Mercurial > repos > pieterlukasse > prims_metabolomics
annotate export_to_metexp_tabular.py @ 62:9bd2597c8851 default tip
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author | pieter.lukasse@wur.nl |
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date | Fri, 06 Feb 2015 15:49:26 +0100 |
parents | 19d8fd10248e |
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0 | 1 #!/usr/bin/env python |
2 # encoding: utf-8 | |
3 ''' | |
4 Module to combine output from the GCMS Galaxy tools RankFilter, CasLookup and MsClust | |
5 into a tabular file that can be uploaded to the MetExp database. | |
6 | |
7 RankFilter, CasLookup are already combined by combine_output.py so here we will use | |
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8 this result. Furthermore here one of the MsClust |
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9 quantification files containing the respective spectra details are to be combined as well. |
0 | 10 |
11 Extra calculations performed: | |
12 - The column MW is also added here and is derived from the column FORMULA found | |
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13 in RankFilter, CasLookup combined result. |
0 | 14 |
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15 So in total here we merge 2 files and calculate one new column. |
0 | 16 ''' |
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17 from pkg_resources import resource_filename # @UnresolvedImport # pylint: disable=E0611 |
0 | 18 import csv |
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19 import re |
0 | 20 import sys |
21 from collections import OrderedDict | |
22 | |
23 __author__ = "Pieter Lukasse" | |
24 __contact__ = "pieter.lukasse@wur.nl" | |
25 __copyright__ = "Copyright, 2013, Plant Research International, WUR" | |
26 __license__ = "Apache v2" | |
27 | |
28 def _process_data(in_csv, delim='\t'): | |
29 ''' | |
30 Generic method to parse a tab-separated file returning a dictionary with named columns | |
31 @param in_csv: input filename to be parsed | |
32 ''' | |
33 data = list(csv.reader(open(in_csv, 'rU'), delimiter=delim)) | |
34 header = data.pop(0) | |
35 # Create dictionary with column name as key | |
36 output = OrderedDict() | |
37 for index in xrange(len(header)): | |
38 output[header[index]] = [row[index] for row in data] | |
39 return output | |
40 | |
41 ONE_TO_ONE = 'one_to_one' | |
42 N_TO_ONE = 'n_to_one' | |
43 | |
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44 def _merge_data(set1, link_field_set1, set2, link_field_set2, compare_function, merge_function, metadata, relation_type=ONE_TO_ONE): |
0 | 45 ''' |
46 Merges data from both input dictionaries based on the link fields. This method will | |
47 build up a new list containing the merged hits as the items. | |
48 @param set1: dictionary holding set1 in the form of N lists (one list per attribute name) | |
49 @param set2: dictionary holding set2 in the form of N lists (one list per attribute name) | |
50 ''' | |
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51 # TODO test for correct input files -> same link_field values should be there |
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52 # (test at least number of unique link_field values): |
0 | 53 # |
54 # if (len(set1[link_field_set1]) != len(set2[link_field_set2])): | |
55 # raise Exception('input files should have the same nr of key values ') | |
56 | |
57 | |
58 merged = [] | |
59 processed = {} | |
60 for link_field_set1_idx in xrange(len(set1[link_field_set1])): | |
61 link_field_set1_value = set1[link_field_set1][link_field_set1_idx] | |
62 if not link_field_set1_value in processed : | |
63 # keep track of processed items to not repeat them | |
64 processed[link_field_set1_value] = link_field_set1_value | |
65 | |
66 # Get the indices for current link_field_set1_value in both data-structures for proper matching | |
67 set1index = [index for index, value in enumerate(set1[link_field_set1]) if value == link_field_set1_value] | |
68 set2index = [index for index, value in enumerate(set2[link_field_set2]) if compare_function(value, link_field_set1_value)==True ] | |
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69 # Validation : |
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70 if len(set2index) == 0: |
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71 # means that corresponding data could not be found in set2, then throw error |
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72 raise Exception("Datasets not compatible, merge not possible. " + link_field_set1 + "=" + |
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73 link_field_set1_value + " only found in first dataset. ") |
0 | 74 |
75 merged_hits = [] | |
76 # Combine hits | |
77 for hit in xrange(len(set1index)): | |
78 # Create records of hits to be merged ("keys" are the attribute names, so what the lines below do | |
79 # is create a new "dict" item with same "keys"/attributes, with each attribute filled with its | |
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80 # corresponding value in the sets; i.e. |
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81 # set1[key] => returns the list/array with size = nrrows, with the values for the attribute |
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82 # represented by "key". |
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83 # set1index[hit] => points to the row nr=hit (hit is a rownr/index) |
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84 # So set1[x][set1index[n]] = set1.attributeX.instanceN |
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85 # |
0 | 86 # It just ensures the entry is made available as a plain named array for easy access. |
87 rf_record = OrderedDict(zip(set1.keys(), [set1[key][set1index[hit]] for key in set1.keys()])) | |
88 if relation_type == ONE_TO_ONE : | |
89 cl_record = OrderedDict(zip(set2.keys(), [set2[key][set2index[hit]] for key in set2.keys()])) | |
90 else: | |
91 # is N to 1: | |
92 cl_record = OrderedDict(zip(set2.keys(), [set2[key][set2index[0]] for key in set2.keys()])) | |
93 | |
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94 merged_hit = merge_function(rf_record, cl_record, metadata) |
0 | 95 merged_hits.append(merged_hit) |
96 | |
97 merged.append(merged_hits) | |
98 | |
99 return merged, len(set1index) | |
100 | |
101 | |
102 def _compare_records(key1, key2): | |
103 ''' | |
104 in this case the compare method is really simple as both keys are expected to contain | |
105 same value when records are the same | |
106 ''' | |
107 if key1 == key2: | |
108 return True | |
109 else: | |
110 return False | |
111 | |
112 | |
113 | |
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114 def _merge_records(rank_caslookup_combi, msclust_quant_record, metadata): |
0 | 115 ''' |
116 Combines single records from both the RankFilter+CasLookup combi file and from MsClust file | |
117 | |
118 @param rank_caslookup_combi: rankfilter and caslookup combined record (see combine_output.py) | |
119 @param msclust_quant_record: msclust quantification + spectrum record | |
120 ''' | |
121 record = [] | |
122 for column in rank_caslookup_combi: | |
123 record.append(rank_caslookup_combi[column]) | |
124 | |
125 for column in msclust_quant_record: | |
126 record.append(msclust_quant_record[column]) | |
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127 |
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128 for column in metadata: |
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129 record.append(metadata[column]) |
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130 |
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131 # add MOLECULAR MASS (MM) |
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132 molecular_mass = get_molecular_mass(rank_caslookup_combi['FORMULA']) |
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133 # limit to two decimals: |
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134 record.append("{0:.2f}".format(molecular_mass)) |
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135 |
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136 # add MOLECULAR WEIGHT (MW) - TODO - calculate this |
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137 record.append('0.0') |
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138 |
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139 # level of identification and Location of reference standard |
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140 record.append('0') |
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141 record.append('') |
0 | 142 |
143 return record | |
144 | |
145 | |
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146 def get_molecular_mass(formula): |
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147 ''' |
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148 Calculates the molecular mass (MM). |
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149 E.g. MM of H2O = (relative)atomic mass of H x2 + (relative)atomic mass of O |
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150 ''' |
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151 |
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152 # Each element is represented by a capital letter, followed optionally by |
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153 # lower case, with one or more digits as for how many elements: |
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154 element_pattern = re.compile("([A-Z][a-z]?)(\d*)") |
0 | 155 |
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156 total_mass = 0 |
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157 for (element_name, count) in element_pattern.findall(formula): |
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158 if count == "": |
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159 count = 1 |
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160 else: |
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161 count = int(count) |
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162 element_mass = float(elements_and_masses_map[element_name]) # "found: Python's built-in float type has double precision " (? check if really correct ?) |
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163 total_mass += element_mass * count |
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164 |
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165 return total_mass |
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166 |
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167 |
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168 |
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169 def _save_data(data, headers, out_csv): |
0 | 170 ''' |
171 Writes tab-separated data to file | |
172 @param data: dictionary containing merged dataset | |
173 @param out_csv: output csv file | |
174 ''' | |
175 | |
176 # Open output file for writing | |
177 outfile_single_handle = open(out_csv, 'wb') | |
178 output_single_handle = csv.writer(outfile_single_handle, delimiter="\t") | |
179 | |
180 # Write headers | |
181 output_single_handle.writerow(headers) | |
182 | |
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183 # Write |
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184 for item_idx in xrange(len(data)): |
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185 for hit in data[item_idx]: |
0 | 186 output_single_handle.writerow(hit) |
187 | |
188 | |
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189 def _get_map_for_elements_and_masses(elements_and_masses): |
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190 ''' |
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191 This method will read out the column 'Chemical symbol' and make a map |
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192 of this, storing the column 'Relative atomic mass' as its value |
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193 ''' |
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194 resultMap = {} |
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195 index = 0 |
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196 for entry in elements_and_masses['Chemical symbol']: |
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197 resultMap[entry] = elements_and_masses['Relative atomic mass'][index] |
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198 index += 1 |
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199 |
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200 return resultMap |
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201 |
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202 |
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203 def init_elements_and_masses_map(): |
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204 ''' |
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205 Initializes the lookup map containing the elements and their respective masses |
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206 ''' |
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207 elements_and_masses = _process_data(resource_filename(__name__, "static_resources/elements_and_masses.tab")) |
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208 global elements_and_masses_map |
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209 elements_and_masses_map = _get_map_for_elements_and_masses(elements_and_masses) |
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210 |
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211 |
0 | 212 def main(): |
213 ''' | |
214 Combine Output main function | |
215 | |
216 RankFilter, CasLookup are already combined by combine_output.py so here we will use | |
217 this result. Furthermore here the MsClust spectra file (.MSP) and one of the MsClust | |
218 quantification files are to be combined with combine_output.py result as well. | |
219 ''' | |
220 rankfilter_and_caslookup_combined_file = sys.argv[1] | |
221 msclust_quantification_and_spectra_file = sys.argv[2] | |
222 output_csv = sys.argv[3] | |
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223 # metadata |
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224 metadata = OrderedDict() |
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225 metadata['organism'] = sys.argv[4] |
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226 metadata['tissue'] = sys.argv[5] |
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227 metadata['experiment_name'] = sys.argv[6] |
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228 metadata['user_name'] = sys.argv[7] |
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229 metadata['column_type'] = sys.argv[8] |
0 | 230 |
231 # Read RankFilter and CasLookup output files | |
232 rankfilter_and_caslookup_combined = _process_data(rankfilter_and_caslookup_combined_file) | |
233 msclust_quantification_and_spectra = _process_data(msclust_quantification_and_spectra_file, ',') | |
234 | |
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235 # Read elements and masses to use for the MW/MM calculation : |
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236 init_elements_and_masses_map() |
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237 |
0 | 238 merged, nhits = _merge_data(rankfilter_and_caslookup_combined, 'Centrotype', |
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239 msclust_quantification_and_spectra, 'centrotype', |
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240 _compare_records, _merge_records, metadata, |
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241 N_TO_ONE) |
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242 headers = rankfilter_and_caslookup_combined.keys() + msclust_quantification_and_spectra.keys() + metadata.keys() + ['MM','MW', 'Level of identification', 'Location of reference standard'] |
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243 _save_data(merged, headers, output_csv) |
0 | 244 |
245 | |
246 if __name__ == '__main__': | |
247 main() |