def compute_regression(input_cancer_type):
if input_cancer_type == "CCRCC":
cancer = cptac.Ccrcc()
elif input_cancer_type == "Endometrial":
cancer = cptac.Endometrial()
elif input_cancer_type == "LUAD":
cancer = cptac.Luad()
elif input_cancer_type == "HNSCC":
cancer = cptac.Hnscc()
elif input_cancer_type == "LSCC":
cancer = cptac.Lscc()
elif input_cancer_type == "PDAC":
cancer = cptac.Pdac()
df = dc.get_prot_trans_df(cancer)
results = df.groupby('Gene').apply(regression)
reg_df = pd.DataFrame(list(results))
reg_df.index = results.index
reg_df.reset_index(inplace=True)
reg_df = reg_df.dropna()
reg_df['interaction_FDR'] = ssm.fdrcorrection(
reg_df['interaction_pval'])[1]
reg_df['condition_FDR'] = ssm.fdrcorrection(reg_df['condition_pval'])[1]
reg_df['intercept_FDR'] = ssm.fdrcorrection(reg_df['intercept_pval'])[1]
reg_df['Cancer'] = [input_cancer_type] * len(reg_df)
file_name = input_cancer_type + '_regressions.csv'
reg_df.to_csv(file_name, index=False)
def load_cancers(include_pdac=False):
ccrcc = cptac.Ccrcc()
en = cptac.Endometrial()
luad = cptac.Luad()
hnscc = cptac.Hnscc()
lscc = cptac.Lscc()
cancers = [ccrcc, en, luad, hnscc, lscc]
cancer_names = ['CCRCC', 'Endometrial', 'LUAD', 'HNSCC', 'LSCC']
if include_pdac:
pdac = cptac.Pdac()
cancers.append(pdac)
cancer_names.append('PDAC')
return cancers, cancer_names
input_cancer_type = sys.argv[1]
mutated_gene = sys.argv[2]
input_permutation_number = int(sys.argv[3])
cutoff = 15
if input_cancer_type == "CCRCC":
cancer = cptac.Ccrcc()
elif input_cancer_type == "Endometrial":
cancer = cptac.Endometrial()
cutoff = 10
elif input_cancer_type == "LUAD":
cancer = cptac.Luad()
elif input_cancer_type == "HNSCC":
cancer = cptac.Hnscc()
elif input_cancer_type == "LSCC":
cancer = cptac.Lscc()
elif input_cancer_type == "PDAC":
cancer = cptac.Pdac()
mutation_df = cancer.get_somatic_mutation()
mutation_df = mutation_df[mutation_df.Gene == mutated_gene]
mutation_df = mutation_df[mutation_df.Mutation != 'Silent']
mutation_df = mutation_df[mutation_df.Mutation != 'RNA']
mutation_df = mutation_df[mutation_df.Mutation != 'synonymous SNV']
mutation_df.reset_index(inplace=True)
prot_trans_df = dc.get_prot_trans_df(cancer)
prot_trans_df = prot_trans_df[prot_trans_df.Tissue == 'tumor']
prot_trans_df['Mutation'] = prot_trans_df.Patient_ID.isin(
mutation_df.Patient_ID)
test_coord_1 = ((index_1, 1), (index_2, 1), (index_3, 1)) # C3N-01515
test_vals_1 = ('No_Mutation', 'No_Mutation', 'No_Mutation')
# Test Del
test_coord_2 = ((index_4, 1), (index_5, 1), (index_6, 1))
test_vals_2 = ('Deletion', 'Deletion', 'Deletion')
# Test Amp
test_coord_3 = ((index_7, 1), (index_8, 1), (index_9, 1))
test_vals_3 = ('Amplification', 'Amplification', 'Amplification')
test_coord_vals = [(test_coord_1, test_vals_1),
(test_coord_2, test_vals_2),
(test_coord_3, test_vals_3)]
for coord, vals in test_coord_vals:
PASS = check_getter(df, dimensions, headers, coord, vals)
print_test_result(PASS)
k = cptac.Ccrcc()
g = cptac.Gbm()
h = cptac.Hnscc()
print("\nRunning tests:\n")
test_genotype_ccrcc_KRAS()
test_genotype_gbm_KRAS()
test_genotype_hnscc_KRAS()
print("Version:", cptac.version())
import warnings
warnings.filterwarnings("ignore")
input_cancer_type = sys.argv[1]
if input_cancer_type == "ccrcc":
ccrcc = cptac.Ccrcc()
cancer_list = [ccrcc]
elif input_cancer_type == "en":
en = cptac.Endometrial()
cancer_list = [en]
elif input_cancer_type == "luad":
luad = cptac.Luad()
cancer_list = [luad]
elif input_cancer_type == "hnscc":
hnscc = cptac.Hnscc()
cancer_list = [hnscc]
elif input_cancer_type == "lscc":
lscc = cptac.Lscc()
cancer_list = [lscc]
# brca = cptac.Brca()
# ccrcc = cptac.Ccrcc()
# colon = cptac.Colon()
# en = cptac.Endometrial()
# gbm = cptac.Gbm()
# luad = cptac.Luad()
# ovarian = cptac.Ovarian()
# hnscc = cptac.Hnscc()
# lscc = cptac.Lscc()
# type_dict = {brca:"brca",ccrcc:"ccrcc",colon:"colon",en:"endometrial",gbm:"gbm",luad:"luad",