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October 18, 2019 17:45
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the usage is described here: http://merenlab.org/2016/11/08/pangenomics-v2/#creating-a-quick-pangenome-with-functions
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#!/usr/bin/env python | |
# -*- coding: utf-8 | |
def main(args): | |
import pandas as pd | |
import re | |
input_file = args.input_file | |
output_file = args.output_file | |
data = pd.read_csv(input_file, sep='\t', index_col=False) | |
# deal with the header of the first column | |
name_column = list(data.columns)[0] | |
new_name_column = 'name' | |
data.rename(columns={name_column:new_name_column}, inplace=True ) | |
name_column = new_name_column | |
# change every non alpha numberic sequence to a single '_' | |
new_functions_names = data[name_column].map(lambda x: re.sub('[^0-9a-zA-Z]+', '_', x)) | |
if args.name_dict_output: | |
# create a dictionary between old and new names so we can use it later if we need to | |
name_dict = dict(zip(list(data[name_column]), list(new_functions_names))) | |
pd.DataFrame.from_dict(name_dict, orient='index').to_csv(args.name_dict_output, sep='\t', header=False) | |
data[name_column] = new_functions_names | |
# Save only the max value between redundant columns | |
new_data = data.groupby(name_column).max() | |
new_data.sort_values(by=name_column, axis=0, inplace=True) | |
new_data.to_csv(output_file, sep='\t') | |
if __name__ == '__main__': | |
import argparse | |
parser = argparse.ArgumentParser(description='Fix duplicate functions in ad hoc functional pangenome') | |
parser.add_argument('--input-file', metavar='FILE', type=str, help='functional occurrence table created with anvi-get-enriched-functions-per-pan-group and --functional-occurrence-table-output option') | |
parser.add_argument('--output-file', metavar='FILE', type=str, help='Output file for the functional occurrence table with new function names.') | |
parser.add_argument('--name-dict-output', metavar='FILE', type=str, help='A file to store a dictionary to translate between the original and simplified names.', default='') | |
args = parser.parse_args() | |
main(args) |
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