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90
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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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8 import itertools as it
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9 import scipy.stats as st
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10 import lxml.etree as ET
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11 import math
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12 import utils.general_utils as utils
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13 from PIL import Image
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14 import os
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15 import copy
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16 import argparse
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17 import pyvips
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18 from PIL import Image, ImageDraw, ImageFont
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19 from typing import Tuple, Union, Optional, List, Dict
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20 import matplotlib.pyplot as plt
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21
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22 ERRORS = []
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23 ########################## argparse ##########################################
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24 ARGS :argparse.Namespace
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25 def process_args() -> argparse.Namespace:
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26 """
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27 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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28
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29 Args:
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30 args : Always obtained (in file) from sys.argv
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31
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32 Returns:
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33 Namespace : An object containing the parsed arguments
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34 """
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35 parser = argparse.ArgumentParser(
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36 usage = "%(prog)s [options]",
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37 description = "process some value's genes to create a comparison's map.")
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38
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39 #General:
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40 parser.add_argument(
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41 '-td', '--tool_dir',
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42 type = str,
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43 required = True,
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44 help = 'your tool directory')
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45
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46 parser.add_argument('-on', '--control', type = str)
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47 parser.add_argument('-ol', '--out_log', help = "Output log")
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48
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49 #Computation details:
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50 parser.add_argument(
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51 '-co', '--comparison',
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52 type = str,
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53 default = '1vs1',
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54 choices = ['manyvsmany', 'onevsrest', 'onevsmany'])
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55
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56 parser.add_argument(
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57 '-pv' ,'--pValue',
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58 type = float,
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59 default = 0.1,
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60 help = 'P-Value threshold (default: %(default)s)')
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61
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62 parser.add_argument(
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63 '-fc', '--fChange',
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64 type = float,
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65 default = 1.5,
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66 help = 'Fold-Change threshold (default: %(default)s)')
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67
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68
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69 parser.add_argument(
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70 '-op', '--option',
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71 type = str,
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72 choices = ['datasets', 'dataset_class'],
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73 help='dataset or dataset and class')
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74
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75 parser.add_argument(
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76 '-idf', '--input_data_fluxes',
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77 type = str,
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78 help = 'input dataset fluxes')
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79
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80 parser.add_argument(
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81 '-icf', '--input_class_fluxes',
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82 type = str,
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83 help = 'sample group specification fluxes')
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84
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85 parser.add_argument(
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86 '-idsf', '--input_datas_fluxes',
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87 type = str,
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88 nargs = '+',
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89 help = 'input datasets fluxes')
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90
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91 parser.add_argument(
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92 '-naf', '--names_fluxes',
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93 type = str,
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94 nargs = '+',
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95 help = 'input names fluxes')
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96
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97 #Output:
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98 parser.add_argument(
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99 "-gs", "--generate_svg",
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100 type = utils.Bool("generate_svg"), default = True,
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101 help = "choose whether to generate svg")
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102
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103 parser.add_argument(
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104 "-gp", "--generate_pdf",
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105 type = utils.Bool("generate_pdf"), default = True,
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106 help = "choose whether to generate pdf")
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107
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108 parser.add_argument(
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109 '-cm', '--custom_map',
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110 type = str,
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111 help='custom map to use')
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112
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113 parser.add_argument(
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114 '-mc', '--choice_map',
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115 type = utils.Model, default = utils.Model.HMRcore,
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116 choices = [utils.Model.HMRcore, utils.Model.ENGRO2, utils.Model.Custom])
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117
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118 parser.add_argument(
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119 '-colorm', '--color_map',
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120 type = str,
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121 choices = ["jet", "viridis"])
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122
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123 args :argparse.Namespace = parser.parse_args()
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124 args.net = True
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125
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126 return args
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127
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128 ############################ dataset input ####################################
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129 def read_dataset(data :str, name :str) -> pd.DataFrame:
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130 """
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131 Tries to read the dataset from its path (data) as a tsv and turns it into a DataFrame.
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132
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133 Args:
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134 data : filepath of a dataset (from frontend input params or literals upon calling)
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135 name : name associated with the dataset (from frontend input params or literals upon calling)
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136
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137 Returns:
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138 pd.DataFrame : dataset in a runtime operable shape
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139
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140 Raises:
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141 sys.exit : if there's no data (pd.errors.EmptyDataError) or if the dataset has less than 2 columns
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142 """
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143 try:
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144 dataset = pd.read_csv(data, sep = '\t', header = 0, engine='python')
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145 except pd.errors.EmptyDataError:
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146 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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147 if len(dataset.columns) < 2:
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148 sys.exit('Execution aborted: wrong format of ' + name + '\n')
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149 return dataset
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150
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151 ############################ dataset name #####################################
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152 def name_dataset(name_data :str, count :int) -> str:
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153 """
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154 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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155
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156 Args:
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157 name_data : name associated with the dataset (from frontend input params)
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158 count : counter from 1 to make these names unique (external)
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159
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160 Returns:
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161 str : the name made unique
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162 """
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163 if str(name_data) == 'Dataset':
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164 return str(name_data) + '_' + str(count)
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165 else:
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166 return str(name_data)
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167
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168 ############################ map_methods ######################################
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169 FoldChange = Union[float, int, str] # Union[float, Literal[0, "-INF", "INF"]]
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170 def fold_change(avg1 :float, avg2 :float) -> FoldChange:
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171 """
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172 Calculates the fold change between two gene expression values.
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173
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174 Args:
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175 avg1 : average expression value from one dataset avg2 : average expression value from the other dataset
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176
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177 Returns:
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178 FoldChange :
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179 0 : when both input values are 0
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180 "-INF" : when avg1 is 0
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181 "INF" : when avg2 is 0
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182 float : for any other combination of values
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183 """
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184 if avg1 == 0 and avg2 == 0:
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185 return 0
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186 elif avg1 == 0:
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187 return '-INF'
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188 elif avg2 == 0:
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189 return 'INF'
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190 else: # (threshold_F_C - 1) / (abs(threshold_F_C) + 1) con threshold_F_C > 1
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191 return (avg1 - avg2) / (abs(avg1) + abs(avg2))
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192
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193 def fix_style(l :str, col :Optional[str], width :str, dash :str) -> str:
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194 """
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195 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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196
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197 Args:
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198 l : current style string of an SVG element
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199 col : new value for the "stroke" style property
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200 width : new value for the "stroke-width" style property
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201 dash : new value for the "stroke-dasharray" style property
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202
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203 Returns:
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204 str : the fixed style string
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205 """
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206 tmp = l.split(';')
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207 flag_col = False
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208 flag_width = False
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209 flag_dash = False
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210 for i in range(len(tmp)):
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211 if tmp[i].startswith('stroke:'):
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212 tmp[i] = 'stroke:' + col
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213 flag_col = True
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214 if tmp[i].startswith('stroke-width:'):
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215 tmp[i] = 'stroke-width:' + width
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216 flag_width = True
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217 if tmp[i].startswith('stroke-dasharray:'):
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218 tmp[i] = 'stroke-dasharray:' + dash
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219 flag_dash = True
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220 if not flag_col:
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221 tmp.append('stroke:' + col)
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222 if not flag_width:
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223 tmp.append('stroke-width:' + width)
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224 if not flag_dash:
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225 tmp.append('stroke-dasharray:' + dash)
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226 return ';'.join(tmp)
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227
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228 # The type of d values is collapsed, losing precision, because the dict containst lists instead of tuples, please fix!
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229 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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230 """
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231 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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232
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233 Args:
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234 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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235 core_map : SVG map to modify
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236 threshold_P_V : threshold for a p-value to be considered significant
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237 threshold_F_C : threshold for a fold change value to be considered significant
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238 max_z_score : highest z-score (absolute value)
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239
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240 Returns:
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241 ET.ElementTree : the modified core_map
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242
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243 Side effects:
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244 core_map : mut
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245 """
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246 maxT = 12
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247 minT = 2
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248 grey = '#BEBEBE'
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249 blue = '#6495ed'
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250 red = '#ecac68'
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251 for el in core_map.iter():
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252 el_id = str(el.get('id'))
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253 if el_id.startswith('R_'):
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254 tmp = d.get(el_id[2:])
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255 if tmp != None:
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256 p_val :float = tmp[0]
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257 f_c = tmp[1]
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258 z_score = tmp[2]
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259 if p_val < threshold_P_V:
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260 if not isinstance(f_c, str):
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261 if abs(f_c) < ((threshold_F_C - 1) / (abs(threshold_F_C) + 1)): #
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262 col = grey
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263 width = str(minT)
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264 else:
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265 if f_c < 0:
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266 col = blue
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267 elif f_c > 0:
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268 col = red
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269 width = str(max((abs(z_score) * maxT) / max_z_score, minT))
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270 else:
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271 if f_c == '-INF':
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272 col = blue
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273 elif f_c == 'INF':
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274 col = red
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275 width = str(maxT)
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276 dash = 'none'
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277 else:
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278 dash = '5,5'
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279 col = grey
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280 width = str(minT)
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281 el.set('style', fix_style(el.get('style', ""), col, width, dash))
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282 return core_map
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283
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284 def getElementById(reactionId :str, metabMap :ET.ElementTree) -> utils.Result[ET.Element, utils.Result.ResultErr]:
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285 """
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286 Finds any element in the given map with the given ID. ID uniqueness in an svg file is recommended but
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287 not enforced, if more than one element with the exact ID is found only the first will be returned.
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288
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289 Args:
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290 reactionId (str): exact ID of the requested element.
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291 metabMap (ET.ElementTree): metabolic map containing the element.
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292
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293 Returns:
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294 utils.Result[ET.Element, ResultErr]: result of the search, either the first match found or a ResultErr.
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295 """
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296 return utils.Result.Ok(
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297 f"//*[@id=\"{reactionId}\"]").map(
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298 lambda xPath : metabMap.xpath(xPath)[0]).mapErr(
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299 lambda _ : utils.Result.ResultErr(f"No elements with ID \"{reactionId}\" found in map"))
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300 # ^^^ we shamelessly ignore the contents of the IndexError, it offers nothing to the user.
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301
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302 def styleMapElement(element :ET.Element, styleStr :str) -> None:
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303 currentStyles :str = element.get("style", "")
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304 if re.search(r";stroke:[^;]+;stroke-width:[^;]+;stroke-dasharray:[^;]+$", currentStyles):
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305 currentStyles = ';'.join(currentStyles.split(';')[:-3])
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306
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307 element.set("style", currentStyles + styleStr)
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308
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309 class ReactionDirection(Enum):
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310 Unknown = ""
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311 Direct = "_F"
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312 Inverse = "_B"
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313
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314 @classmethod
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315 def fromDir(cls, s :str) -> "ReactionDirection":
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316 # vvv as long as there's so few variants I actually condone the if spam:
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317 if s == ReactionDirection.Direct.value: return ReactionDirection.Direct
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318 if s == ReactionDirection.Inverse.value: return ReactionDirection.Inverse
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319 return ReactionDirection.Unknown
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320
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321 @classmethod
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322 def fromReactionId(cls, reactionId :str) -> "ReactionDirection":
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323 return ReactionDirection.fromDir(reactionId[-2:])
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324
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325 def getArrowBodyElementId(reactionId :str) -> str:
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326 if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV
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327 elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: reactionId = reactionId[:-2]
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328 return f"R_{reactionId}"
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329
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330 def getArrowHeadElementId(reactionId :str) -> Tuple[str, str]:
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331 """
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332 We attempt extracting the direction information from the provided reaction ID, if unsuccessful we provide the IDs of both directions.
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333
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334 Args:
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335 reactionId : the provided reaction ID.
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336
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337 Returns:
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338 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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339 """
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340 if reactionId.endswith("_RV"): reactionId = reactionId[:-3] #TODO: standardize _RV
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341 elif ReactionDirection.fromReactionId(reactionId) is not ReactionDirection.Unknown: return reactionId[:-3:-1] + reactionId[:-2], ""
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342 return f"F_{reactionId}", f"B_{reactionId}"
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343
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344 class ArrowColor(Enum):
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345 """
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346 Encodes possible arrow colors based on their meaning in the enrichment process.
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347 """
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348 Invalid = "#BEBEBE" # gray, fold-change under treshold
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349 Transparent = "#ffffff00" # white, not significant p-value
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350 UpRegulated = "#ecac68" # red, up-regulated reaction
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351 DownRegulated = "#6495ed" # blue, down-regulated reaction
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352
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353 UpRegulatedInv = "#FF0000"
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354 # ^^^ different shade of red (actually orange), up-regulated net value for a reversible reaction with
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355 # conflicting enrichment in the two directions.
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356
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357 DownRegulatedInv = "#0000FF"
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358 # ^^^ different shade of blue (actually purple), down-regulated net value for a reversible reaction with
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359 # conflicting enrichment in the two directions.
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360
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361 @classmethod
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362 def fromFoldChangeSign(cls, foldChange :float, *, useAltColor = False) -> "ArrowColor":
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363 colors = (cls.DownRegulated, cls.DownRegulatedInv) if foldChange < 0 else (cls.UpRegulated, cls.UpRegulatedInv)
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364 return colors[useAltColor]
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365
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366 def __str__(self) -> str: return self.value
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367
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368 class Arrow:
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369 """
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370 Models the properties of a reaction arrow that change based on enrichment.
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371 """
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372 MIN_W = 2
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373 MAX_W = 12
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374
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375 def __init__(self, width :int, col: ArrowColor, *, isDashed = False) -> None:
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376 """
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377 (Private) Initializes an instance of Arrow.
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378
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379 Args:
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380 width : width of the arrow, ideally to be kept within Arrow.MIN_W and Arrow.MAX_W (not enforced).
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381 col : color of the arrow.
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382 isDashed : whether the arrow should be dashed, meaning the associated pValue resulted not significant.
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383
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384 Returns:
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385 None : practically, a Arrow instance.
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386 """
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387 self.w = width
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388 self.col = col
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389 self.dash = isDashed
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390
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391 def applyTo(self, reactionId :str, metabMap :ET.ElementTree, styleStr :str) -> None:
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392 if getElementById(reactionId, metabMap).map(lambda el : styleMapElement(el, styleStr)).isErr:
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393 ERRORS.append(reactionId)
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394
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395 def styleReactionElements(self, metabMap :ET.ElementTree, reactionId :str, *, mindReactionDir = True) -> None:
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396 if not mindReactionDir:
|
|
|
397 return self.applyTo(getArrowBodyElementId(reactionId), metabMap, self.toStyleStr())
|
|
|
398
|
|
|
399 # Now we style the arrow head(s):
|
|
|
400 idOpt1, idOpt2 = getArrowHeadElementId(reactionId)
|
|
|
401 self.applyTo(idOpt1, metabMap, self.toStyleStr(downSizedForTips = True))
|
|
|
402 if idOpt2: self.applyTo(idOpt2, metabMap, self.toStyleStr(downSizedForTips = True))
|
|
|
403
|
|
|
404 def styleReactionElementsMeanMedian(self, metabMap :ET.ElementTree, reactionId :str, isNegative:bool) -> None:
|
|
|
405
|
|
|
406 self.applyTo(getArrowBodyElementId(reactionId), metabMap, self.toStyleStr())
|
|
|
407 idOpt1, idOpt2 = getArrowHeadElementId(reactionId)
|
|
|
408
|
|
|
409 if(isNegative):
|
|
|
410 self.applyTo(idOpt2, metabMap, self.toStyleStr(downSizedForTips = True))
|
|
|
411 self.col = ArrowColor.Transparent
|
|
|
412 self.applyTo(idOpt1, metabMap, self.toStyleStr(downSizedForTips = True)) #trasp
|
|
|
413 else:
|
|
|
414 self.applyTo(idOpt1, metabMap, self.toStyleStr(downSizedForTips = True))
|
|
|
415 self.col = ArrowColor.Transparent
|
|
|
416 self.applyTo(idOpt2, metabMap, self.toStyleStr(downSizedForTips = True)) #trasp
|
|
|
417
|
|
|
418
|
|
|
419
|
|
|
420 def getMapReactionId(self, reactionId :str, mindReactionDir :bool) -> str:
|
|
|
421 """
|
|
|
422 Computes the reaction ID as encoded in the map for a given reaction ID from the dataset.
|
|
|
423
|
|
|
424 Args:
|
|
|
425 reactionId: the reaction ID, as encoded in the dataset.
|
|
|
426 mindReactionDir: if True forward (F_) and backward (B_) directions will be encoded in the result.
|
|
|
427
|
|
|
428 Returns:
|
|
|
429 str : the ID of an arrow's body or tips in the map.
|
|
|
430 """
|
|
|
431 # we assume the reactionIds also don't encode reaction dir if they don't mind it when styling the map.
|
|
|
432 if not mindReactionDir: return "R_" + reactionId
|
|
|
433
|
|
|
434 #TODO: this is clearly something we need to make consistent in fluxes
|
|
|
435 return (reactionId[:-3:-1] + reactionId[:-2]) if reactionId[:-2] in ["_F", "_B"] else f"F_{reactionId}" # "Pyr_F" --> "F_Pyr"
|
|
|
436
|
|
|
437 def toStyleStr(self, *, downSizedForTips = False) -> str:
|
|
|
438 """
|
|
|
439 Collapses the styles of this Arrow into a str, ready to be applied as part of the "style" property on an svg element.
|
|
|
440
|
|
|
441 Returns:
|
|
|
442 str : the styles string.
|
|
|
443 """
|
|
|
444 width = self.w
|
|
|
445 if downSizedForTips: width *= 0.8
|
|
|
446 return f";stroke:{self.col};stroke-width:{width};stroke-dasharray:{'5,5' if self.dash else 'none'}"
|
|
|
447
|
|
|
448 # vvv These constants could be inside the class itself a static properties, but python
|
|
|
449 # was built by brainless organisms so here we are!
|
|
|
450 INVALID_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid)
|
|
|
451 INSIGNIFICANT_ARROW = Arrow(Arrow.MIN_W, ArrowColor.Invalid, isDashed = True)
|
|
|
452
|
|
|
453 def applyFluxesEnrichmentToMap(fluxesEnrichmentRes :Dict[str, Union[Tuple[float, FoldChange], Tuple[float, FoldChange, float, float]]], metabMap :ET.ElementTree, maxNumericZScore :float) -> None:
|
|
|
454 """
|
|
|
455 Applies fluxes enrichment results to the provided metabolic map.
|
|
|
456
|
|
|
457 Args:
|
|
|
458 fluxesEnrichmentRes : fluxes enrichment results.
|
|
|
459 metabMap : the metabolic map to edit.
|
|
|
460 maxNumericZScore : biggest finite z-score value found.
|
|
|
461
|
|
|
462 Side effects:
|
|
|
463 metabMap : mut
|
|
|
464
|
|
|
465 Returns:
|
|
|
466 None
|
|
|
467 """
|
|
|
468 for reactionId, values in fluxesEnrichmentRes.items():
|
|
|
469 pValue = values[0]
|
|
|
470 foldChange = values[1]
|
|
|
471 z_score = values[2]
|
|
|
472
|
|
|
473 if isinstance(foldChange, str): foldChange = float(foldChange)
|
|
|
474 if pValue >= ARGS.pValue: # pValue above tresh: dashed arrow
|
|
|
475 INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId)
|
|
|
476 INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId, mindReactionDir = False)
|
|
|
477
|
|
|
478 continue
|
|
|
479
|
|
|
480 if abs(foldChange) < (ARGS.fChange - 1) / (abs(ARGS.fChange) + 1):
|
|
|
481 INVALID_ARROW.styleReactionElements(metabMap, reactionId)
|
|
|
482 INVALID_ARROW.styleReactionElements(metabMap, reactionId, mindReactionDir = False)
|
|
|
483
|
|
|
484 continue
|
|
|
485
|
|
|
486 width = Arrow.MAX_W
|
|
|
487 if not math.isinf(foldChange):
|
|
|
488 try:
|
|
|
489 width = max(abs(z_score * Arrow.MAX_W) / maxNumericZScore, Arrow.MIN_W)
|
|
|
490
|
|
|
491 except ZeroDivisionError: pass
|
|
|
492
|
|
|
493 #if not reactionId.endswith("_RV"): # RV stands for reversible reactions
|
|
|
494 # Arrow(width, ArrowColor.fromFoldChangeSign(foldChange)).styleReactionElements(metabMap, reactionId)
|
|
|
495 # continue
|
|
|
496
|
|
|
497 #reactionId = reactionId[:-3] # Remove "_RV"
|
|
|
498
|
|
|
499 inversionScore = (values[3] < 0) + (values[4] < 0) # Compacts the signs of averages into 1 easy to check score
|
|
|
500 if inversionScore == 2: foldChange *= -1
|
|
|
501 # ^^^ Style the inverse direction with the opposite sign netValue
|
|
|
502
|
|
|
503 # If the score is 1 (opposite signs) we use alternative colors vvv
|
|
|
504 arrow = Arrow(width, ArrowColor.fromFoldChangeSign(foldChange, useAltColor = inversionScore == 1))
|
|
|
505
|
|
|
506 # vvv These 2 if statements can both be true and can both happen
|
|
|
507 if ARGS.net: # style arrow head(s):
|
|
|
508 arrow.styleReactionElements(metabMap, reactionId + ("_B" if inversionScore == 2 else "_F"))
|
|
|
509 arrow.applyTo(("F_" if inversionScore == 2 else "B_") + reactionId, metabMap, f";stroke:{ArrowColor.Transparent};stroke-width:0;stroke-dasharray:None")
|
|
|
510
|
|
|
511 arrow.styleReactionElements(metabMap, reactionId, mindReactionDir = False)
|
|
|
512
|
|
|
513
|
|
|
514 ############################ split class ######################################
|
|
|
515 def split_class(classes :pd.DataFrame, resolve_rules :Dict[str, List[float]]) -> Dict[str, List[List[float]]]:
|
|
|
516 """
|
|
|
517 Generates a :dict that groups together data from a :DataFrame based on classes the data is related to.
|
|
|
518
|
|
|
519 Args:
|
|
|
520 classes : a :DataFrame of only string values, containing class information (rows) and keys to query the resolve_rules :dict
|
|
|
521 resolve_rules : a :dict containing :float data
|
|
|
522
|
|
|
523 Returns:
|
|
|
524 dict : the dict with data grouped by class
|
|
|
525
|
|
|
526 Side effects:
|
|
|
527 classes : mut
|
|
|
528 """
|
|
|
529 class_pat :Dict[str, List[List[float]]] = {}
|
|
|
530 for i in range(len(classes)):
|
|
|
531 classe :str = classes.iloc[i, 1]
|
|
|
532 if pd.isnull(classe): continue
|
|
|
533
|
|
|
534 l :List[List[float]] = []
|
|
|
535 for j in range(i, len(classes)):
|
|
|
536 if classes.iloc[j, 1] == classe:
|
|
|
537 pat_id :str = classes.iloc[j, 0]
|
|
|
538 tmp = resolve_rules.get(pat_id, None)
|
|
|
539 if tmp != None:
|
|
|
540 l.append(tmp)
|
|
|
541 classes.iloc[j, 1] = None
|
|
|
542
|
|
|
543 if l:
|
|
|
544 class_pat[classe] = list(map(list, zip(*l)))
|
|
|
545 continue
|
|
|
546
|
|
|
547 utils.logWarning(
|
|
|
548 f"Warning: no sample found in class \"{classe}\", the class has been disregarded", ARGS.out_log)
|
|
|
549
|
|
|
550 return class_pat
|
|
|
551
|
|
|
552 ############################ conversion ##############################################
|
|
|
553 #conversion from svg to png
|
|
|
554 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:
|
|
|
555 """
|
|
|
556 Internal utility to convert an SVG to PNG (forced opaque) to aid in PDF conversion.
|
|
|
557
|
|
|
558 Args:
|
|
|
559 svg_path : path to SVG file
|
|
|
560 png_path : path for new PNG file
|
|
|
561 dpi : dots per inch of the generated PNG
|
|
|
562 scale : scaling factor for the generated PNG, computed internally when a size is provided
|
|
|
563 size : final effective width of the generated PNG
|
|
|
564
|
|
|
565 Returns:
|
|
|
566 None
|
|
|
567 """
|
|
|
568 if size:
|
|
|
569 image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=1)
|
|
|
570 scale = size / image.width
|
|
|
571 image = image.resize(scale)
|
|
|
572 else:
|
|
|
573 image = pyvips.Image.new_from_file(svg_path.show(), dpi=dpi, scale=scale)
|
|
|
574
|
|
|
575 white_background = pyvips.Image.black(image.width, image.height).new_from_image([255, 255, 255])
|
|
|
576 white_background = white_background.affine([scale, 0, 0, scale])
|
|
|
577
|
|
|
578 if white_background.bands != image.bands:
|
|
|
579 white_background = white_background.extract_band(0)
|
|
|
580
|
|
|
581 composite_image = white_background.composite2(image, 'over')
|
|
|
582 composite_image.write_to_file(png_path.show())
|
|
|
583
|
|
|
584 #funzione unica, lascio fuori i file e li passo in input
|
|
|
585 #conversion from png to pdf
|
|
|
586 def convert_png_to_pdf(png_file :utils.FilePath, pdf_file :utils.FilePath) -> None:
|
|
|
587 """
|
|
|
588 Internal utility to convert a PNG to PDF to aid from SVG conversion.
|
|
|
589
|
|
|
590 Args:
|
|
|
591 png_file : path to PNG file
|
|
|
592 pdf_file : path to new PDF file
|
|
|
593
|
|
|
594 Returns:
|
|
|
595 None
|
|
|
596 """
|
|
|
597 image = Image.open(png_file.show())
|
|
|
598 image = image.convert("RGB")
|
|
|
599 image.save(pdf_file.show(), "PDF", resolution=100.0)
|
|
|
600
|
|
|
601 #function called to reduce redundancy in the code
|
|
|
602 def convert_to_pdf(file_svg :utils.FilePath, file_png :utils.FilePath, file_pdf :utils.FilePath) -> None:
|
|
|
603 """
|
|
|
604 Converts the SVG map at the provided path to PDF.
|
|
|
605
|
|
|
606 Args:
|
|
|
607 file_svg : path to SVG file
|
|
|
608 file_png : path to PNG file
|
|
|
609 file_pdf : path to new PDF file
|
|
|
610
|
|
|
611 Returns:
|
|
|
612 None
|
|
|
613 """
|
|
|
614 svg_to_png_with_background(file_svg, file_png)
|
|
|
615 try:
|
|
|
616 convert_png_to_pdf(file_png, file_pdf)
|
|
|
617 print(f'PDF file {file_pdf.filePath} successfully generated.')
|
|
|
618
|
|
|
619 except Exception as e:
|
|
|
620 raise utils.DataErr(file_pdf.show(), f'Error generating PDF file: {e}')
|
|
|
621
|
|
|
622 ############################ map ##############################################
|
|
|
623 def buildOutputPath(dataset1Name :str, dataset2Name = "rest", *, details = "", ext :utils.FileFormat) -> utils.FilePath:
|
|
|
624 """
|
|
|
625 Builds a FilePath instance from the names of confronted datasets ready to point to a location in the
|
|
|
626 "result/" folder, used by this tool for output files in collections.
|
|
|
627
|
|
|
628 Args:
|
|
|
629 dataset1Name : _description_
|
|
|
630 dataset2Name : _description_. Defaults to "rest".
|
|
|
631 details : _description_
|
|
|
632 ext : _description_
|
|
|
633
|
|
|
634 Returns:
|
|
|
635 utils.FilePath : _description_
|
|
|
636 """
|
|
|
637 # This function returns a util data structure but is extremely specific to this module.
|
|
|
638 # RAS also uses collections as output and as such might benefit from a method like this, but I'd wait
|
|
|
639 # TODO: until a third tool with multiple outputs appears before porting this to utils.
|
|
|
640 return utils.FilePath(
|
|
|
641 f"{dataset1Name}_vs_{dataset2Name}" + (f" ({details})" if details else ""),
|
|
|
642 # ^^^ yes this string is built every time even if the form is the same for the same 2 datasets in
|
|
|
643 # all output files: I don't care, this was never the performance bottleneck of the tool and
|
|
|
644 # there is no other net gain in saving and re-using the built string.
|
|
|
645 ext,
|
|
|
646 prefix = "result")
|
|
|
647
|
|
|
648 FIELD_NOT_AVAILABLE = '/'
|
|
|
649 def writeToCsv(rows: List[list], fieldNames :List[str], outPath :utils.FilePath) -> None:
|
|
|
650 fieldsAmt = len(fieldNames)
|
|
|
651 with open(outPath.show(), "w", newline = "") as fd:
|
|
|
652 writer = csv.DictWriter(fd, fieldnames = fieldNames, delimiter = '\t')
|
|
|
653 writer.writeheader()
|
|
|
654
|
|
|
655 for row in rows:
|
|
|
656 sizeMismatch = fieldsAmt - len(row)
|
|
|
657 if sizeMismatch > 0: row.extend([FIELD_NOT_AVAILABLE] * sizeMismatch)
|
|
|
658 writer.writerow({ field : data for field, data in zip(fieldNames, row) })
|
|
|
659
|
|
|
660 OldEnrichedScores = Dict[str, List[Union[float, FoldChange]]] #TODO: try to use Tuple whenever possible
|
|
|
661 def writeTabularResult(enrichedScores : OldEnrichedScores, outPath :utils.FilePath) -> None:
|
|
|
662 fieldNames = ["ids", "P_Value", "fold change"]
|
|
|
663 fieldNames.extend(["average_1", "average_2"])
|
|
|
664
|
|
|
665 writeToCsv([ [reactId] + values for reactId, values in enrichedScores.items() ], fieldNames, outPath)
|
|
|
666
|
|
|
667 def temp_thingsInCommon(tmp :Dict[str, List[Union[float, FoldChange]]], core_map :ET.ElementTree, max_z_score :float, dataset1Name :str, dataset2Name = "rest") -> None:
|
|
|
668 # this function compiles the things always in common between comparison modes after enrichment.
|
|
|
669 # TODO: organize, name better.
|
|
|
670 writeTabularResult(tmp, buildOutputPath(dataset1Name, dataset2Name, details = "Tabular Result", ext = utils.FileFormat.TSV))
|
|
|
671 for reactId, enrichData in tmp.items(): tmp[reactId] = tuple(enrichData)
|
|
|
672 applyFluxesEnrichmentToMap(tmp, core_map, max_z_score)
|
|
|
673
|
|
|
674 def computePValue(dataset1Data: List[float], dataset2Data: List[float]) -> Tuple[float, float]:
|
|
|
675 """
|
|
|
676 Computes the statistical significance score (P-value) of the comparison between coherent data
|
|
|
677 from two datasets. The data is supposed to, in both datasets:
|
|
|
678 - be related to the same reaction ID;
|
|
|
679 - be ordered by sample, such that the item at position i in both lists is related to the
|
|
|
680 same sample or cell line.
|
|
|
681
|
|
|
682 Args:
|
|
|
683 dataset1Data : data from the 1st dataset.
|
|
|
684 dataset2Data : data from the 2nd dataset.
|
|
|
685
|
|
|
686 Returns:
|
|
|
687 tuple: (P-value, Z-score)
|
|
|
688 - P-value from a Kolmogorov-Smirnov test on the provided data.
|
|
|
689 - Z-score of the difference between means of the two datasets.
|
|
|
690 """
|
|
|
691 # Perform Kolmogorov-Smirnov test
|
|
|
692 ks_statistic, p_value = st.ks_2samp(dataset1Data, dataset2Data)
|
|
|
693
|
|
|
694 # Calculate means and standard deviations
|
|
|
695 mean1 = np.mean(dataset1Data)
|
|
|
696 mean2 = np.mean(dataset2Data)
|
|
|
697 std1 = np.std(dataset1Data, ddof=1)
|
|
|
698 std2 = np.std(dataset2Data, ddof=1)
|
|
|
699
|
|
|
700 n1 = len(dataset1Data)
|
|
|
701 n2 = len(dataset2Data)
|
|
|
702
|
|
|
703 # Calculate Z-score
|
|
|
704 z_score = (mean1 - mean2) / np.sqrt((std1**2 / n1) + (std2**2 / n2))
|
|
|
705
|
|
|
706 return p_value, z_score
|
|
|
707
|
|
|
708 def compareDatasetPair(dataset1Data :List[List[float]], dataset2Data :List[List[float]], ids :List[str]) -> Tuple[Dict[str, List[Union[float, FoldChange]]], float]:
|
|
|
709 #TODO: the following code still suffers from "dumbvarnames-osis"
|
|
|
710 tmp :Dict[str, List[Union[float, FoldChange]]] = {}
|
|
|
711 count = 0
|
|
|
712 max_z_score = 0
|
|
|
713
|
|
|
714 for l1, l2 in zip(dataset1Data, dataset2Data):
|
|
|
715 reactId = ids[count]
|
|
|
716 count += 1
|
|
|
717 if not reactId: continue # we skip ids that have already been processed
|
|
|
718
|
|
|
719 try:
|
|
|
720 p_value, z_score = computePValue(l1, l2)
|
|
|
721 avg1 = sum(l1) / len(l1)
|
|
|
722 avg2 = sum(l2) / len(l2)
|
|
|
723 avg = fold_change(avg1, avg2)
|
|
|
724 if not isinstance(z_score, str) and max_z_score < abs(z_score): max_z_score = abs(z_score)
|
|
|
725 tmp[reactId] = [float(p_value), avg, z_score, avg1, avg2]
|
|
|
726 except (TypeError, ZeroDivisionError): continue
|
|
|
727
|
|
|
728 return tmp, max_z_score
|
|
|
729
|
|
|
730 def computeEnrichment(metabMap :ET.ElementTree, class_pat :Dict[str, List[List[float]]], ids :List[str]) -> None:
|
|
|
731 """
|
|
|
732 Compares clustered data based on a given comparison mode and applies enrichment-based styling on the
|
|
|
733 provided metabolic map.
|
|
|
734
|
|
|
735 Args:
|
|
|
736 metabMap : SVG map to modify.
|
|
|
737 class_pat : the clustered data.
|
|
|
738 ids : ids for data association.
|
|
|
739
|
|
|
740
|
|
|
741 Returns:
|
|
|
742 None
|
|
|
743
|
|
|
744 Raises:
|
|
|
745 sys.exit : if there are less than 2 classes for comparison
|
|
|
746
|
|
|
747 Side effects:
|
|
|
748 metabMap : mut
|
|
|
749 ids : mut
|
|
|
750 """
|
|
|
751 class_pat = { k.strip() : v for k, v in class_pat.items() }
|
|
|
752 #TODO: simplfy this stuff vvv and stop using sys.exit (raise the correct utils error)
|
|
|
753 if (not class_pat) or (len(class_pat.keys()) < 2): sys.exit('Execution aborted: classes provided for comparisons are less than two\n')
|
|
|
754
|
|
|
755 if ARGS.comparison == "manyvsmany":
|
|
|
756 for i, j in it.combinations(class_pat.keys(), 2):
|
|
|
757 #TODO: these 2 functions are always called in pair and in this order and need common data,
|
|
|
758 # some clever refactoring would be appreciated.
|
|
|
759 comparisonDict, max_z_score = compareDatasetPair(class_pat.get(i), class_pat.get(j), ids)
|
|
|
760 temp_thingsInCommon(comparisonDict, metabMap, max_z_score, i, j)
|
|
|
761
|
|
|
762 elif ARGS.comparison == "onevsrest":
|
|
|
763 for single_cluster in class_pat.keys():
|
|
|
764 t :List[List[List[float]]] = []
|
|
|
765 for k in class_pat.keys():
|
|
|
766 if k != single_cluster:
|
|
|
767 t.append(class_pat.get(k))
|
|
|
768
|
|
|
769 rest :List[List[float]] = []
|
|
|
770 for i in t:
|
|
|
771 rest = rest + i
|
|
|
772
|
|
|
773 comparisonDict, max_z_score = compareDatasetPair(class_pat.get(single_cluster), rest, ids)
|
|
|
774 temp_thingsInCommon(comparisonDict, metabMap, max_z_score, single_cluster)
|
|
|
775
|
|
|
776 elif ARGS.comparison == "onevsmany":
|
|
|
777 controlItems = class_pat.get(ARGS.control)
|
|
|
778 for otherDataset in class_pat.keys():
|
|
|
779 if otherDataset == ARGS.control: continue
|
|
|
780
|
|
|
781 comparisonDict, max_z_score = compareDatasetPair(controlItems, class_pat.get(otherDataset), ids)
|
|
|
782 temp_thingsInCommon(comparisonDict, metabMap, max_z_score, ARGS.control, otherDataset)
|
|
|
783
|
|
|
784 def createOutputMaps(dataset1Name :str, dataset2Name :str, core_map :ET.ElementTree) -> None:
|
|
|
785 svgFilePath = buildOutputPath(dataset1Name, dataset2Name, details = "SVG Map", ext = utils.FileFormat.SVG)
|
|
|
786 utils.writeSvg(svgFilePath, core_map)
|
|
|
787
|
|
|
788 if ARGS.generate_pdf:
|
|
|
789 pngPath = buildOutputPath(dataset1Name, dataset2Name, details = "PNG Map", ext = utils.FileFormat.PNG)
|
|
|
790 pdfPath = buildOutputPath(dataset1Name, dataset2Name, details = "PDF Map", ext = utils.FileFormat.PDF)
|
|
|
791 convert_to_pdf(svgFilePath, pngPath, pdfPath)
|
|
|
792
|
|
|
793 if not ARGS.generate_svg: os.remove(svgFilePath.show())
|
|
|
794
|
|
|
795 ClassPat = Dict[str, List[List[float]]]
|
|
|
796 def getClassesAndIdsFromDatasets(datasetsPaths :List[str], datasetPath :str, classPath :str, names :List[str]) -> Tuple[List[str], ClassPat]:
|
|
|
797 # TODO: I suggest creating dicts with ids as keys instead of keeping class_pat and ids separate,
|
|
|
798 # for the sake of everyone's sanity.
|
|
|
799 class_pat :ClassPat = {}
|
|
|
800 if ARGS.option == 'datasets':
|
|
|
801 num = 1 #TODO: the dataset naming function could be a generator
|
|
|
802 for path, name in zip(datasetsPaths, names):
|
|
|
803 name = name_dataset(name, num)
|
|
|
804 resolve_rules_float, ids = getDatasetValues(path, name)
|
|
|
805 if resolve_rules_float != None:
|
|
|
806 class_pat[name] = list(map(list, zip(*resolve_rules_float.values())))
|
|
|
807
|
|
|
808 num += 1
|
|
|
809
|
|
|
810 elif ARGS.option == "dataset_class":
|
|
|
811 classes = read_dataset(classPath, "class")
|
|
|
812 classes = classes.astype(str)
|
|
|
813
|
|
|
814 resolve_rules_float, ids = getDatasetValues(datasetPath, "Dataset Class (not actual name)")
|
|
|
815 if resolve_rules_float != None: class_pat = split_class(classes, resolve_rules_float)
|
|
|
816
|
|
|
817 return ids, class_pat
|
|
|
818 #^^^ 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)
|
|
|
819
|
|
|
820 #TODO: create these damn args as FilePath objects
|
|
|
821 def getDatasetValues(datasetPath :str, datasetName :str) -> Tuple[ClassPat, List[str]]:
|
|
|
822 """
|
|
|
823 Opens the dataset at the given path and extracts the values (expected nullable numerics) and the IDs.
|
|
|
824
|
|
|
825 Args:
|
|
|
826 datasetPath : path to the dataset
|
|
|
827 datasetName (str): dataset name, used in error reporting
|
|
|
828
|
|
|
829 Returns:
|
|
|
830 Tuple[ClassPat, List[str]]: values and IDs extracted from the dataset
|
|
|
831 """
|
|
|
832 dataset = read_dataset(datasetPath, datasetName)
|
|
|
833 IDs = pd.Series.tolist(dataset.iloc[:, 0].astype(str))
|
|
|
834
|
|
|
835 dataset = dataset.drop(dataset.columns[0], axis = "columns").to_dict("list")
|
|
|
836 return { id : list(map(utils.Float("Dataset values, not an argument"), values)) for id, values in dataset.items() }, IDs
|
|
|
837
|
|
|
838 def rgb_to_hex(rgb):
|
|
|
839 """
|
|
|
840 Convert RGB values (0-1 range) to hexadecimal color format.
|
|
|
841
|
|
|
842 Args:
|
|
|
843 rgb (numpy.ndarray): An array of RGB color components (in the range [0, 1]).
|
|
|
844
|
|
|
845 Returns:
|
|
|
846 str: The color in hexadecimal format (e.g., '#ff0000' for red).
|
|
|
847 """
|
|
|
848 # Convert RGB values (0-1 range) to hexadecimal format
|
|
|
849 rgb = (np.array(rgb) * 255).astype(int)
|
|
|
850 return '#{:02x}{:02x}{:02x}'.format(rgb[0], rgb[1], rgb[2])
|
|
|
851
|
|
|
852
|
|
|
853
|
|
|
854 def save_colormap_image(min_value: float, max_value: float, path: utils.FilePath, colorMap:str="viridis"):
|
|
|
855 """
|
|
|
856 Create and save an image of the colormap showing the gradient and its range.
|
|
|
857
|
|
|
858 Args:
|
|
|
859 min_value (float): The minimum value of the colormap range.
|
|
|
860 max_value (float): The maximum value of the colormap range.
|
|
|
861 filename (str): The filename for saving the image.
|
|
|
862 """
|
|
|
863
|
|
|
864 # Create a colormap using matplotlib
|
|
|
865 cmap = plt.get_cmap(colorMap)
|
|
|
866
|
|
|
867 # Create a figure and axis
|
|
|
868 fig, ax = plt.subplots(figsize=(6, 1))
|
|
|
869 fig.subplots_adjust(bottom=0.5)
|
|
|
870
|
|
|
871 # Create a gradient image
|
|
|
872 gradient = np.linspace(0, 1, 256)
|
|
|
873 gradient = np.vstack((gradient, gradient))
|
|
|
874
|
|
|
875 # Add min and max value annotations
|
|
|
876 ax.text(0, 0.5, f'{np.round(min_value, 3)}', va='center', ha='right', transform=ax.transAxes, fontsize=12, color='black')
|
|
|
877 ax.text(1, 0.5, f'{np.round(max_value, 3)}', va='center', ha='left', transform=ax.transAxes, fontsize=12, color='black')
|
|
|
878
|
|
|
879
|
|
|
880 # Display the gradient image
|
|
|
881 ax.imshow(gradient, aspect='auto', cmap=cmap)
|
|
|
882 ax.set_axis_off()
|
|
|
883
|
|
|
884 # Save the image
|
|
|
885 plt.savefig(path.show(), bbox_inches='tight', pad_inches=0)
|
|
|
886 plt.close()
|
|
|
887 pass
|
|
|
888
|
|
|
889 def min_nonzero_abs(arr):
|
|
|
890 # Flatten the array and filter out zeros, then find the minimum of the remaining values
|
|
|
891 non_zero_elements = np.abs(arr)[np.abs(arr) > 0]
|
|
|
892 return np.min(non_zero_elements) if non_zero_elements.size > 0 else None
|
|
|
893
|
|
|
894 def computeEnrichmentMeanMedian(metabMap: ET.ElementTree, class_pat: Dict[str, List[List[float]]], ids: List[str], colormap:str) -> None:
|
|
|
895 """
|
|
|
896 Compute and visualize the metabolic map based on mean and median of the input fluxes.
|
|
|
897 The fluxes are normalised across classes/datasets and visualised using the given colormap.
|
|
|
898
|
|
|
899 Args:
|
|
|
900 metabMap (ET.ElementTree): An XML tree representing the metabolic map.
|
|
|
901 class_pat (Dict[str, List[List[float]]]): A dictionary where keys are class names and values are lists of enrichment values.
|
|
|
902 ids (List[str]): A list of reaction IDs to be used for coloring arrows.
|
|
|
903
|
|
|
904 Returns:
|
|
|
905 None
|
|
|
906 """
|
|
|
907 # Create copies only if they are needed
|
|
|
908 metabMap_mean = copy.deepcopy(metabMap)
|
|
|
909 metabMap_median = copy.deepcopy(metabMap)
|
|
|
910
|
|
|
911 # Compute medians and means
|
|
|
912 medians = {key: np.round(np.median(np.array(value), axis=1), 6) for key, value in class_pat.items()}
|
|
|
913 means = {key: np.round(np.mean(np.array(value), axis=1),6) for key, value in class_pat.items()}
|
|
|
914
|
|
|
915 # Normalize medians and means
|
|
|
916 max_flux_medians = max(np.max(np.abs(arr)) for arr in medians.values())
|
|
|
917 max_flux_means = max(np.max(np.abs(arr)) for arr in means.values())
|
|
|
918
|
|
|
919 min_flux_medians = min(min_nonzero_abs(arr) for arr in medians.values())
|
|
|
920 min_flux_means = min(min_nonzero_abs(arr) for arr in means.values())
|
|
|
921
|
|
|
922 medians = {key: median/max_flux_medians for key, median in medians.items()}
|
|
|
923 means = {key: mean/max_flux_means for key, mean in means.items()}
|
|
|
924
|
|
|
925 save_colormap_image(min_flux_medians, max_flux_medians, utils.FilePath("Color map median", ext=utils.FileFormat.PNG, prefix="result"), colormap)
|
|
|
926 save_colormap_image(min_flux_means, max_flux_means, utils.FilePath("Color map mean", ext=utils.FileFormat.PNG, prefix="result"), colormap)
|
|
|
927
|
|
|
928 cmap = plt.get_cmap(colormap)
|
|
|
929
|
|
|
930 for key in class_pat:
|
|
|
931 # Create color mappings for median and mean
|
|
|
932 colors_median = {
|
|
|
933 rxn_id: rgb_to_hex(cmap(abs(medians[key][i]))) if medians[key][i] != 0 else '#bebebe' #grey blocked
|
|
|
934 for i, rxn_id in enumerate(ids)
|
|
|
935 }
|
|
|
936
|
|
|
937 colors_mean = {
|
|
|
938 rxn_id: rgb_to_hex(cmap(abs(means[key][i]))) if means[key][i] != 0 else '#bebebe' #grey blocked
|
|
|
939 for i, rxn_id in enumerate(ids)
|
|
|
940 }
|
|
|
941
|
|
|
942 for i, rxn_id in enumerate(ids):
|
|
|
943 isNegative = medians[key][i] < 0
|
|
|
944
|
|
|
945 # Apply median arrows
|
|
|
946 apply_arrow(metabMap_median, rxn_id, colors_median[rxn_id], isNegative)
|
|
|
947
|
|
|
948 isNegative = means[key][i] < 0
|
|
|
949 # Apply mean arrows
|
|
|
950 apply_arrow(metabMap_mean, rxn_id, colors_mean[rxn_id], isNegative)
|
|
|
951
|
|
|
952 # Save and convert the SVG files
|
|
|
953 save_and_convert(metabMap_mean, "mean", key)
|
|
|
954 save_and_convert(metabMap_median, "median", key)
|
|
|
955
|
|
|
956 def apply_arrow(metabMap, rxn_id, color, isNegative):
|
|
|
957 """
|
|
|
958 Apply an arrow to a specific reaction in the metabolic map with a given color.
|
|
|
959
|
|
|
960 Args:
|
|
|
961 metabMap (ET.ElementTree): An XML tree representing the metabolic map.
|
|
|
962 rxn_id (str): The ID of the reaction to which the arrow will be applied.
|
|
|
963 color (str): The color of the arrow in hexadecimal format.
|
|
|
964
|
|
|
965 Returns:
|
|
|
966 None
|
|
|
967 """
|
|
|
968 arrow = Arrow(width=5, col=color)
|
|
|
969 arrow.styleReactionElementsMeanMedian(metabMap, rxn_id, isNegative)
|
|
|
970 pass
|
|
|
971
|
|
|
972 def save_and_convert(metabMap, map_type, key):
|
|
|
973 """
|
|
|
974 Save the metabolic map as an SVG file and optionally convert it to PNG and PDF formats.
|
|
|
975
|
|
|
976 Args:
|
|
|
977 metabMap (ET.ElementTree): An XML tree representing the metabolic map.
|
|
|
978 map_type (str): The type of map ('mean' or 'median').
|
|
|
979 key (str): The key identifying the specific map.
|
|
|
980
|
|
|
981 Returns:
|
|
|
982 None
|
|
|
983 """
|
|
|
984 svgFilePath = utils.FilePath(f"SVG Map {map_type} - {key}", ext=utils.FileFormat.SVG, prefix="result")
|
|
|
985 utils.writeSvg(svgFilePath, metabMap)
|
|
|
986 if ARGS.generate_pdf:
|
|
|
987 pngPath = utils.FilePath(f"PNG Map {map_type} - {key}", ext=utils.FileFormat.PNG, prefix="result")
|
|
|
988 pdfPath = utils.FilePath(f"PDF Map {map_type} - {key}", ext=utils.FileFormat.PDF, prefix="result")
|
|
|
989 convert_to_pdf(svgFilePath, pngPath, pdfPath)
|
|
|
990 if not ARGS.generate_svg:
|
|
|
991 os.remove(svgFilePath.show())
|
|
|
992
|
|
|
993
|
|
|
994
|
|
|
995
|
|
|
996 ############################ MAIN #############################################
|
|
|
997 def main() -> None:
|
|
|
998 """
|
|
|
999 Initializes everything and sets the program in motion based on the fronted input arguments.
|
|
|
1000
|
|
|
1001 Returns:
|
|
|
1002 None
|
|
|
1003
|
|
|
1004 Raises:
|
|
|
1005 sys.exit : if a user-provided custom map is in the wrong format (ET.XMLSyntaxError, ET.XMLSchemaParseError)
|
|
|
1006 """
|
|
|
1007
|
|
|
1008 global ARGS
|
|
|
1009 ARGS = process_args()
|
|
|
1010
|
|
|
1011 if os.path.isdir('result') == False: os.makedirs('result')
|
|
|
1012
|
|
|
1013 core_map :ET.ElementTree = ARGS.choice_map.getMap(
|
|
|
1014 ARGS.tool_dir,
|
|
|
1015 utils.FilePath.fromStrPath(ARGS.custom_map) if ARGS.custom_map else None)
|
|
|
1016 # TODO: ^^^ ugly but fine for now, the argument is None if the model isn't custom because no file was given.
|
|
|
1017 # getMap will None-check the customPath and panic when the model IS custom but there's no file (good). A cleaner
|
|
|
1018 # solution can be derived from my comment in FilePath.fromStrPath
|
|
|
1019
|
|
|
1020 ids, class_pat = getClassesAndIdsFromDatasets(ARGS.input_datas_fluxes, ARGS.input_data_fluxes, ARGS.input_class_fluxes, ARGS.names_fluxes)
|
|
|
1021
|
|
|
1022 if(ARGS.choice_map == utils.Model.HMRcore):
|
|
|
1023 temp_map = utils.Model.HMRcore_no_legend
|
|
|
1024 computeEnrichmentMeanMedian(temp_map.getMap(ARGS.tool_dir), class_pat, ids, ARGS.color_map)
|
|
|
1025 elif(ARGS.choice_map == utils.Model.ENGRO2):
|
|
|
1026 temp_map = utils.Model.ENGRO2_no_legend
|
|
|
1027 computeEnrichmentMeanMedian(temp_map.getMap(ARGS.tool_dir), class_pat, ids, ARGS.color_map)
|
|
|
1028 else:
|
|
|
1029 computeEnrichmentMeanMedian(core_map, class_pat, ids, ARGS.color_map)
|
|
|
1030
|
|
|
1031
|
|
|
1032 computeEnrichment(core_map, class_pat, ids)
|
|
|
1033
|
|
|
1034 # create output files: TODO: this is the same comparison happening in "maps", find a better way to organize this
|
|
|
1035 if ARGS.comparison == "manyvsmany":
|
|
|
1036 for i, j in it.combinations(class_pat.keys(), 2): createOutputMaps(i, j, core_map)
|
|
|
1037 return
|
|
|
1038
|
|
|
1039 if ARGS.comparison == "onevsrest":
|
|
|
1040 for single_cluster in class_pat.keys(): createOutputMaps(single_cluster, "rest", core_map)
|
|
|
1041 return
|
|
|
1042
|
|
|
1043 for otherDataset in class_pat.keys():
|
|
|
1044 if otherDataset != ARGS.control: createOutputMaps(i, j, core_map)
|
|
|
1045
|
|
|
1046 if not ERRORS: return
|
|
|
1047 utils.logWarning(
|
|
|
1048 f"The following reaction IDs were mentioned in the dataset but weren't found in the map: {ERRORS}",
|
|
|
1049 ARGS.out_log)
|
|
|
1050
|
|
|
1051 print('Execution succeded')
|
|
|
1052
|
|
|
1053 ###############################################################################
|
|
|
1054 if __name__ == "__main__":
|
|
|
1055 main() |
