axis=1)
# export result
output_result = os.path.join(os.path.dirname(args.output_file),
'{}'.format(comparison))
logging.debug('Path to file: {}'.format(output_result))
h.export_result_to_csv(result_df, output_result)
logger.info('Data for {} exported to csv'.format(comparison))
return
if __name__ == "__main__":
args = get_args()
rule_params = h.load_json_parameter(args.file_id)
filename = h.filename(args.input_file)
# get logger
logpath = os.path.join(paths.global_data_dir, args.file_id,
'log/divide.log')
logger = h.get_logger(logpath)
# get data
data_df = pd.read_csv(args.input_file, header=0, index_col=None)
# parameters
values_cols_prefix = rule_params['all']['values_cols_prefix']
# get contrast matrix
comparison_df = pd.read_csv(args.comparison_file, index_col=0, header=0)
data_col_str = rule_params['all']['value_col_prefix'] + '_'
data_col = df.filter(regex=data_col_str)
#data_col = data_col.apply(pd.to_numeric) # à déplacer
res = pd.concat([df[metadata_col], data_col], axis=1)
return res
def suppress_panel_genes(df):
res = df[df['Class Name'] == 'Endogenous']
return res
if __name__ == "__main__":
args = get_args()
rule_params = h.load_json_parameter(args.project, args.version)
filename = h.filename(args.input_file)
logpath = os.path.join(rule_params['all']['logpath'], 'NanoString_data_format.log')
logger = h.get_logger(logpath)
input_df = pd.read_csv(args.input_file, header=0, index_col=None, sep='\t')
print(input_df.head())
if args.mapping_file:
# annotate columns based on mapping file information
df_metadata, df_numeric_data = split_data(input_df)
header = df_metadata.iloc[0] # discard rows with annotations
data = pd.concat([header, df_numeric_data], axis=1)
