Gene', 'Unique_Samples_Mut', 'Missense_Mut', 'Truncation_Mut']
# test gene names
test_coord_names = ((0, 0), (2, 0), (8, 0))
test_vals_names = ('ARID1A', 'CTNNB1', 'TP53')
total_tumors = 95
# test missense and trucation don't equal the unique_samples_mutated
#(miss and trunc in same sample and counted in each category)
test_coord_ARID1A = ((0, 1), (0, 2), (0, 3))
test_vals_ARID1A = (43 / total_tumors, 13 / total_tumors,
38 / total_tumors)
# test no truncation type mutatations
test_coord_CTNNB1 = ((2, 1), (2, 2), (2, 3))
test_vals_CTNNB1 = (29 / total_tumors, 29 / total_tumors, 0 / total_tumors)
# test close to the cutoff
test_coord_ZFHX3 = ((9, 1), (9, 2), (9, 3))
test_vals_ZFHX3 = (21 / total_tumors, 8 / total_tumors, 16 / total_tumors)
# test miss and trunc almost equal
test_coord_KMT2B = ((3, 1), (3, 2), (3, 3))
test_vals_KMT2B = (23 / total_tumors, 11 / total_tumors, 12 / total_tumors)
# common test
test_coord_TP53 = ((8, 1), (8, 2), (8, 3))
test_vals_TP53 = (21 / total_tumors, 15 / total_tumors, 7 / total_tumors)
test_coord_vals = [(test_coord_names, test_vals_names),
(test_coord_ARID1A, test_vals_ARID1A),
(test_coord_CTNNB1, test_vals_CTNNB1),
(test_coord_ZFHX3, test_vals_ZFHX3),
(test_coord_KMT2B, test_vals_KMT2B),
(test_coord_TP53, test_vals_TP53)]
for coord, vals in test_coord_vals:
PASS = check_getter(df, dimensions, headers, coord, vals)
print_test_result(PASS)
def test_get_frequently_mutated_renal_01_cutoff():
rc = cptac.Ccrcc()
print('Running get_frequently_mutated...')
df = ut.get_frequently_mutated(rc, cutoff=0.01)
dimensions = (1106, 4)
headers = ['Gene', 'Unique_Samples_Mut', 'Missense_Mut', 'Truncation_Mut']
# test genes names
test_coord_names = ((11, 0), (992, 0), (1080, 0))
test_vals_names = ('ABCC3', 'TTN', 'ZNF532')
total_tumors = 110
# test no missense
test_coord_ABCC3 = ((11, 1), (11, 2), (11, 3))
test_vals_ABCC3 = (2 / total_tumors, 0 / total_tumors, 2 / total_tumors)
# test no truncation and close to cutoff
test_coord_ZNF532 = ((1080, 1), (1080, 2), (1080, 3))
test_vals_ZNF532 = (2 / total_tumors, 2 / total_tumors, 0 / total_tumors)
# test miss and trunc equal to unique_samples_mutated
test_coord_NAV3 = ((611, 1), (611, 2), (611, 3))
test_vals_NAV3 = (7 / total_tumors, 5 / total_tumors, 2 / total_tumors)
# check that silent mutations are not counted (TTN has many silent mutations)
# and missense and trucation not equal to unique_samples_mutated
test_coord_TTN = ((992, 1), (992, 2), (992, 3))
test_vals_TTN = (13 / total_tumors, 10 / total_tumors, 4 / total_tumors)
# common test and highest count
test_coord_VHL = ((1019, 1), (1019, 2), (1019, 3))
test_vals_VHL = (82 / total_tumors, 33 / total_tumors, 49 / total_tumors)
test_coord_vals = [(test_coord_names, test_vals_names),
(test_coord_ABCC3, test_vals_ABCC3),
(test_coord_ZNF532, test_vals_ZNF532),
(test_coord_NAV3, test_vals_NAV3),
(test_coord_TTN, test_vals_TTN),
(test_coord_VHL, test_vals_VHL)]
for coord, val in test_coord_vals:
PASS = check_getter(df, dimensions, headers, coord, val)
print_test_result(PASS)
def test_get_frequently_mutated_renal_default_cutoff():
rc = cptac.Ccrcc()
print('Running get_frequently_mutated...')
df = ut.get_frequently_mutated(rc)
dimensions = (6, 4)
headers = ['Gene', 'Unique_Samples_Mut', 'Missense_Mut', 'Truncation_Mut']
# test genes names
test_coord_names = ((0, 0), (2, 0), (4, 0))
test_vals_names = ('BAP1', 'PBRM1', 'TTN')
total_tumors = 110
# test miss and trunc equal to unique_samples_mutated
test_coord_BAP1 = ((0, 1), (0, 2), (0, 3))
test_vals_BAP1 = (17 / total_tumors, 7 / total_tumors, 10 / total_tumors)
# test high truncation, low missense count
test_coord_PBRM1 = ((2, 1), (2, 2), (2, 3))
test_vals_PBRM1 = (44 / total_tumors, 8 / total_tumors, 37 / total_tumors)
# check that silent mutations are not counted (TTN has many silent mutations)
# and missense and trucation not equal to unique_samples_mutated
test_coord_TTN = ((4, 1), (4, 2), (4, 3))
test_vals_TTN = (13 / total_tumors, 10 / total_tumors, 4 / total_tumors)
# test close to cutoff
test_coord_SETD2 = ((3, 1), (3, 2), (3, 3))
test_vals_SETD2 = (15 / total_tumors, 2 / total_tumors, 13 / total_tumors)
# common test and highest count
test_coord_VHL = ((5, 1), (5, 2), (5, 3))
test_vals_VHL = (82 / total_tumors, 33 / total_tumors, 49 / total_tumors)
test_coord_vals = [(test_coord_names, test_vals_names),
(test_coord_BAP1, test_vals_BAP1),
(test_coord_PBRM1, test_vals_PBRM1),
(test_coord_TTN, test_vals_TTN),
(test_coord_SETD2, test_vals_SETD2),
(test_coord_VHL, test_vals_VHL)]
for coord, vals in test_coord_vals:
PASS = check_getter(df, dimensions, headers, coord, vals)
print_test_result(PASS)
def test_get_frequently_mutated_ov_default_cutoff():
ov = cptac.Ovarian()
print('Running get_frequently_mutated...')
df = ut.get_frequently_mutated(ov)
dimensions = (16, 4)
headers = ['Gene', 'Unique_Samples_Mut', 'Missense_Mut', 'Truncation_Mut']
# test genes names
test_coord_names = ((15, 0), (13, 0), (2, 0))
test_vals_names = ('WDFY4', 'TP53', 'MT-CO1')
total_tumors = 83
#test missense and trucation not equal to unique_samples_mutated
#(miss and trunc in same sample)
test_coord_WDFY4 = ((15, 1), (15, 2), (15, 3))
test_vals_WDFY4 = (10 / total_tumors, 8 / total_tumors, 3 / total_tumors)
# test miss and trunc equal to unique_samples_mutated
test_coord_MUC4 = ((8, 1), (8, 2), (8, 3))
test_vals_MUC4 = (27 / total_tumors, 26 / total_tumors, 1 / total_tumors)
# test no truncation mutations
test_coord_MTCO1 = ((2, 1), (2, 2), (2, 3))
test_vals_MTCO1 = (10 / total_tumors, 10 / total_tumors, 0 / total_tumors)
# test close to cutoff
test_coord_FSIP2 = ((1, 1), (1, 2), (1, 3))
test_vals_FSIP2 = (9 / total_tumors, 8 / total_tumors, 2 / total_tumors)
# common test and highest count
test_coord_TP53 = ((13, 1), (13, 2), (13, 3))
test_vals_TP53 = (77 / total_tumors, 50 / total_tumors, 27 / total_tumors)
test_coord_vals = [(test_coord_names, test_vals_names),
(test_coord_WDFY4, test_vals_WDFY4),
(test_coord_MUC4, test_vals_MUC4),
(test_coord_MTCO1, test_vals_MTCO1),
(test_coord_FSIP2, test_vals_FSIP2),
(test_coord_TP53, test_vals_TP53)]
for coord, vals in test_coord_vals:
PASS = check_getter(df, dimensions, headers, coord, vals)
print_test_result(PASS)
