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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,95 @@ | ||
| #!/usr/bin/env python | ||
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| import pandas as pd | ||
| import json | ||
| import os | ||
| import argparse | ||
| import logging | ||
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| logger = logging.getLogger(__name__) | ||
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| parser = argparse.ArgumentParser(description='This script will collect align summaries based on hisat2 run.') | ||
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| parser.add_argument('-p', '--project-path', help='Path to project folder') | ||
| parser.add_argument('-g', '--groups-json', help='json file for experiment setup with full path.') | ||
| parser.add_argument('-o', '--output', help='Path and prefix of output file. e.g.: ./results/align_summaries.tsv') | ||
| parser.add_argument('-m', '--map-to-mask', help='Collect align summaries for mask only run.', action='store_true', default=False) | ||
| args = parser.parse_args() | ||
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| groups = json.load(open(args.groups_json, 'r')) | ||
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| if args.map_to_mask: | ||
| pp = os.path.abspath(os.path.join(args.project_path, 'map_to_mask')) # pp: path prefix | ||
| else: | ||
| pp = os.path.abspath(args.project_path) # pp: path prefix | ||
| for sample in [sample for group in groups.values() for sample in group.keys()]: | ||
| with open(os.path.join(pp, sample, 'align_summary.txt'), 'r') as f: | ||
| for line in f.readlines(): | ||
| if line.startswith('Left'): # it is tophat summary | ||
| sum_type = 'tophat2' | ||
| if 'df' not in locals(): | ||
| df = pd.DataFrame(columns=['Left_reads', 'Mapped_Left_reads', 'Percent_mapped_Left_reads', | ||
| 'Multiple_alignments_Left_reads', | ||
| 'Percent_multiple_alignments_Left_reads', | ||
| 'Right_reads', 'Mapped_Right_reads', 'Percent_mapped_Right_reads', | ||
| 'Multiple_alignments_Right_reads', | ||
| 'Percent_multiple_alignments_Right_reads', | ||
| 'Aligned_pairs', 'Multiple_alignments_pairs', | ||
| 'Percent_multiple_alignments_pairs', 'Concordant_pair_alignment_rate']) | ||
| elif line.endswith('reads; of these:\n'): # it is hisat2 summary | ||
| sum_type = 'hisat2' | ||
| if 'df' not in locals(): | ||
| df = pd.DataFrame(columns=['Reads', 'Paired_reads', 'Pairs_not_concordantly_aligned', | ||
| 'Pairs_concordantly_aligned_once', 'Pairs_concordantly_aligned_multiple', | ||
| 'Pairs_discordantly_aligned_once', | ||
| 'Remaining_singles_not_aligned', 'Remaining_singles_aligned_once', | ||
| 'Remaining_singles_aligned_multiple', 'Overall_alignment_percentage']) | ||
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| if sum_type == 'tophat2': | ||
| if line.startswith('Left'): | ||
| side = 'Left' | ||
| elif line.startswith('Right'): | ||
| side = 'Right' | ||
| elif line.startswith('Aligned'): | ||
| side = False | ||
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| if line.startswith(' Input :') and side != False: | ||
| df.loc[i, side + '_reads'] = line.split()[2] | ||
| elif line.startswith(' Mapped :') and side != False: | ||
| df.loc[i, 'Mapped_' + side + '_reads'] = line.split()[2] | ||
| df.loc[i, 'Percent_mapped_' + side + '_reads'] = line.split('(')[1].split('%')[0] | ||
| elif line.startswith(' of these:') and side != False: | ||
| df.loc[i, 'Multiple_alignments_' + side + '_reads'] = line.split()[2] | ||
| df.loc[i, 'Percent_multiple_alignments_' + side + '_reads'] = line.split('(')[1].split('%')[0] | ||
| elif side == False and line.startswith('Aligned pairs:'): | ||
| df.loc[i, 'Aligned_pairs'] = line.split()[2] | ||
| elif side == False and line.startswith(' of these:'): | ||
| df.loc[i, 'Multiple_alignments_pairs'] = line.split()[2] | ||
| df.loc[i, 'Percent_multiple_alignments_pairs'] = line.split('(')[1].split('%')[0] | ||
| elif side == False and line.endswith('concordant pair alignment rate.\n'): | ||
| df.loc[i, 'Concordant_pair_alignment_rate'] = line.split('%')[0] | ||
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| elif sum_type == 'hisat2': | ||
| if line.endswith('reads; of these:\n'): | ||
| df.loc[i, 'Reads'] = line.split()[0] | ||
| elif line.endswith('were paired; of these:\n'): | ||
| df.loc[i, 'Paired_reads'] = line.split()[0] | ||
| elif line.endswith('aligned concordantly 0 times\n'): | ||
| df.loc[i, 'Pairs_not_concordantly_aligned'] = line.split()[0] | ||
| elif line.endswith('aligned concordantly exactly 1 time\n'): | ||
| df.loc[i, 'Pairs_concordantly_aligned_once'] = line.split()[0] | ||
| elif line.endswith('aligned concordantly >1 times\n'): | ||
| df.loc[i, 'Pairs_concordantly_aligned_multiple'] = line.split()[0] | ||
| elif line.endswith('aligned discordantly 1 time\n'): | ||
| df.loc[i, 'Pairs_discordantly_aligned_once'] = line.split()[0] | ||
| elif line.endswith('aligned 0 times\n'): | ||
| df.loc[i, 'Remaining_singles_not_aligned'] = line.split()[0] | ||
| elif line.endswith('aligned exactly 1 time\n'): | ||
| df.loc[i, 'Remaining_singles_aligned_once'] = line.split()[0] | ||
| elif line.endswith('aligned >1 times\n'): | ||
| df.loc[i, 'Remaining_singles_aligned_multiple'] = line.split()[0] | ||
| elif line.endswith('overall alignment rate\n'): | ||
| df.loc[i, 'Overall_alignment_percentage'] = line.split('%')[0] | ||
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| df.to_csv('align_summaries.csv', sep=' ') |
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