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1 import argparse
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2 from viz_graphs import *
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3 import sys
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4 import pandas as pd
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5 import matplotlib.pyplot as plt
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6 import matplotlib.patches as mpatches
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7 import matplotlib.font_manager as font_manager
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8 import time
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9 from multiprocessing import Process, Queue, Lock, Pool, Manager, Value
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10
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11
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12 ##################################################################################################################################################################################################################
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13
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14 def top_diff(miRNA_info, number,flag,l):
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15
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16 Kind=[]
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17
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18 miRNA_info.sort(key = lambda x: abs(x[1]),reverse=True)
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19 miRNA_info = miRNA_info[:number]
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20 miRNA_info.sort(key = lambda x: x[0])
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21
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22 for x in miRNA_info:
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23 if x[1] > 0:
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24 Kind.append(True)
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25 elif x[1] < 0:
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26 Kind.append(False)
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27 else:
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28 Kind.append("Zero")
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29
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30 top_miRNA = {"Names": [x[0] for x in miRNA_info],
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31 "Log2FC": [x[1] for x in miRNA_info],
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32 "Kind": Kind};
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33
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34 df_miRNA = pd.DataFrame(data=top_miRNA)
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35 df_miRNA = df_miRNA.sort_values(by=['Names'])
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36 if df_miRNA.empty==False:
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37 h1=df_miRNA.plot.barh(x= 'Names',y='Log2FC',color=df_miRNA.Kind.map({True: 'g', False: 'r', 'Zero':'k'}))
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38 figure = plt.gcf() # get current figure
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39 figure.set_size_inches(5, 12) # set figure's size manually to your full screen (32x18)
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40 up_reg = mpatches.Patch(color='green', label='Upregulated')
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41 down_reg = mpatches.Patch(color='red', label='Downregulated')
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42 font = font_manager.FontProperties(weight='bold', style='normal')
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43 l3 = plt.legend(handles=[up_reg,down_reg],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
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44 h1.set_ylabel(" ", fontsize=3, fontweight='bold')
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45 h1.set_xlabel("Log2FC", fontsize=12, fontweight='bold')
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46 plt.axvline(x=0, color="k")
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47
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48 plt.grid(axis='y', linewidth=0.2)
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49 plt.grid(axis='x', linewidth=0.2)
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50 if flag=='t':
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51 plt.savefig('tem.png', bbox_inches='tight', dpi=300)
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52 if flag=='nt':
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53 plt.savefig('non.png', bbox_inches='tight', dpi=300)
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54
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55 ####################################################################################################################################################################################################################
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56
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57 def unique(sequence):
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58 seen = set()
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59 return [x for x in sequence if not (x in seen or seen.add(x))]
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60
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61 ###########################################################################################################################################################################################################################################################################
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62
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63 def top_scatter_non(matures,isoforms,non_temp,uni_names,number):
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64
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65 mat_names=[]
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66 mat_log2fc=[]
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67
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68 iso_names=[]
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69 iso_log2fc=[]
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70
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71 non_temp_names=[]
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72 non_temp_log2fc=[]
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73
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74 count=0
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75 for x in uni_names:
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76 flag = False
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77 if count<number:
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78 for y in matures:
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79 if x in y[0]:
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80 mat_log2fc.append(y[1])
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81 mat_names.append(x)
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82 flag=True
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83 for y in isoforms:
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84 if x in y[0]:
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85 iso_log2fc.append(y[1])
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86 iso_names.append(x)
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87 flag=True
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88 for y in non_temp:
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89 if x in y[0]:
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90 non_temp_log2fc.append(y[1])
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91 non_temp_names.append(x)
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92 flag=True
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93 if flag==True:
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94 count+=1
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95
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96 mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
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97 iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
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98 non_df = pd.DataFrame(dict(names=non_temp_names, log2fc= non_temp_log2fc))
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99
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100 iso_df.sort_values(by=['names'])
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101 mat_df.sort_values(by=['names'])
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102 non_df.sort_values(by=['names'])
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103
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104 fig, ax = plt.subplots()
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105
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106 h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red')
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107 h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green')
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108 h2=ax.scatter(non_df['log2fc'],non_df['names'],edgecolors='k',linewidth=1, marker='o', c='blue')
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109
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110 l3 = plt.legend([h1,h2,h3],["Reference miRNA","Non-template","Template isomiRs"],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
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111 plt.axvline(x=0, color="k")
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112 plt.grid(axis='y', linewidth=0.2)
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113 plt.grid(axis='x', linewidth=0.2)
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114 plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
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115 plt.yticks(rotation=0,ha="right", fontsize=10)
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116 plt.xticks(rotation=0,ha="right", fontsize=10)
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117 plt.tight_layout()
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118 figure = plt.gcf() # get current figure
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119 figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
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120 plt.savefig('a2.png', bbox_inches='tight', dpi=300)
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121
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122 #########################################################################################################################################################################################################################################
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123 def top_scatter_tem(matures,isoforms,uni_names,number):
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124
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125 mat_names=[]
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126 mat_log2fc=[]
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127
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128 iso_names=[]
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129 iso_log2fc=[]
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130
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131 count=0
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132 for x in uni_names:
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133 flag = False
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134 if count<number:
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135 for y in matures:
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136 if x in y[0]:
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137 mat_log2fc.append(y[1])
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138 mat_names.append(x)
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139 flag=True
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140 for y in isoforms:
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141 if x in y[0]:
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142 iso_log2fc.append(y[1])
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143 iso_names.append(x)
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144 flag=True
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145 if flag==True:
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146 count+=1
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147
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148 mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
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149 iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
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150
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151 iso_df.sort_values(by=['names'])
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152 mat_df.sort_values(by=['names'])
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153
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154 fig, ax = plt.subplots()
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155
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156 h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red')
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157 h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green')
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158
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159 l3 = plt.legend([h1,h3],["Reference miRNA","Template isomiRs"],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
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160 plt.axvline(x=0, color="k")
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161 plt.grid(axis='y', linewidth=0.2)
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162 plt.grid(axis='x', linewidth=0.2)
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163 plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
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164 plt.yticks(rotation=0,ha="right", fontsize=10)
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165 plt.xticks(rotation=0,ha="right", fontsize=10)
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166 plt.tight_layout()
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167 figure = plt.gcf() # get current figure
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168 figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
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169 plt.savefig('a2.png', bbox_inches='tight', dpi=300)
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170
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171
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172 ##############################################################################################################################################################################################################################################
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173 def preproccess(non_templated,matures,isoforms,log2fc,pval):
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174
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175 non_temp = [[x[0],float(x[1]),float(x[2])] for x in non_templated if abs(float(x[1]))>log2fc and float(x[2])<pval]
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176 mat = [[x[0],float(x[1]),float(x[2])] for x in matures if abs(float(x[1]))>log2fc and float(x[2])<pval]
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177 iso = [[x[0],float(x[1]),float(x[2])] for x in isoforms if abs(float(x[1]))>log2fc and float(x[2])<pval]
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178 mat_iso = mat+iso
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179
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180 if not non_temp and not mat and not iso:
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181 sys.exit("There aren't entries which meet these criteria")
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182
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183 mat.sort(key = lambda x: abs(float(x[1])),reverse=True)
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184 iso.sort(key = lambda x: abs(float(x[1])),reverse=True)
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185 non_temp.sort(key = lambda x: abs(float(x[1])),reverse=True)
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186
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187 all=mat+iso+non_temp
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188 all.sort(key = lambda x: abs(float(x[1])), reverse=True)
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189 names=[x[0].split("_")[0] for x in all]
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190 uni_names=unique(names)
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191
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192 diff_non_templated = [[x[0],float(x[1]),float(x[2])] for x in non_templated if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
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193 diff_matures = [[x[0],float(x[1]),float(x[2])] for x in matures if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
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194 diff_isoforms = [[x[0],float(x[1]),float(x[2])] for x in isoforms if abs(float(x[1]))>1 and float(x[2])<pval and x[0].split("_")[0] in uni_names]
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195
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196 diff_matures.sort(key = lambda x: abs(float(x[1])),reverse=True)
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197 diff_isoforms.sort(key = lambda x: abs(float(x[1])),reverse=True)
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198 diff_non_templated.sort(key = lambda x: abs(float(x[1])),reverse=True)
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199
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200 return diff_matures,diff_isoforms,diff_non_templated,uni_names,non_temp,mat_iso
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201
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202 ##################################################################################################################################################################################################################################################################
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203 starttime = time.time()
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204
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205 parser = argparse.ArgumentParser()
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206 parser.add_argument("-in", "--input", help="choose type of analysis", action="store")
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207 parser.add_argument("-p_value", "--pval", help="choose type of analysis", action="store")
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208 parser.add_argument("-fc", "--log2fc", help="choose type of analysis", action="store")
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209 parser.add_argument("-top", "--top_mirnas", help="choose type of analysis", action="store")
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210 parser.add_argument("-tool_dir", "--tool_directory", help="tool directory path", action="store")
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211 parser.add_argument("-statistic", "--stat", help="tool directory path", action="store")
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212 parser.add_argument("-diff_tool", "--tool", help="tool directory path", action="store")
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213
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214 args = parser.parse_args()
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215
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216 l=Lock()
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217 number = int(args.top_mirnas)
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218 log2fc = float(args.log2fc)
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219 pval = float(args.pval)
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220
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221 if args.tool=="2":
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222
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223 raw_EdgeR = read(args.input,0)
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224 EdgeR = [x.rstrip("\n").split("\t") for x in raw_EdgeR]
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225 del EdgeR[0]
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226
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227 if args.stat=="1":
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228 non_templated = [[x[0],x[1],x[4]] for x in EdgeR if "__" in x[0] and x[1]!="NA" and x[4]!="NA"]
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229 matures = [[x[0],x[1],x[4]] for x in EdgeR if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[4]!="NA"]
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230 isoforms = [[x[0],x[1],x[4]] for x in EdgeR if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[4]!="NA"]
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231 else:
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232 non_templated = [[x[0],x[1],x[5]] for x in EdgeR if "__" in x[0] and x[1]!="NA" and x[5]!="NA"]
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233 matures = [[x[0],x[1],x[5]] for x in EdgeR if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[5]!="NA"]
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234 isoforms = [[x[0],x[1],x[5]] for x in EdgeR if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[1]!="NA" and x[5]!="NA"]
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235
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236 if args.tool=="1":
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237
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238 raw_Deseq = read(args.input,0)
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239 Deseq = [x.rstrip("\n").split("\t") for x in raw_Deseq]
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240
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241 if args.stat=="1":
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242 non_templated = [[x[0],x[2],x[5]] for x in Deseq if "__" in x[0] and x[2]!="NA" and x[5]!="NA"]
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243 matures = [[x[0],x[2],x[5]] for x in Deseq if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[5]!="NA"]
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244 isoforms = [[x[0],x[2],x[5]] for x in Deseq if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[5]!="NA"]
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245 else:
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246 non_templated = [[x[0],x[2],x[6]] for x in Deseq if "__" in x[0] and x[2]!="NA" and x[6]!="NA"]
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247 matures = [[x[0],x[2],x[6]] for x in Deseq if 'chr' in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[6]!="NA"]
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248 isoforms = [[x[0],x[2],x[6]] for x in Deseq if 'chr' not in x[0].split("_")[-1] and "__" not in x[0] and x[2]!="NA" and x[6]!="NA"]
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249
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250
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251 diff_matures,diff_isoforms,diff_non_templated,names,non_temp,mat_iso = preproccess(non_templated,matures,isoforms,log2fc,pval)
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252
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253 if non_templated!=[]:
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254 analysis="2"
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255 p=[Process(target=top_diff,args=(non_temp,number,"nt",l))]
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256 p.extend([Process(target=top_diff,args=(mat_iso,number,"t",l))])
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257 p.extend([Process(target=top_scatter_non,args=(diff_matures,diff_isoforms,diff_non_templated,names,number))])
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258
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259 else:
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260 analysis="1"
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261 p=[Process(target=top_diff,args=(mat_iso,number,"t"))]
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262 p.extend([Process(target=top_scatter_tem,args=(diff_matures,diff_isoforms,names,number))])
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263
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264 [x.start() for x in p]
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265 [x.join() for x in p]
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266
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267 pdf_after_DE(analysis,args.top_mirnas)
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268
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269 print('That took {} seconds'.format(time.time() - starttime))
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270
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