annotate vsnp_build_tables.py @ 5:2ec51816eaac draft

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author greg
date Wed, 16 Jun 2021 17:43:30 +0000
parents abfb861df879
children f641e52353e8
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1 #!/usr/bin/env python
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2
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3 import argparse
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4 import os
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5 import re
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6
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7 import pandas
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8 import pandas.io.formats.excel
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9 from Bio import SeqIO
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10
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11 # Maximum columns allowed in a LibreOffice
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12 # spreadsheet is 1024. Excel allows for
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13 # 16,384 columns, but we'll set the lower
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14 # number as the maximum. Some browsers
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15 # (e.g., Firefox on Linux) are configured
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16 # to use LibreOffice for Excel spreadsheets.
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17 MAXCOLS = 1024
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18 OUTPUT_EXCEL_DIR = 'output_excel_dir'
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19
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20
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21 def annotate_table(table_df, group, annotation_dict):
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22 for gbk_chrome, pro in list(annotation_dict.items()):
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23 ref_pos = list(table_df)
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24 ref_series = pandas.Series(ref_pos)
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25 ref_df = pandas.DataFrame(ref_series.str.split(':', expand=True).values, columns=['reference', 'position'])
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26 all_ref = ref_df[ref_df['reference'] == gbk_chrome]
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27 positions = all_ref.position.to_frame()
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28 # Create an annotation file.
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29 annotation_file = "%s_annotations.csv" % group
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30 with open(annotation_file, "a") as fh:
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31 for _, row in positions.iterrows():
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32 pos = row.position
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33 try:
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34 aaa = pro.iloc[pro.index.get_loc(int(pos))][['chrom', 'locus', 'product', 'gene']]
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35 try:
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36 chrom, name, locus, tag = aaa.values[0]
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37 print("{}:{}\t{}, {}, {}".format(chrom, pos, locus, tag, name), file=fh)
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38 except ValueError:
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39 # If only one annotation for the entire
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40 # chromosome (e.g., flu) then having [0] fails
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41 chrom, name, locus, tag = aaa.values
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42 print("{}:{}\t{}, {}, {}".format(chrom, pos, locus, tag, name), file=fh)
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43 except KeyError:
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44 print("{}:{}\tNo annotated product".format(gbk_chrome, pos), file=fh)
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45 # Read the annotation file into a data frame.
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46 annotations_df = pandas.read_csv(annotation_file, sep='\t', header=None, names=['index', 'annotations'], index_col='index')
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47 # Remove the annotation_file from disk since both
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48 # cascade and sort tables are built using the file,
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49 # and it is opened for writing in append mode.
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50 os.remove(annotation_file)
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51 # Process the data.
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52 table_df_transposed = table_df.T
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53 table_df_transposed.index = table_df_transposed.index.rename('index')
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54 table_df_transposed = table_df_transposed.merge(annotations_df, left_index=True, right_index=True)
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55 table_df = table_df_transposed.T
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56 return table_df
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57
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58
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59 def excel_formatter(json_file_name, excel_file_name, group, annotation_dict):
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60 pandas.io.formats.excel.header_style = None
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61 table_df = pandas.read_json(json_file_name, orient='split')
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62 if annotation_dict is not None:
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63 table_df = annotate_table(table_df, group, annotation_dict)
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64 else:
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65 table_df = table_df.append(pandas.Series(name='no annotations'))
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66 writer = pandas.ExcelWriter(excel_file_name, engine='xlsxwriter')
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67 table_df.to_excel(writer, sheet_name='Sheet1')
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68 writer_book = writer.book
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69 ws = writer.sheets['Sheet1']
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70 format_a = writer_book.add_format({'bg_color': '#58FA82'})
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71 format_g = writer_book.add_format({'bg_color': '#F7FE2E'})
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72 format_c = writer_book.add_format({'bg_color': '#0000FF'})
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73 format_t = writer_book.add_format({'bg_color': '#FF0000'})
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74 format_normal = writer_book.add_format({'bg_color': '#FDFEFE'})
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75 formatlowqual = writer_book.add_format({'font_color': '#C70039', 'bg_color': '#E2CFDD'})
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76 format_ambigous = writer_book.add_format({'font_color': '#C70039', 'bg_color': '#E2CFDD'})
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77 format_n = writer_book.add_format({'bg_color': '#E2CFDD'})
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78 rows, cols = table_df.shape
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79 ws.set_column(0, 0, 30)
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80 ws.set_column(1, cols, 2.1)
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81 ws.freeze_panes(2, 1)
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82 format_annotation = writer_book.add_format({'font_color': '#0A028C', 'rotation': '-90', 'align': 'top'})
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83 # Set last row.
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84 ws.set_row(rows + 1, cols + 1, format_annotation)
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85 # Make sure that row/column locations don't overlap.
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86 ws.conditional_format(rows - 2, 1, rows - 1, cols, {'type': 'cell', 'criteria': '<', 'value': 55, 'format': formatlowqual})
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87 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'cell', 'criteria': '==', 'value': 'B$2', 'format': format_normal})
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88 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'A', 'format': format_a})
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89 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'G', 'format': format_g})
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90 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'C', 'format': format_c})
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91 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'T', 'format': format_t})
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92 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'S', 'format': format_ambigous})
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93 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'Y', 'format': format_ambigous})
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94 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'R', 'format': format_ambigous})
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95 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'W', 'format': format_ambigous})
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96 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'K', 'format': format_ambigous})
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97 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'M', 'format': format_ambigous})
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98 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': 'N', 'format': format_n})
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99 ws.conditional_format(2, 1, rows - 2, cols, {'type': 'text', 'criteria': 'containing', 'value': '-', 'format': format_n})
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100 format_rotation = writer_book.add_format({})
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101 format_rotation.set_rotation(90)
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102 for column_num, column_name in enumerate(list(table_df.columns)):
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103 ws.write(0, column_num + 1, column_name, format_rotation)
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104 format_annotation = writer_book.add_format({'font_color': '#0A028C', 'rotation': '-90', 'align': 'top'})
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105 # Set last row.
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106 ws.set_row(rows, 400, format_annotation)
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107 writer.save()
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108
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109
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110 def get_annotation_dict(gbk_file):
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111 gbk_dict = SeqIO.to_dict(SeqIO.parse(gbk_file, "genbank"))
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112 annotation_dict = {}
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113 tmp_file = "features.csv"
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114 # Create a file of chromosomes and features.
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115 for chromosome in list(gbk_dict.keys()):
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116 with open(tmp_file, 'w+') as fh:
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117 for feature in gbk_dict[chromosome].features:
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118 if "CDS" in feature.type or "rRNA" in feature.type:
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119 try:
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120 product = feature.qualifiers['product'][0]
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121 except KeyError:
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122 product = None
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123 try:
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124 locus = feature.qualifiers['locus_tag'][0]
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125 except KeyError:
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126 locus = None
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127 try:
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128 gene = feature.qualifiers['gene'][0]
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129 except KeyError:
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130 gene = None
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131 fh.write("%s\t%d\t%d\t%s\t%s\t%s\n" % (chromosome, int(feature.location.start), int(feature.location.end), locus, product, gene))
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132 # Read the chromosomes and features file into a data frame.
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133 df = pandas.read_csv(tmp_file, sep='\t', names=["chrom", "start", "stop", "locus", "product", "gene"])
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134 # Process the data.
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135 df = df.sort_values(['start', 'gene'], ascending=[True, False])
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136 df = df.drop_duplicates('start')
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137 pro = df.reset_index(drop=True)
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138 pro.index = pandas.IntervalIndex.from_arrays(pro['start'], pro['stop'], closed='both')
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139 annotation_dict[chromosome] = pro
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140 return annotation_dict
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141
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142
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143 def get_sample_name(file_path):
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144 base_file_name = os.path.basename(file_path)
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145 if base_file_name.find(".") > 0:
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146 # Eliminate the extension.
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147 return os.path.splitext(base_file_name)[0]
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148 return base_file_name
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149
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150
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151 def output_cascade_table(cascade_order, mqdf, group, annotation_dict):
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152 cascade_order_mq = pandas.concat([cascade_order, mqdf], join='inner')
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153 output_table(cascade_order_mq, "cascade", group, annotation_dict)
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154
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155
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156 def output_excel(df, type_str, group, annotation_dict, count=None):
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157 # Output the temporary json file that
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158 # is used by the excel_formatter.
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159 if count is None:
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160 if group is None:
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161 json_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_order_mq.json" % type_str)
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162 excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_table.xlsx" % type_str)
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163 else:
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164 json_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_%s_order_mq.json" % (group, type_str))
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165 excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_%s_table.xlsx" % (group, type_str))
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166 else:
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167 # The table has more columns than is allowed by the
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168 # MAXCOLS setting, so multiple files will be produced
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169 # as an output collection.
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170 if group is None:
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171 json_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_order_mq_%d.json" % (type_str, count))
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172 excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_table_%d.xlsx" % (type_str, count))
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173 else:
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174 json_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_%s_order_mq_%d.json" % (group, type_str, count))
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175 excel_file_name = os.path.join(OUTPUT_EXCEL_DIR, "%s_%s_table_%d.xlsx" % (group, type_str, count))
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176 df.to_json(json_file_name, orient='split')
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177 # Output the Excel file.
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178 excel_formatter(json_file_name, excel_file_name, group, annotation_dict)
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179
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180
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181 def output_sort_table(cascade_order, mqdf, group, annotation_dict):
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182 sort_df = cascade_order.T
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183 sort_df['abs_value'] = sort_df.index
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184 sort_df[['chrom', 'pos']] = sort_df['abs_value'].str.split(':', expand=True)
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185 sort_df = sort_df.drop(['abs_value', 'chrom'], axis=1)
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186 sort_df.pos = sort_df.pos.astype(int)
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187 sort_df = sort_df.sort_values(by=['pos'])
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188 sort_df = sort_df.drop(['pos'], axis=1)
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189 sort_df = sort_df.T
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190 sort_order_mq = pandas.concat([sort_df, mqdf], join='inner')
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191 output_table(sort_order_mq, "sort", group, annotation_dict)
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192
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193
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194 def output_table(df, type_str, group, annotation_dict):
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195 if isinstance(group, str) and group.startswith("dataset"):
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196 # Inputs are single files, not collections,
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197 # so input file names are not useful for naming
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198 # output files.
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199 group_str = None
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200 else:
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201 group_str = group
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202 count = 0
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203 chunk_start = 0
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204 chunk_end = 0
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205 column_count = df.shape[1]
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206 if column_count >= MAXCOLS:
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207 # Here the number of columns is greater than
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208 # the maximum allowed by Excel, so multiple
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209 # outputs will be produced.
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210 while column_count >= MAXCOLS:
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211 count += 1
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212 chunk_end += MAXCOLS
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213 df_of_type = df.iloc[:, chunk_start:chunk_end]
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214 output_excel(df_of_type, type_str, group_str, annotation_dict, count=count)
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215 chunk_start += MAXCOLS
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216 column_count -= MAXCOLS
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217 count += 1
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218 df_of_type = df.iloc[:, chunk_start:]
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219 output_excel(df_of_type, type_str, group_str, annotation_dict, count=count)
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220 else:
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221 output_excel(df, type_str, group_str, annotation_dict)
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222
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223
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224 def preprocess_tables(newick_file, json_file, json_avg_mq_file, annotation_dict):
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225 avg_mq_series = pandas.read_json(json_avg_mq_file, typ='series', orient='split')
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226 # Map quality to dataframe.
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227 mqdf = avg_mq_series.to_frame(name='MQ')
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228 mqdf = mqdf.T
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229 # Get the group.
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230 group = get_sample_name(newick_file)
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231 snps_df = pandas.read_json(json_file, orient='split')
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232 with open(newick_file, 'r') as fh:
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233 for line in fh:
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234 line = re.sub('[:,]', '\n', line)
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235 line = re.sub('[)(]', '', line)
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236 line = re.sub(r'[0-9].*\.[0-9].*\n', '', line)
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237 line = re.sub('root\n', '', line)
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238 sample_order = line.split('\n')
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239 sample_order = list([_f for _f in sample_order if _f])
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240 sample_order.insert(0, 'root')
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241 tree_order = snps_df.loc[sample_order]
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242 # Count number of SNPs in each column.
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243 snp_per_column = []
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244 for column_header in tree_order:
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245 count = 0
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246 column = tree_order[column_header]
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247 for element in column:
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248 if element != column[0]:
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249 count = count + 1
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250 snp_per_column.append(count)
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251 row1 = pandas.Series(snp_per_column, tree_order.columns, name="snp_per_column")
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252 # Count number of SNPS from the
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253 # top of each column in the table.
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254 snp_from_top = []
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255 for column_header in tree_order:
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256 count = 0
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257 column = tree_order[column_header]
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258 # for each element in the column
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259 # skip the first element
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260 for element in column[1:]:
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261 if element == column[0]:
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262 count = count + 1
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263 else:
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264 break
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265 snp_from_top.append(count)
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266 row2 = pandas.Series(snp_from_top, tree_order.columns, name="snp_from_top")
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267 tree_order = tree_order.append([row1])
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268 tree_order = tree_order.append([row2])
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269 # In pandas=0.18.1 even this does not work:
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270 # abc = row1.to_frame()
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271 # abc = abc.T --> tree_order.shape (5, 18), abc.shape (1, 18)
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272 # tree_order.append(abc)
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273 # Continue to get error: "*** ValueError: all the input arrays must have same number of dimensions"
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274 tree_order = tree_order.T
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275 tree_order = tree_order.sort_values(['snp_from_top', 'snp_per_column'], ascending=[True, False])
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276 tree_order = tree_order.T
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277 # Remove snp_per_column and snp_from_top rows.
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278 cascade_order = tree_order[:-2]
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279 # Output the cascade table.
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280 output_cascade_table(cascade_order, mqdf, group, annotation_dict)
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281 # Output the sorted table.
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282 output_sort_table(cascade_order, mqdf, group, annotation_dict)
0
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283
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284
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285 if __name__ == '__main__':
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286 parser = argparse.ArgumentParser()
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287
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288 parser.add_argument('--gbk_file', action='store', dest='gbk_file', required=False, default=None, help='Optional gbk file'),
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289 parser.add_argument('--input_avg_mq_json', action='store', dest='input_avg_mq_json', help='Average MQ json file')
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290 parser.add_argument('--input_newick', action='store', dest='input_newick', help='Newick file')
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291 parser.add_argument('--input_snps_json', action='store', dest='input_snps_json', help='SNPs json file')
0
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292
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293 args = parser.parse_args()
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294
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295 if args.gbk_file is not None:
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296 # Create the annotation_dict for annotating
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297 # the Excel tables.
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298 annotation_dict = get_annotation_dict(args.gbk_file)
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299 else:
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300 annotation_dict = None
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301
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302 preprocess_tables(args.input_newick, args.input_snps_json, args.input_avg_mq_json, annotation_dict)