def main(species, outPath, base_path, samples):
samples = f_utils.set_path_to_files_glob(samples, 'TAB_lenDist_summary.txt')
datout, window, tot_c, mirs_dat, mirs, mirs_c = get_data_from_file(samples, species)
write_raw_counts_table(datout, window, outPath)
all_wind = windows_to_norm_counts(datout, window, tot_c)
RPMM_mir_100 = mirs_over_thresh(all_wind, 100, window, species)
out_name = write_output(all_wind, 'RPMM_all.csv', outPath)
out_name = write_output(RPMM_mir_100, 'RPMM_mirs_over_100.csv', outPath)
mir_wind = windows_to_norm_counts(mirs_dat, mirs, mirs_c)
RPMMM_mir_50 = mirs_over_thresh(mir_wind, 50, mirs, species)
out_name = write_output(mir_wind, 'RPMMM_all.csv', outPath)
out_name = write_output(RPMMM_mir_50, 'RPMMM_mirs_over_50.csv', outPath)
print 'DONE!\n'
create_boxcox_trans(out_name)
if os.path.exists('{}/conditions.csv'.format(base_path)):
print 'Generating sample correlation heatmap...'
cmd = 'Rscript {}/sample_correlation.R {} {}'.format(os.path.dirname(__file__), out_name, '{}/conditions.csv'.format(base_path))
os.system(cmd)
print 'DONE!\n'
print 'Generating PCA...'
cmd = 'Rscript {}/pca.R {} {}'.format(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)
def main(basePath, 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()
cond_di = check_for_conditions_file(basePath)
write_mapping_file(out_di, outPath, line_head_li, cond_di)
create_map_stats_image(os.path.dirname(__file__), outPath)
def main(species, outPath, base_path, name, threshold, samples):
samples = f_utils.set_path_to_files_glob(samples,
'TAB_3p_summary_yRNA.txt')
datout, window, tot_c = get_data_from_file(samples, species)
all_wind = windows_to_norm_counts(datout, window, tot_c)
out_name = write_output(all_wind, '{}_all.csv'.format(name), outPath)
if threshold:
over_thresh = windows_over_thresh(all_wind, threshold, window, species)
out_name = write_output(over_thresh,
'{}_over_{}.csv'.format(name,
threshold), outPath)
def main(species, outPath, samples):
samples = f_utils.set_path_to_files_glob(samples, 'TAB_lenDist_summary.txt')
datout, window, tot_c, mirs_dat, mirs, mirs_c = get_data_from_file(samples, species)
all_wind = windows_to_norm_counts(datout, window, tot_c)
RPMM_mir_100 = mirs_over_thresh(all_wind, 100, window, species)
out_name = write_output(all_wind, 'RPMM_all.csv', outPath)
out_name = write_output(RPMM_mir_100, 'RPMM_mirs_over_100.csv', outPath)
mir_wind = windows_to_norm_counts(mirs_dat, mirs, mirs_c)
RPMMM_mir_50 = mirs_over_thresh(mir_wind, 50, mirs, species)
out_name = write_output(mir_wind, 'RPMMM_all.csv', outPath)
out_name = write_output(RPMMM_mir_50, 'RPMMM_mirs_over_50.csv', outPath)
cmd = 'Rscript {}/sample_correlation.R {}'.format(os.path.dirname(__file__), out_name)
os.system(cmd)
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)
def main(outPath, samples):
samples = f_utils.set_path_to_files_glob(samples, 'ead_length_histo')
len_di, lengths = read_lengths_dict(samples)
out_name = write_length_distribution(outPath, len_di, lengths)
create_length_dist_image(os.path.dirname(__file__), out_name)
def main(outPath, samples):
samples = f_utils.set_path_to_files_glob(samples, 'ead_length_histo')
len_di, lengths = read_lengths_dict(samples)
out_name = write_length_distribution(outPath, len_di, lengths)
create_length_dist_image(os.path.dirname(__file__), out_name)
