os.path.dirname(__file__), out_name,
'{}/conditions.csv'.format(base_path))
os.system(cmd)
else:
print 'Generating sample correlation heatmap...'
cmd = 'Rscript {}/sample_correlation.R {}'.format(
os.path.dirname(__file__), out_name)
os.system(cmd)
print 'DONE!\n'
print 'Generating PCA...'
cmd = 'Rscript {}/pca.R {}'.format(os.path.dirname(__file__), out_name)
os.system(cmd)
if __name__ == '__main__':
f_utils.check_for_input(sys.argv, usage)
parser = argparse.ArgumentParser(
description='Calculates the RPMM across samples')
parser.add_argument('sp',
action='store',
help='Species used in this study')
parser.add_argument('outPath',
action='store',
help='Path to where the output file will be located')
parser.add_argument('base_path',
action='store',
help='Path to where the output file will be located')
parser.add_argument(
'samples',
action='store',
nargs='+',
def main():
f_utils.check_for_input(sys.argv, usage)
mapping_stats(sys.argv[1])
length_distribution(sys.argv[1])
RPMMandRPMMM(sys.argv[1], sys.argv[2])
sample_correlation('RPMM_mirs_over_100.csv')
def write_mapping_file(out_di, out_dir, line_head_li):
"""
Writes the output dictionary to a file, called MappingInfoTable.csv
"""
with open("{}/Mapping_Statistics.csv".format(out_dir), "w") as f:
f.write("Sample_name,{}\n".format(",".join(sorted(out_di))))
for item in line_head_li:
f.write("{}".format(item[1]))
for sample in sorted(out_di):
f.write(",{}".format(out_di[sample][item[0]]))
f.write("\n")
def main(outPath, samples):
samples = f_utils.set_path_to_files_glob(samples, "stats")
out_di = mapping_stats_dict(samples)
out_di = calculate_additional_stats(out_di)
line_head_li = output_line_headers()
write_mapping_file(out_di, outPath, line_head_li)
if __name__ == "__main__":
f_utils.check_for_input(sys.argv, usage)
parser = argparse.ArgumentParser(description=usage)
parser.add_argument("outPath", action="store", help="Path to where the output file will be located")
parser.add_argument(
"samples", nargs="+", action="store", help="Path to where the sample output folders are located"
)
arg = parser.parse_args()
main(arg.outPath, arg.samples)
def main():
f_utils.check_for_input(sys.argv, usage)
mapping_stats(sys.argv[1])
length_distribution(sys.argv[1])
RPMMandRPMMM(sys.argv[1], sys.argv[2])
sample_correlation('RPMM_mirs_over_100.csv')