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1 import pandas as pd
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2 import matplotlib.patches as mpatches
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3 import matplotlib.font_manager as font_manager
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4 import matplotlib.pyplot as plt
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5
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6 #########################################################################################
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7
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8 # Read a file and return it as a list
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9 def read(path, flag):
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10 if flag == 0:
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11 with open(path) as fp:
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12 file=fp.readlines()
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13 fp.close()
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14 return file
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15
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16 if flag == 1:
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17 with open(path) as fp:
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18 file = fp.read().splitlines()
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19 fp.close()
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20 return file
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21
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22 # Write a list to a txt file
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23 def write(path, list):
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24 with open(path,'w') as fp:
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25 for x in list:
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26 fp.write(str("\t".join(x[1:-1])))
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27 fp.close()
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28
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29
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30 ################################################################################################################################################################>
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31
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32 def top_diff(miRNA_info, number,flag,l):
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33
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34 Kind=[]
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35
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36 miRNA_info.sort(key = lambda x: abs(x[1]),reverse=True)
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37 miRNA_info = miRNA_info[:number]
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38 miRNA_info.sort(key = lambda x: x[0])
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39
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40 for x in miRNA_info:
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41 if x[1] > 0:
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42 Kind.append(True)
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43 elif x[1] < 0:
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44 Kind.append(False)
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45 else:
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46 Kind.append("Zero")
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47
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48 top_miRNA = {"Names": [x[0] for x in miRNA_info],
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49 "Log2FC": [x[1] for x in miRNA_info],
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50 "Kind": Kind};
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51
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52 df_miRNA = pd.DataFrame(data=top_miRNA)
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53 df_miRNA = df_miRNA.sort_values(by=['Names'])
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54 if df_miRNA.empty==False:
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55 h1=df_miRNA.plot.barh(x= 'Names',y='Log2FC',color=df_miRNA.Kind.map({True: 'g', False: 'r', 'Zero':'k'}))
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56 figure = plt.gcf() # get current figure
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57 figure.set_size_inches(5, 12) # set figure's size manually to your full screen (32x18)
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58 up_reg = mpatches.Patch(color='green', label='Upregulated')
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59 down_reg = mpatches.Patch(color='red', label='Downregulated')
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60 font = font_manager.FontProperties(weight='bold', style='normal')
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61 l3 = plt.legend(handles=[up_reg,down_reg],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
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62 h1.set_ylabel(" ", fontsize=3, fontweight='bold')
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63 h1.set_xlabel("Log2FC", fontsize=12, fontweight='bold')
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64 plt.axvline(x=0, color="k")
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65
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66 plt.grid(axis='y', linewidth=0.2)
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67 plt.grid(axis='x', linewidth=0.2)
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68 if flag=='t':
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69 plt.savefig('tem.png', bbox_inches='tight', dpi=300)
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70 if flag=='nt':
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71 plt.savefig('non.png', bbox_inches='tight', dpi=300)
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72
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73
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74 ################################################################################################################################################################>
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75
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76 def unique(sequence):
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77 seen = set()
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78 return [x for x in sequence if not (x in seen or seen.add(x))]
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79
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80 ################################################################################################################################################################>
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81
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82 def top_scatter_non(matures,isoforms,non_temp,uni_names,number):
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83
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84 mat_names=[]
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85 mat_log2fc=[]
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86
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87 iso_names=[]
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88 iso_log2fc=[]
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89
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90 non_temp_names=[]
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91 non_temp_log2fc=[]
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92
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93 count=0
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94 for x in uni_names:
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95 flag = False
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96 if count<number:
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97 for y in matures:
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98 if x in y[0]:
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99 mat_log2fc.append(y[1])
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100 mat_names.append(x)
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101 flag=True
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102 for y in isoforms:
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103 if x in y[0]:
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104 iso_log2fc.append(y[1])
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105 iso_names.append(x)
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106 flag=True
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107 for y in non_temp:
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108 if x in y[0]:
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109 non_temp_log2fc.append(y[1])
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110 non_temp_names.append(x)
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111 flag=True
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112 if flag==True:
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113 count+=1
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114
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115 mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
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116 iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
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117 non_df = pd.DataFrame(dict(names=non_temp_names, log2fc= non_temp_log2fc))
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118
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119 iso_df.sort_values(by=['names'])
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120 mat_df.sort_values(by=['names'])
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121 non_df.sort_values(by=['names'])
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122
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123 fig, ax = plt.subplots()
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124
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125 h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red',alpha=0.4)
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126 h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green',alpha=0.4)
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127 h2=ax.scatter(non_df['log2fc'],non_df['names'],edgecolors='k',linewidth=1, marker='o', c='orange',alpha=0.4)
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128
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129 l3 = plt.legend([h1,h2,h3],["RefSeq miRNA","Non-templated isomiR","Templated isomiR"],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
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130 plt.axvline(x=0, color="k")
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131 plt.grid(axis='y', linewidth=0.2)
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132 plt.grid(axis='x', linewidth=0.2)
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133 plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
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134 plt.yticks(rotation=0,ha="right", fontsize=10)
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135 plt.xticks(rotation=0,ha="right", fontsize=10)
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136 plt.tight_layout()
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137 figure = plt.gcf() # get current figure
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138 figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
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139 plt.savefig('a2.png', bbox_inches='tight', dpi=300)
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140
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141 #########################################################################################################################################################################################################################################
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142
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143 def top_scatter_tem(matures,isoforms,uni_names,number):
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144
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145 mat_names=[]
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146 mat_log2fc=[]
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147
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148 iso_names=[]
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149 iso_log2fc=[]
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150
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151 count=0
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152 for x in uni_names:
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153 flag = False
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154 if count<number:
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155 for y in matures:
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156 if x in y[0]:
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157 mat_log2fc.append(y[1])
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158 mat_names.append(x)
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159 flag=True
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160 for y in isoforms:
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161 if x in y[0]:
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162 iso_log2fc.append(y[1])
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163 iso_names.append(x)
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164 flag=True
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165 if flag==True:
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166 count+=1
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167
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168 mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
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169 iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
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170
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171 iso_df.sort_values(by=['names'])
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172 mat_df.sort_values(by=['names'])
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173
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174 fig, ax = plt.subplots()
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175
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176 h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red',alpha=0.4)
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177 h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green',alpha=0.4)
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178
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179 l3 = plt.legend([h1,h3],["RefSeq miRNA","Templated isomiR"],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
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180 plt.axvline(x=0, color="k")
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181 plt.grid(axis='y', linewidth=0.2)
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182 plt.grid(axis='x', linewidth=0.2)
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183 plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
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184 plt.yticks(rotation=0,ha="right", fontsize=10)
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185 plt.xticks(rotation=0,ha="right", fontsize=10)
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186 plt.tight_layout()
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187 figure = plt.gcf() # get current figure
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188 figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
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189 plt.savefig('a2.png', bbox_inches='tight', dpi=300)
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190
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191
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192 ##############################################################################################################################################################################################################################################
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193
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194 def preproccess(non_templated,matures,isoforms,log2fc,pval,stat):
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195
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196 if stat=="3":
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197 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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198 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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199 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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200 else:
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201 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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202 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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203 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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204
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205 mat_iso = mat+iso
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206
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207 if not non_temp and not mat and not iso:
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208 sys.exit("There aren't entries which meet these criteria")
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209
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210 mat.sort(key = lambda x: abs(float(x[1])),reverse=True)
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211 iso.sort(key = lambda x: abs(float(x[1])),reverse=True)
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212 non_temp.sort(key = lambda x: abs(float(x[1])),reverse=True)
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213
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214 all=mat+iso+non_temp
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215 all.sort(key = lambda x: abs(float(x[1])), reverse=True)
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216 names=[x[0].split("_")[0] for x in all]
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217 uni_names=unique(names)
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218
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219 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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220 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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221 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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222
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223 diff_matures.sort(key = lambda x: abs(float(x[1])),reverse=True)
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224 diff_isoforms.sort(key = lambda x: abs(float(x[1])),reverse=True)
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225 diff_non_templated.sort(key = lambda x: abs(float(x[1])),reverse=True)
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226
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227 return diff_matures,diff_isoforms,diff_non_templated,uni_names,non_temp,mat_iso
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228
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229 ################################################################################################################################################################################################################################################>
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230
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