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193
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1 from __future__ import division
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2 import csv
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3 from enum import Enum
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4 import re
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5 import sys
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6 import numpy as np
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7 import pandas as pd
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253
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8 import matplotlib.pyplot as plt
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193
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9 import itertools as it
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10 import scipy.stats as st
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11 import lxml.etree as ET
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12 import math
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13 import utils.general_utils as utils
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14 from PIL import Image
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15 import os
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16 import argparse
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17 import pyvips
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18 from typing import Tuple, Union, Optional, List, Dict
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19
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20 ERRORS = []
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21 ########################## argparse ##########################################
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22 ARGS :argparse.Namespace
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23 def process_args() -> argparse.Namespace:
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24 """
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25 Interfaces the script of a module with its frontend, making the user's choices for various parameters available as values in code.
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26
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27 Args:
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28 args : Always obtained (in file) from sys.argv
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29
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30 Returns:
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31 Namespace : An object containing the parsed arguments
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32 """
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33 parser = argparse.ArgumentParser(
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34 usage = "%(prog)s [options]",
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35 description = "process some value's genes to create a comparison's map.")
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36
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37 #General:
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38 parser.add_argument(
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39 '-td', '--tool_dir',
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40 type = str,
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41 required = True,
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42 help = 'your tool directory')
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43
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44 parser.add_argument('-on', '--control', type = str)
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45 parser.add_argument('-ol', '--out_log', help = "Output log")
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46
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47 #Computation details:
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48 parser.add_argument(
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49 '-co', '--comparison',
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50 type = str,
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51 default = '1vs1',
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52 choices = ['manyvsmany', 'onevsrest', 'onevsmany'])
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53
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54 parser.add_argument(
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55 '-pv' ,'--pValue',
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56 type = float,
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57 default = 0.1,
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58 help = 'P-Value threshold (default: %(default)s)')
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59
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60 parser.add_argument(
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61 '-fc', '--fChange',
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62 type = float,
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63 default = 1.5,
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64 help = 'Fold-Change threshold (default: %(default)s)')
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65
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66
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67 parser.add_argument(
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68 '-op', '--option',
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69 type = str,
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70 choices = ['datasets', 'dataset_class'],
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71 help='dataset or dataset and class')
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72
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73 parser.add_argument(
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241
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74 '-idf', '--input_data_fluxes',
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75 type = str,
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241
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76 help = 'input dataset fluxes')
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77
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78 parser.add_argument(
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241
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79 '-icf', '--input_class_fluxes',
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80 type = str,
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81 help = 'sample group specification fluxes')
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193
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82
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83 parser.add_argument(
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241
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84 '-idsf', '--input_datas_fluxes',
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193
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85 type = str,
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86 nargs = '+',
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241
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87 help = 'input datasets fluxes')
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193
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88
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89 parser.add_argument(
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241
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90 '-naf', '--names_fluxes',
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91 type = str,
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92 nargs = '+',
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241
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93 help = 'input names fluxes')
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193
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94
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95 #Output:
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96 parser.add_argument(
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97 "-gs", "--generate_svg",
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98 type = utils.Bool("generate_svg"), default = True,
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244
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99 help = "choose whether to generate svg")
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193
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100
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101 parser.add_argument(
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102 "-gp", "--generate_pdf",
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103 type = utils.Bool("generate_pdf"), default = True,
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244
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104 help = "choose whether to generate pdf")
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193
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105
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106 parser.add_argument(
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107 '-cm', '--custom_map',
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108 type = str,
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109 help='custom map to use')
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110
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111 parser.add_argument(
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112 '-mc', '--choice_map',
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113 type = utils.Model, default = utils.Model.HMRcore,
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114 choices = [utils.Model.HMRcore, utils.Model.ENGRO2, utils.Model.Custom])
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115
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116 args :argparse.Namespace = parser.parse_args()
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241
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117 args.net = True
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193
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118
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119 return args
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120
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121 ############################ dataset input ####################################
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122 def read_dataset(data :str, name :str) -> pd.DataFrame:
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123 """
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124 Tries to read the dataset from its path (data) as a tsv and turns it into a DataFrame.
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125
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126 Args:
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127 data : filepath of a dataset (from frontend input params or literals upon calling)
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128 name : name associated with the dataset (from frontend input params or literals upon calling)
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129
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130 Returns:
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131 pd.DataFrame : dataset in a runtime operable shape
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132
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133 Raises:
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134 sys.exit : if there's no data (pd.errors.EmptyDataError) or if the dataset has less than 2 columns
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135 """
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136 try:
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137 dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
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138 except pd.errors.EmptyDataError:
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139 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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140 if len(dataset.columns) < 2:
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141 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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142 return dataset
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143
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144 ############################ dataset name #####################################
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145 def name_dataset(name_data :str, count :int) -> str:
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146 """
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147 Produces a unique name for a dataset based on what was provided by the user. The default name for any dataset is "Dataset", thus if the user didn't change it this function appends f"_{count}" to make it unique.
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148
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149 Args:
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150 name_data : name associated with the dataset (from frontend input params)
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151 count : counter from 1 to make these names unique (external)
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152
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153 Returns:
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154 str : the name made unique
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155 """
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156 if str(name_data) == 'Dataset':
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157 return str(name_data) + '_' + str(count)
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158 else:
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159 return str(name_data)
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160
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161 ############################ map_methods ######################################
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162 FoldChange = Union[float, int, str] # Union[float, Literal[0, "-INF", "INF"]]
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163 def fold_change(avg1 :float, avg2 :float) -> FoldChange:
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164 """
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165 Calculates the fold change between two gene expression values.
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166
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167 Args:
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168 avg1 : average expression value from one dataset avg2 : average expression value from the other dataset
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169
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170 Returns:
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171 FoldChange :
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172 0 : when both input values are 0
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173 "-INF" : when avg1 is 0
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174 "INF" : when avg2 is 0
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175 float : for any other combination of values
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176 """
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177 if avg1 == 0 and avg2 == 0:
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178 return 0
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179 elif avg1 == 0:
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180 return '-INF'
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181 elif avg2 == 0:
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182 return 'INF'
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183 else: # (threshold_F_C - 1) / (abs(threshold_F_C) + 1) con threshold_F_C > 1
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184 return (avg1 - avg2) / (abs(avg1) + abs(avg2))
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185
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186 def fix_style(l :str, col :Optional[str], width :str, dash :str) -> str:
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187 """
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188 Produces a "fixed" style string to assign to a reaction arrow in the SVG map, assigning style properties to the corresponding values passed as input params.
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189
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190 Args:
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191 l : current style string of an SVG element
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192 col : new value for the "stroke" style property
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193 width : new value for the "stroke-width" style property
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194 dash : new value for the "stroke-dasharray" style property
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195
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196 Returns:
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197 str : the fixed style string
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198 """
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199 tmp = l.split(';')
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200 flag_col = False
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201 flag_width = False
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202 flag_dash = False
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203 for i in range(len(tmp)):
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204 if tmp[i].startswith('stroke:'):
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205 tmp[i] = 'stroke:' + col
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206 flag_col = True
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207 if tmp[i].startswith('stroke-width:'):
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208 tmp[i] = 'stroke-width:' + width
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209 flag_width = True
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210 if tmp[i].startswith('stroke-dasharray:'):
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211 tmp[i] = 'stroke-dasharray:' + dash
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212 flag_dash = True
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213 if not flag_col:
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214 tmp.append('stroke:' + col)
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215 if not flag_width:
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216 tmp.append('stroke-width:' + width)
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217 if not flag_dash:
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218 tmp.append('stroke-dasharray:' + dash)
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219 return ';'.join(tmp)
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220
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221 # The type of d values is collapsed, losing precision, because the dict containst lists instead of tuples, please fix!
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222 def fix_map(d :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, threshold_P_V :float, threshold_F_C :float, max_z_score :float) -> ET.ElementTree:
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223 """
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224 Edits the selected SVG map based on the p-value and fold change data (d) and some significance thresholds also passed as inputs.
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225
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226 Args:
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227 d : dictionary mapping a p-value and a fold-change value (values) to each reaction ID as encoded in the SVG map (keys)
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228 core_map : SVG map to modify
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229 threshold_P_V : threshold for a p-value to be considered significant
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230 threshold_F_C : threshold for a fold change value to be considered significant
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231 max_z_score : highest z-score (absolute value)
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232
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233 Returns:
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234 ET.ElementTree : the modified core_map
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235
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236 Side effects:
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237 core_map : mut
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238 """
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239 maxT = 12
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240 minT = 2
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241 grey = '#BEBEBE'
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242 blue = '#6495ed'
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243 red = '#ecac68'
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244 for el in core_map.iter():
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245 el_id = str(el.get('id'))
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246 if el_id.startswith('R_'):
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247 tmp = d.get(el_id[2:])
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248 if tmp != None:
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249 p_val :float = tmp[0]
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250 f_c = tmp[1]
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251 z_score = tmp[2]
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252 if p_val < threshold_P_V:
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253 if not isinstance(f_c, str):
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254 if abs(f_c) < ((threshold_F_C - 1) / (abs(threshold_F_C) + 1)): #
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255 col = grey
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256 width = str(minT)
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257 else:
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258 if f_c < 0:
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259 col = blue
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260 elif f_c > 0:
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261 col = red
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262 width = str(max((abs(z_score) * maxT) / max_z_score, minT))
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263 else:
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264 if f_c == '-INF':
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265 col = blue
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266 elif f_c == 'INF':
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267 col = red
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268 width = str(maxT)
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269 dash = 'none'
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270 else:
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271 dash = '5,5'
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272 col = grey
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273 width = str(minT)
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274 el.set('style', fix_style(el.get('style', ""), col, width, dash))
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275 return core_map
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276
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277 def getElementById(reactionId :str, metabMap :ET.ElementTree) -> utils.Result[ET.Element, utils.Result.ResultErr]:
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278 """
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279 Finds any element in the given map with the given ID. ID uniqueness in an svg file is recommended but
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280 not enforced, if more than one element with the exact ID is found only the first will be returned.
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281
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282 Args:
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283 reactionId (str): exact ID of the requested element.
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284 metabMap (ET.ElementTree): metabolic map containing the element.
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285
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286 Returns:
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287 utils.Result[ET.Element, ResultErr]: result of the search, either the first match found or a ResultErr.
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288 """
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289 return utils.Result.Ok(
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290 f"//*[@id=\"{reactionId}\"]").map(
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291 lambda xPath : metabMap.xpath(xPath)[0]).mapErr(
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292 lambda _ : utils.Result.ResultErr(f"No elements with ID \"{reactionId}\" found in map"))
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293 # ^^^ we shamelessly ignore the contents of the IndexError, it offers nothing to the user.
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294
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295 def styleMapElement(element :ET.Element, styleStr :str) -> None:
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296 currentStyles :str = element.get("style", "")
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297 if re.search(r";stroke:[^;]+;stroke-width:[^;]+;stroke-dasharray:[^;]+$", currentStyles):
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298 currentStyles = ';'.join(currentStyles.split(';')[:-3])
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299
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300 element.set("style", currentStyles + styleStr)
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301
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302 class ReactionDirection(Enum):
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303 Unknown = ""
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304 Direct = "_F"
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305 Inverse = "_B"
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306
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307 @classmethod
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308 def fromDir(cls, s :str) -> "ReactionDirection":
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309 # vvv as long as there's so few variants I actually condone the if spam:
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310 if s == ReactionDirection.Direct.value: return ReactionDirection.Direct
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311 if s == ReactionDirection.Inverse.value: return ReactionDirection.Inverse
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312 return ReactionDirection.Unknown
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313
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314 @classmethod
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315 def fromReactionId(cls, reactionId :str) -> "ReactionDirection":
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316 return ReactionDirection.fromDir(reactionId[-2:])
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317
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318 def getArrowBodyElementId(reactionId :str) -> str:
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319 if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV
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320 elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: reactionId = reactionId[:-2]
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321 return f"R_{reactionId}"
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322
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323 def getArrowHeadElementId(reactionId :str) -> Tuple[str, str]:
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324 """
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325 We attempt extracting the direction information from the provided reaction ID, if unsuccessful we provide the IDs of both directions.
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326
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327 Args:
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328 reactionId : the provided reaction ID.
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329
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330 Returns:
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331 Tuple[str, str]: either a single str ID for the correct arrow head followed by an empty string or both options to try.
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332 """
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333 if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV
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334 elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: return reactionId[:-3:-1] + reactionId[:-2], ""
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335 return f"F_{reactionId}", f"B_{reactionId}"
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336
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337 class ArrowColor(Enum):
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338 """
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339 Encodes possible arrow colors based on their meaning in the enrichment process.
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340 """
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341 Invalid = "#BEBEBE" # gray, fold-change under treshold
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222
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342 Transparent = "#ffffff00" # white, not significant p-value
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193
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343 UpRegulated = "#ecac68" # red, up-regulated reaction
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344 DownRegulated = "#6495ed" # blue, down-regulated reaction
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207
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345
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193
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346 UpRegulatedInv = "#FF0000"
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347 # ^^^ different shade of red (actually orange), up-regulated net value for a reversible reaction with
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348 # conflicting enrichment in the two directions.
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349
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350 DownRegulatedInv = "#0000FF"
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351 # ^^^ different shade of blue (actually purple), down-regulated net value for a reversible reaction with
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352 # conflicting enrichment in the two directions.
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353
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354 @classmethod
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355 def fromFoldChangeSign(cls, foldChange :float, *, useAltColor = False) -> "ArrowColor":
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356 colors = (cls.DownRegulated, cls.DownRegulatedInv) if foldChange < 0 else (cls.UpRegulated, cls.UpRegulatedInv)
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357 return colors[useAltColor]
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358
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359 def __str__(self) -> str: return self.value
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360
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361 class Arrow:
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362 """
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363 Models the properties of a reaction arrow that change based on enrichment.
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364 """
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365 MIN_W = 2
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366 MAX_W = 12
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367
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368 def __init__(self, width :int, col: ArrowColor, *, isDashed = False) -> None:
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369 """
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370 (Private) Initializes an instance of Arrow.
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371
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372 Args:
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373 width : width of the arrow, ideally to be kept within Arrow.MIN_W and Arrow.MAX_W (not enforced).
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374 col : color of the arrow.
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375 isDashed : whether the arrow should be dashed, meaning the associated pValue resulted not significant.
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376
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377 Returns:
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378 None : practically, a Arrow instance.
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379 """
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380 self.w = width
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381 self.col = col
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382 self.dash = isDashed
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383
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384 def applyTo(self, reactionId :str, metabMap :ET.ElementTree, styleStr :str) -> None:
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385 if getElementById(reactionId, metabMap).map(lambda el : styleMapElement(el, styleStr)).isErr:
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386 ERRORS.append(reactionId)
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387
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388 def styleReactionElements(self, metabMap :ET.ElementTree, reactionId :str, *, mindReactionDir = True) -> None:
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389 if not mindReactionDir:
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390 return self.applyTo(getArrowBodyElementId(reactionId), metabMap, self.toStyleStr())
|
|
|
391
|
|
|
392 # Now we style the arrow head(s):
|
|
|
393 idOpt1, idOpt2 = getArrowHeadElementId(reactionId)
|
|
|
394 self.applyTo(idOpt1, metabMap, self.toStyleStr(downSizedForTips = True))
|
|
211
|
395 if idOpt2: self.applyTo(idOpt2, metabMap, self.toStyleStr(downSizedForTips = True))
|
|
214
|
396
|
|
|
397
|
|
193
|
398
|
|
|
399 def getMapReactionId(self, reactionId :str, mindReactionDir :bool) -> str:
|
|
|
400 """
|
|
|
401 Computes the reaction ID as encoded in the map for a given reaction ID from the dataset.
|
|
|
402
|
|
|
403 Args:
|
|
|
404 reactionId: the reaction ID, as encoded in the dataset.
|
|
|
405 mindReactionDir: if True forward (F_) and backward (B_) directions will be encoded in the result.
|
|
|
406
|
|
|
407 Returns:
|
|
|
408 str : the ID of an arrow's body or tips in the map.
|
|
|
409 """
|
|
|
410 # we assume the reactionIds also don't encode reaction dir if they don't mind it when styling the map.
|
|
|
411 if not mindReactionDir: return "R_" + reactionId
|
|
|
412
|
|
241
|
413 #TODO: this is clearly something we need to make consistent in fluxes
|
|
193
|
414 return (reactionId[:-3:-1] + reactionId[:-2]) if reactionId[:-2] in ["_F", "_B"] else f"F_{reactionId}" # "Pyr_F" --> "F_Pyr"
|
|
|
415
|
|
|
416 def toStyleStr(self, *, downSizedForTips = False) -> str:
|
|
|
417 """
|
|
|
418 Collapses the styles of this Arrow into a str, ready to be applied as part of the "style" property on an svg element.
|
|
|
419
|
|
|
420 Returns:
|
|
|
421 str : the styles string.
|
|
|
422 """
|
|
|
423 width = self.w
|
|
|
424 if downSizedForTips: width *= 0.8
|
|
|
425 return f";stroke:{self.col};stroke-width:{width};stroke-dasharray:{'5,5' if self.dash else 'none'}"
|
|
|
426
|
|
|
427 # vvv These constants could be inside the class itself a static properties, but python
|
|
|
428 # was built by brainless organisms so here we are!
|
|
|
429 INVALID_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid)
|
|
|
430 INSIGNIFICANT_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid, isDashed = True)
|
|
207
|
431
|
|
241
|
432 def applyFluxesEnrichmentToMap(fluxesEnrichmentRes :Dict[str, Union[Tuple[float, FoldChange], Tuple[float, FoldChange, float, float]]], metabMap :ET.ElementTree, maxNumericZScore :float) -> None:
|
|
193
|
433 """
|
|
241
|
434 Applies fluxes enrichment results to the provided metabolic map.
|
|
193
|
435
|
|
|
436 Args:
|
|
241
|
437 fluxesEnrichmentRes : fluxes enrichment results.
|
|
193
|
438 metabMap : the metabolic map to edit.
|
|
|
439 maxNumericZScore : biggest finite z-score value found.
|
|
|
440
|
|
|
441 Side effects:
|
|
|
442 metabMap : mut
|
|
|
443
|
|
|
444 Returns:
|
|
|
445 None
|
|
|
446 """
|
|
241
|
447 for reactionId, values in fluxesEnrichmentRes.items():
|
|
193
|
448 pValue = values[0]
|
|
|
449 foldChange = values[1]
|
|
|
450 z_score = values[2]
|
|
222
|
451
|
|
193
|
452 if isinstance(foldChange, str): foldChange = float(foldChange)
|
|
|
453 if pValue >= ARGS.pValue: # pValue above tresh: dashed arrow
|
|
|
454 INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId)
|
|
211
|
455 INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId, mindReactionDir = False)
|
|
222
|
456
|
|
193
|
457 continue
|
|
|
458
|
|
|
459 if abs(foldChange) < (ARGS.fChange - 1) / (abs(ARGS.fChange) + 1):
|
|
|
460 INVALID_ARROW.styleReactionElements(metabMap, reactionId)
|
|
211
|
461 INVALID_ARROW.styleReactionElements(metabMap, reactionId, mindReactionDir = False)
|
|
222
|
462
|
|
193
|
463 continue
|
|
|
464
|
|
|
465 width = Arrow.MAX_W
|
|
|
466 if not math.isinf(foldChange):
|
|
196
|
467 try:
|
|
|
468 width = max(abs(z_score * Arrow.MAX_W) / maxNumericZScore, Arrow.MIN_W)
|
|
222
|
469
|
|
193
|
470 except ZeroDivisionError: pass
|
|
|
471
|
|
203
|
472 #if not reactionId.endswith("_RV"): # RV stands for reversible reactions
|
|
|
473 # Arrow(width, ArrowColor.fromFoldChangeSign(foldChange)).styleReactionElements(metabMap, reactionId)
|
|
|
474 # continue
|
|
193
|
475
|
|
195
|
476 #reactionId = reactionId[:-3] # Remove "_RV"
|
|
193
|
477
|
|
|
478 inversionScore = (values[3] < 0) + (values[4] < 0) # Compacts the signs of averages into 1 easy to check score
|
|
|
479 if inversionScore == 2: foldChange *= -1
|
|
|
480 # ^^^ Style the inverse direction with the opposite sign netValue
|
|
|
481
|
|
|
482 # If the score is 1 (opposite signs) we use alternative colors vvv
|
|
|
483 arrow = Arrow(width, ArrowColor.fromFoldChangeSign(foldChange, useAltColor = inversionScore == 1))
|
|
|
484
|
|
|
485 # vvv These 2 if statements can both be true and can both happen
|
|
|
486 if ARGS.net: # style arrow head(s):
|
|
|
487 arrow.styleReactionElements(metabMap, reactionId + ("_B" if inversionScore == 2 else "_F"))
|
|
222
|
488 arrow.applyTo(("F_" if inversionScore == 2 else "B_") + reactionId, metabMap, f";stroke:{ArrowColor.Transparent};stroke-width:0;stroke-dasharray:None")
|
|
244
|
489
|
|
|
490 arrow.styleReactionElements(metabMap, reactionId, mindReactionDir = False)
|
|
222
|
491
|
|
193
|
492
|
|
|
493 ############################ split class ######################################
|
|
|
494 def split_class(classes :pd.DataFrame, resolve_rules :Dict[str, List[float]]) -> Dict[str, List[List[float]]]:
|
|
|
495 """
|
|
|
496 Generates a :dict that groups together data from a :DataFrame based on classes the data is related to.
|
|
|
497
|
|
|
498 Args:
|
|
|
499 classes : a :DataFrame of only string values, containing class information (rows) and keys to query the resolve_rules :dict
|
|
|
500 resolve_rules : a :dict containing :float data
|
|
|
501
|
|
|
502 Returns:
|
|
|
503 dict : the dict with data grouped by class
|
|
|
504
|
|
|
505 Side effects:
|
|
|
506 classes : mut
|
|
|
507 """
|
|
|
508 class_pat :Dict[str, List[List[float]]] = {}
|
|
|
509 for i in range(len(classes)):
|
|
|
510 classe :str = classes.iloc[i, 1]
|
|
|
511 if pd.isnull(classe): continue
|
|
|
512
|
|
|
513 l :List[List[float]] = []
|
|
|
514 for j in range(i, len(classes)):
|
|
|
515 if classes.iloc[j, 1] == classe:
|
|
|
516 pat_id :str = classes.iloc[j, 0]
|
|
|
517 tmp = resolve_rules.get(pat_id, None)
|
|
|
518 if tmp != None:
|
|
|
519 l.append(tmp)
|
|
|
520 classes.iloc[j, 1] = None
|
|
|
521
|
|
|
522 if l:
|
|
|
523 class_pat[classe] = list(map(list, zip(*l)))
|
|
|
524 continue
|
|
|
525
|
|
|
526 utils.logWarning(
|
|
|
527 f"Warning: no sample found in class \"{classe}\", the class has been disregarded", ARGS.out_log)
|
|
|
528
|
|
|
529 return class_pat
|
|
|
530
|
|
|
531 ############################ conversion ##############################################
|
|
|
532 #conversion from svg to png
|
|
|
533 def svg_to_png_with_background(svg_path :utils.FilePath, png_path :utils.FilePath, dpi :int = 72, scale :int = 1, size :Optional[float] = None) -> None:
|
|
|
534 """
|
|
|
535 Internal utility to convert an SVG to PNG (forced opaque) to aid in PDF conversion.
|
|
|
536
|
|
|
537 Args:
|
|
|
538 svg_path : path to SVG file
|
|
|
539 png_path : path for new PNG file
|
|
|
540 dpi : dots per inch of the generated PNG
|
|
|
541 scale : scaling factor for the generated PNG, computed internally when a size is provided
|
|
|
542 size : final effective width of the generated PNG
|
|
|
543
|
|
|
544 Returns:
|
|
|
545 None
|
|
|
546 """
|
|
|
547 if size:
|
|
|
548 image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=1)
|
|
|
549 scale = size / image.width
|
|
|
550 image = image.resize(scale)
|
|
|
551 else:
|
|
|
552 image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=scale)
|
|
|
553
|
|
|
554 white_background = pyvips.Image.black(image.width, image.height).new_from_image([255, 255, 255])
|
|
|
555 white_background = white_background.affine([scale, 0, 0, scale])
|
|
|
556
|
|
|
557 if white_background.bands != image.bands:
|
|
|
558 white_background = white_background.extract_band(0)
|
|
|
559
|
|
|
560 composite_image = white_background.composite2(image, 'over')
|
|
|
561 composite_image.write_to_file(png_path.show())
|
|
|
562
|
|
|
563 #funzione unica, lascio fuori i file e li passo in input
|
|
|
564 #conversion from png to pdf
|
|
|
565 def convert_png_to_pdf(png_file :utils.FilePath, pdf_file :utils.FilePath) -> None:
|
|
|
566 """
|
|
|
567 Internal utility to convert a PNG to PDF to aid from SVG conversion.
|
|
|
568
|
|
|
569 Args:
|
|
|
570 png_file : path to PNG file
|
|
|
571 pdf_file : path to new PDF file
|
|
|
572
|
|
|
573 Returns:
|
|
|
574 None
|
|
|
575 """
|
|
|
576 image = Image.open(png_file.show())
|
|
|
577 image = image.convert("RGB")
|
|
|
578 image.save(pdf_file.show(), "PDF", resolution=100.0)
|
|
|
579
|
|
|
580 #function called to reduce redundancy in the code
|
|
|
581 def convert_to_pdf(file_svg :utils.FilePath, file_png :utils.FilePath, file_pdf :utils.FilePath) -> None:
|
|
|
582 """
|
|
|
583 Converts the SVG map at the provided path to PDF.
|
|
|
584
|
|
|
585 Args:
|
|
|
586 file_svg : path to SVG file
|
|
|
587 file_png : path to PNG file
|
|
|
588 file_pdf : path to new PDF file
|
|
|
589
|
|
|
590 Returns:
|
|
|
591 None
|
|
|
592 """
|
|
|
593 svg_to_png_with_background(file_svg, file_png)
|
|
|
594 try:
|
|
|
595 convert_png_to_pdf(file_png, file_pdf)
|
|
|
596 print(f'PDF file {file_pdf.filePath} successfully generated.')
|
|
|
597
|
|
|
598 except Exception as e:
|
|
|
599 raise utils.DataErr(file_pdf.show(), f'Error generating PDF file: {e}')
|
|
|
600
|
|
|
601 ############################ map ##############################################
|
|
|
602 def buildOutputPath(dataset1Name :str, dataset2Name = "rest", *, details = "", ext :utils.FileFormat) -> utils.FilePath:
|
|
|
603 """
|
|
|
604 Builds a FilePath instance from the names of confronted datasets ready to point to a location in the
|
|
|
605 "result/" folder, used by this tool for output files in collections.
|
|
|
606
|
|
|
607 Args:
|
|
|
608 dataset1Name : _description_
|
|
|
609 dataset2Name : _description_. Defaults to "rest".
|
|
|
610 details : _description_
|
|
|
611 ext : _description_
|
|
|
612
|
|
|
613 Returns:
|
|
|
614 utils.FilePath : _description_
|
|
|
615 """
|
|
|
616 # This function returns a util data structure but is extremely specific to this module.
|
|
|
617 # RAS also uses collections as output and as such might benefit from a method like this, but I'd wait
|
|
|
618 # TODO: until a third tool with multiple outputs appears before porting this to utils.
|
|
|
619 return utils.FilePath(
|
|
|
620 f"{dataset1Name}_vs_{dataset2Name}" + (f" ({details})" if details else ""),
|
|
|
621 # ^^^ yes this string is built every time even if the form is the same for the same 2 datasets in
|
|
|
622 # all output files: I don't care, this was never the performance bottleneck of the tool and
|
|
|
623 # there is no other net gain in saving and re-using the built string.
|
|
|
624 ext,
|
|
|
625 prefix = "result")
|
|
|
626
|
|
|
627 FIELD_NOT_AVAILABLE = '/'
|
|
|
628 def writeToCsv(rows: List[list], fieldNames :List[str], outPath :utils.FilePath) -> None:
|
|
|
629 fieldsAmt = len(fieldNames)
|
|
|
630 with open(outPath.show(), "w", newline = "") as fd:
|
|
|
631 writer = csv.DictWriter(fd, fieldnames = fieldNames, delimiter = '\t')
|
|
|
632 writer.writeheader()
|
|
|
633
|
|
|
634 for row in rows:
|
|
|
635 sizeMismatch = fieldsAmt - len(row)
|
|
|
636 if sizeMismatch > 0: row.extend([FIELD_NOT_AVAILABLE] * sizeMismatch)
|
|
|
637 writer.writerow({ field : data for field, data in zip(fieldNames, row) })
|
|
|
638
|
|
|
639 OldEnrichedScores = Dict[str, List[Union[float, FoldChange]]] #TODO: try to use Tuple whenever possible
|
|
244
|
640 def writeTabularResult(enrichedScores : OldEnrichedScores, outPath :utils.FilePath) -> None:
|
|
193
|
641 fieldNames = ["ids", "P_Value", "fold change"]
|
|
244
|
642 fieldNames.extend(["average_1", "average_2"])
|
|
193
|
643
|
|
|
644 writeToCsv([ [reactId] + values for reactId, values in enrichedScores.items() ], fieldNames, outPath)
|
|
|
645
|
|
244
|
646 def temp_thingsInCommon(tmp :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, max_z_score :float, dataset1Name :str, dataset2Name = "rest") -> None:
|
|
193
|
647 # this function compiles the things always in common between comparison modes after enrichment.
|
|
|
648 # TODO: organize, name better.
|
|
244
|
649 writeTabularResult(tmp, buildOutputPath(dataset1Name, dataset2Name, details = "Tabular Result", ext = utils.FileFormat.TSV))
|
|
193
|
650 for reactId, enrichData in tmp.items(): tmp[reactId] = tuple(enrichData)
|
|
241
|
651 applyFluxesEnrichmentToMap(tmp, core_map, max_z_score)
|
|
193
|
652
|
|
|
653 def computePValue(dataset1Data: List[float], dataset2Data: List[float]) -> Tuple[float, float]:
|
|
|
654 """
|
|
|
655 Computes the statistical significance score (P-value) of the comparison between coherent data
|
|
|
656 from two datasets. The data is supposed to, in both datasets:
|
|
|
657 - be related to the same reaction ID;
|
|
|
658 - be ordered by sample, such that the item at position i in both lists is related to the
|
|
|
659 same sample or cell line.
|
|
|
660
|
|
|
661 Args:
|
|
|
662 dataset1Data : data from the 1st dataset.
|
|
|
663 dataset2Data : data from the 2nd dataset.
|
|
|
664
|
|
|
665 Returns:
|
|
|
666 tuple: (P-value, Z-score)
|
|
|
667 - P-value from a Kolmogorov-Smirnov test on the provided data.
|
|
|
668 - Z-score of the difference between means of the two datasets.
|
|
|
669 """
|
|
|
670 # Perform Kolmogorov-Smirnov test
|
|
|
671 ks_statistic, p_value = st.ks_2samp(dataset1Data, dataset2Data)
|
|
|
672
|
|
|
673 # Calculate means and standard deviations
|
|
|
674 mean1 = np.mean(dataset1Data)
|
|
|
675 mean2 = np.mean(dataset2Data)
|
|
|
676 std1 = np.std(dataset1Data, ddof=1)
|
|
|
677 std2 = np.std(dataset2Data, ddof=1)
|
|
|
678
|
|
|
679 n1 = len(dataset1Data)
|
|
|
680 n2 = len(dataset2Data)
|
|
|
681
|
|
|
682 # Calculate Z-score
|
|
|
683 z_score = (mean1 - mean2) / np.sqrt((std1**2 / n1) + (std2**2 / n2))
|
|
|
684
|
|
|
685 return p_value, z_score
|
|
|
686
|
|
|
687 def compareDatasetPair(dataset1Data :List[List[float]], dataset2Data :List[List[float]], ids :List[str]) -> Tuple[Dict[str, List[Union[float, FoldChange]]], float]:
|
|
|
688 #TODO: the following code still suffers from "dumbvarnames-osis"
|
|
|
689 tmp :Dict[str, List[Union[float, FoldChange]]] = {}
|
|
|
690 count = 0
|
|
|
691 max_z_score = 0
|
|
|
692
|
|
|
693 for l1, l2 in zip(dataset1Data, dataset2Data):
|
|
|
694 reactId = ids[count]
|
|
|
695 count += 1
|
|
|
696 if not reactId: continue # we skip ids that have already been processed
|
|
|
697
|
|
|
698 try:
|
|
|
699 p_value, z_score = computePValue(l1, l2)
|
|
204
|
700 avg1 = sum(l1) / len(l1)
|
|
|
701 avg2 = sum(l2) / len(l2)
|
|
|
702 avg = fold_change(avg1, avg2)
|
|
193
|
703 if not isinstance(z_score, str) and max_z_score < abs(z_score): max_z_score = abs(z_score)
|
|
204
|
704 tmp[reactId] = [float(p_value), avg, z_score, avg1, avg2]
|
|
193
|
705 except (TypeError, ZeroDivisionError): continue
|
|
|
706
|
|
|
707 return tmp, max_z_score
|
|
|
708
|
|
244
|
709 def computeEnrichment(metabMap :ET.ElementTree, class_pat :Dict[str, List[List[float]]], ids :List[str]) -> None:
|
|
193
|
710 """
|
|
|
711 Compares clustered data based on a given comparison mode and applies enrichment-based styling on the
|
|
|
712 provided metabolic map.
|
|
|
713
|
|
|
714 Args:
|
|
|
715 metabMap : SVG map to modify.
|
|
|
716 class_pat : the clustered data.
|
|
|
717 ids : ids for data association.
|
|
244
|
718
|
|
193
|
719
|
|
|
720 Returns:
|
|
|
721 None
|
|
|
722
|
|
|
723 Raises:
|
|
|
724 sys.exit : if there are less than 2 classes for comparison
|
|
|
725
|
|
|
726 Side effects:
|
|
|
727 metabMap : mut
|
|
|
728 ids : mut
|
|
|
729 """
|
|
|
730 class_pat = { k.strip() : v for k, v in class_pat.items() }
|
|
|
731 #TODO: simplfy this stuff vvv and stop using sys.exit (raise the correct utils error)
|
|
|
732 if (not class_pat) or (len(class_pat.keys()) < 2): sys.exit('Execution aborted: classes provided for comparisons are less than two\n')
|
|
|
733
|
|
|
734 if ARGS.comparison == "manyvsmany":
|
|
|
735 for i, j in it.combinations(class_pat.keys(), 2):
|
|
|
736 #TODO: these 2 functions are always called in pair and in this order and need common data,
|
|
|
737 # some clever refactoring would be appreciated.
|
|
|
738 comparisonDict, max_z_score = compareDatasetPair(class_pat.get(i), class_pat.get(j), ids)
|
|
244
|
739 temp_thingsInCommon(comparisonDict, metabMap, max_z_score, i, j)
|
|
193
|
740
|
|
|
741 elif ARGS.comparison == "onevsrest":
|
|
|
742 for single_cluster in class_pat.keys():
|
|
|
743 t :List[List[List[float]]] = []
|
|
|
744 for k in class_pat.keys():
|
|
|
745 if k != single_cluster:
|
|
|
746 t.append(class_pat.get(k))
|
|
|
747
|
|
|
748 rest :List[List[float]] = []
|
|
|
749 for i in t:
|
|
|
750 rest = rest + i
|
|
|
751
|
|
|
752 comparisonDict, max_z_score = compareDatasetPair(class_pat.get(single_cluster), rest, ids)
|
|
244
|
753 temp_thingsInCommon(comparisonDict, metabMap, max_z_score, single_cluster)
|
|
193
|
754
|
|
|
755 elif ARGS.comparison == "onevsmany":
|
|
|
756 controlItems = class_pat.get(ARGS.control)
|
|
|
757 for otherDataset in class_pat.keys():
|
|
|
758 if otherDataset == ARGS.control: continue
|
|
|
759
|
|
|
760 comparisonDict, max_z_score = compareDatasetPair(controlItems, class_pat.get(otherDataset), ids)
|
|
244
|
761 temp_thingsInCommon(comparisonDict, metabMap, max_z_score, ARGS.control, otherDataset)
|
|
193
|
762
|
|
|
763 def createOutputMaps(dataset1Name :str, dataset2Name :str, core_map :ET.ElementTree) -> None:
|
|
|
764 svgFilePath = buildOutputPath(dataset1Name, dataset2Name, details = "SVG Map", ext = utils.FileFormat.SVG)
|
|
|
765 utils.writeSvg(svgFilePath, core_map)
|
|
|
766
|
|
|
767 if ARGS.generate_pdf:
|
|
|
768 pngPath = buildOutputPath(dataset1Name, dataset2Name, details = "PNG Map", ext = utils.FileFormat.PNG)
|
|
|
769 pdfPath = buildOutputPath(dataset1Name, dataset2Name, details = "PDF Map", ext = utils.FileFormat.PDF)
|
|
|
770 convert_to_pdf(svgFilePath, pngPath, pdfPath)
|
|
|
771
|
|
|
772 if not ARGS.generate_svg: os.remove(svgFilePath.show())
|
|
|
773
|
|
|
774 ClassPat = Dict[str, List[List[float]]]
|
|
|
775 def getClassesAndIdsFromDatasets(datasetsPaths :List[str], datasetPath :str, classPath :str, names :List[str]) -> Tuple[List[str], ClassPat]:
|
|
|
776 # TODO: I suggest creating dicts with ids as keys instead of keeping class_pat and ids separate,
|
|
|
777 # for the sake of everyone's sanity.
|
|
|
778 class_pat :ClassPat = {}
|
|
|
779 if ARGS.option == 'datasets':
|
|
|
780 num = 1 #TODO: the dataset naming function could be a generator
|
|
|
781 for path, name in zip(datasetsPaths, names):
|
|
|
782 name = name_dataset(name, num)
|
|
|
783 resolve_rules_float, ids = getDatasetValues(path, name)
|
|
|
784 if resolve_rules_float != None:
|
|
|
785 class_pat[name] = list(map(list, zip(*resolve_rules_float.values())))
|
|
|
786
|
|
|
787 num += 1
|
|
|
788
|
|
|
789 elif ARGS.option == "dataset_class":
|
|
|
790 classes = read_dataset(classPath, "class")
|
|
|
791 classes = classes.astype(str)
|
|
|
792
|
|
|
793 resolve_rules_float, ids = getDatasetValues(datasetPath, "Dataset Class (not actual name)")
|
|
|
794 if resolve_rules_float != None: class_pat = split_class(classes, resolve_rules_float)
|
|
|
795
|
|
|
796 return ids, class_pat
|
|
|
797 #^^^ TODO: this could be a match statement over an enum, make it happen future marea dev with python 3.12! (it's why I kept the ifs)
|
|
|
798
|
|
|
799 #TODO: create these damn args as FilePath objects
|
|
|
800 def getDatasetValues(datasetPath :str, datasetName :str) -> Tuple[ClassPat, List[str]]:
|
|
|
801 """
|
|
|
802 Opens the dataset at the given path and extracts the values (expected nullable numerics) and the IDs.
|
|
|
803
|
|
|
804 Args:
|
|
|
805 datasetPath : path to the dataset
|
|
|
806 datasetName (str): dataset name, used in error reporting
|
|
|
807
|
|
|
808 Returns:
|
|
|
809 Tuple[ClassPat, List[str]]: values and IDs extracted from the dataset
|
|
|
810 """
|
|
|
811 dataset = read_dataset(datasetPath, datasetName)
|
|
|
812 IDs = pd.Series.tolist(dataset.iloc[:, 0].astype(str))
|
|
|
813
|
|
|
814 dataset = dataset.drop(dataset.columns[0], axis = "columns").to_dict("list")
|
|
|
815 return { id : list(map(utils.Float("Dataset values, not an argument"), values)) for id, values in dataset.items() }, IDs
|
|
|
816
|
|
249
|
817 def rgba_to_hex(rgba):
|
|
|
818 """Convert an RGBA color to HEX format."""
|
|
|
819 return '#{:02x}{:02x}{:02x}'.format(int(rgba[0] * 255), int(rgba[1] * 255), int(rgba[2] * 255))
|
|
|
820
|
|
246
|
821 def computeEnrichmentMedoids(metabMap :ET.ElementTree, class_pat :Dict[str, List[List[float]]], ids :List[str]) -> None:
|
|
249
|
822
|
|
|
823 metabMap_mean = metabMap.copy()
|
|
|
824 metabMap_median = metabMap.copy()
|
|
|
825
|
|
248
|
826 #class_pat 462 * cellule
|
|
246
|
827 utils.logWarning(
|
|
248
|
828 ids,
|
|
246
|
829 ARGS.out_log)
|
|
248
|
830 medians = {}
|
|
|
831 means = {}
|
|
|
832 for key, value in class_pat.items():
|
|
|
833 values = np.array(class_pat[key])
|
|
|
834 median = np.median(values, axis=1)
|
|
|
835 mean = np.mean(values, axis=1)
|
|
|
836 medians[key] = median
|
|
|
837 means[key] = means
|
|
|
838
|
|
249
|
839 max_flux_medians = max(np.max(np.abs(arr)) for arr in medians.values())
|
|
|
840 max_flux_means = max(np.max(np.abs(arr)) for arr in means.values())
|
|
248
|
841
|
|
|
842 for key, value in medians.items():
|
|
|
843 medians[key] = medians[key] / max_flux_medians
|
|
247
|
844
|
|
248
|
845 for key, value in means.items():
|
|
|
846 means[key] = means[key] / max_flux_means
|
|
|
847
|
|
249
|
848 cmap = plt.cm.Reds
|
|
|
849 norm = plt.Normalize(vmin=0, vmax=1)
|
|
248
|
850
|
|
249
|
851 colors_median_rgb = {k: cmap(norm(v)) for k, v in medians.items()}
|
|
|
852 colors_median = {k: rgba_to_hex(c) for k, c in colors_median_rgb.items()}
|
|
248
|
853
|
|
249
|
854 colors_mean_rgb = {k: cmap(norm(v)) for k, v in means.items()}
|
|
|
855 colors_mean = {k: rgba_to_hex(c) for k, c in colors_mean_rgb.items()}
|
|
248
|
856
|
|
249
|
857 for rxn_id in ids:
|
|
|
858 arrow = Arrow(width=5, col=colors_median[rxn_id])
|
|
|
859 arrow.applyTo(rxn_id, metabMap_median, arrow.toStyleStr())
|
|
|
860
|
|
|
861 svgFilePath = utils.FilePath(details = "SVG Map median", ext = utils.FileFormat.SVG, prefix="result")
|
|
|
862 utils.writeSvg(svgFilePath, metabMap_median)
|
|
247
|
863 pass
|
|
|
864
|
|
248
|
865
|
|
193
|
866 ############################ MAIN #############################################
|
|
|
867 def main() -> None:
|
|
|
868 """
|
|
|
869 Initializes everything and sets the program in motion based on the fronted input arguments.
|
|
|
870
|
|
|
871 Returns:
|
|
|
872 None
|
|
|
873
|
|
|
874 Raises:
|
|
|
875 sys.exit : if a user-provided custom map is in the wrong format (ET.XMLSyntaxError, ET.XMLSchemaParseError)
|
|
|
876 """
|
|
|
877
|
|
|
878 global ARGS
|
|
|
879 ARGS = process_args()
|
|
|
880
|
|
|
881 if os.path.isdir('result') == False: os.makedirs('result')
|
|
|
882
|
|
|
883 core_map :ET.ElementTree = ARGS.choice_map.getMap(
|
|
|
884 ARGS.tool_dir,
|
|
|
885 utils.FilePath.fromStrPath(ARGS.custom_map) if ARGS.custom_map else None)
|
|
|
886 # TODO: ^^^ ugly but fine for now, the argument is None if the model isn't custom because no file was given.
|
|
|
887 # getMap will None-check the customPath and panic when the model IS custom but there's no file (good). A cleaner
|
|
|
888 # solution can be derived from my comment in FilePath.fromStrPath
|
|
|
889
|
|
241
|
890 ids, class_pat = getClassesAndIdsFromDatasets(ARGS.input_datas_fluxes, ARGS.input_data_fluxes, ARGS.input_class_fluxes, ARGS.names_fluxes)
|
|
246
|
891
|
|
|
892 computeEnrichmentMedoids(core_map, class_pat, ids)
|
|
|
893
|
|
|
894 pass
|
|
|
895
|
|
244
|
896 computeEnrichment(core_map, class_pat, ids)
|
|
193
|
897
|
|
|
898 # create output files: TODO: this is the same comparison happening in "maps", find a better way to organize this
|
|
|
899 if ARGS.comparison == "manyvsmany":
|
|
|
900 for i, j in it.combinations(class_pat.keys(), 2): createOutputMaps(i, j, core_map)
|
|
|
901 return
|
|
|
902
|
|
|
903 if ARGS.comparison == "onevsrest":
|
|
|
904 for single_cluster in class_pat.keys(): createOutputMaps(single_cluster, "rest", core_map)
|
|
|
905 return
|
|
|
906
|
|
|
907 for otherDataset in class_pat.keys():
|
|
|
908 if otherDataset != ARGS.control: createOutputMaps(i, j, core_map)
|
|
199
|
909
|
|
193
|
910 if not ERRORS: return
|
|
|
911 utils.logWarning(
|
|
|
912 f"The following reaction IDs were mentioned in the dataset but weren't found in the map: {ERRORS}",
|
|
|
913 ARGS.out_log)
|
|
|
914
|
|
|
915 print('Execution succeded')
|
|
|
916
|
|
|
917 ###############################################################################
|
|
|
918 if __name__ == "__main__":
|
|
|
919 main() |
