def main():
args = parse_args()
cell_lines = CCLE_Info.read_cell_lines(args.cell_lines)
rnaseq_raw = pd.read_csv(args.input,
sep='\t',
header=0,
skiprows=range(0, 2))
truncated = restrict_to_cell_lines(rnaseq_raw, cell_lines)
truncated.to_csv(args.output, sep='\t')
def main():
args = parse_args()
df = pd.read_csv(args.input, header=0, sep='\t')
ccle = CCLE_Info(args.ccle_metadata)
cell_lines = CCLE_Info.read_cell_lines(args.cell_lines)
nonsense_df = extract_mutations_of_type(['Nonsense_Mutation'], df, ccle, cell_lines)
missense_df = extract_mutations_of_type(['Missense_Mutation'], df, ccle, cell_lines)
nm_df = extract_mutations_of_type(['Missense_Mutation', 'Nonsense_Mutation'], df, ccle, cell_lines)
# Save the dataframe
missense_df.to_csv(args.missense_output, sep='\t', header=True, index=True)
nonsense_df.to_csv(args.nonsense_output, sep='\t', header=True, index=True)
nm_df.to_csv(args.combined_output, sep='\t', header=True, index=True)
def main():
args = parse_args()
df = pd.read_csv(args.input, header=0)
cell_lines = CCLE_Info.read_cell_lines(args.cell_lines)
df = df[['Cell_Line', 'Tissue', 'Perturbagen', 'GRinf', 'GR_AOC']]
df = df.rename(columns={'Perturbagen': 'Drug'})
# restrict to cell line
df = df[df.Cell_Line.isin(cell_lines)]
print('* Number of drugs:', len(args.drugs))
print('\tDrugs:', args.drugs)
for drug, output in zip(args.drugs, args.outputs):
dr_df = get_drug_response_df(df, drug, cell_lines)
dr_df.to_csv(output, sep='\t', header=True, index=True)