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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 import sys
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6
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7 #########################################################################################
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8
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9 # Read a file and return it as a list
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10 def read(path, flag):
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11 if flag == 0:
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12 with open(path) as fp:
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13 file=fp.readlines()
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14 fp.close()
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15 return file
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16
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17 if flag == 1:
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18 with open(path) as fp:
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19 file = fp.read().splitlines()
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20 fp.close()
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21 return file
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22
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23 # Write a list to a txt file
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24 def write(path, list):
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25 with open(path,'w') as fp:
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26 for x in list:
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27 fp.write(str("\t".join(x[1:-1])))
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28 fp.close()
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29
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30
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31 ################################################################################################################################################################>
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32
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33 def top_diff(miRNA_info, number,flag,l):
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34
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35 Kind=[]
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36
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37 miRNA_info.sort(key = lambda x: abs(x[1]),reverse=True)
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38 miRNA_info = miRNA_info[:number]
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39 miRNA_info.sort(key = lambda x: x[0])
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40
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41 for x in miRNA_info:
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42 if x[1] > 0:
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43 Kind.append(True)
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44 elif x[1] < 0:
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45 Kind.append(False)
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46 else:
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47 Kind.append("Zero")
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48
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49 top_miRNA = {"Names": [x[0] for x in miRNA_info],
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50 "Log2FC": [x[1] for x in miRNA_info],
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51 "Kind": Kind};
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52
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53 df_miRNA = pd.DataFrame(data=top_miRNA)
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54 df_miRNA = df_miRNA.sort_values(by=['Names'])
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55 if df_miRNA.empty==False:
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56 h1=df_miRNA.plot.barh(x= 'Names',y='Log2FC',color=df_miRNA.Kind.map({True: 'g', False: 'r', 'Zero':'k'}))
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57 figure = plt.gcf() # get current figure
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58 figure.set_size_inches(5, 12) # set figure's size manually to your full screen (32x18)
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59 up_reg = mpatches.Patch(color='green', label='Upregulated')
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60 down_reg = mpatches.Patch(color='red', label='Downregulated')
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61 font = font_manager.FontProperties(weight='bold', style='normal')
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62 l3 = plt.legend(handles=[up_reg,down_reg],bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
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63 h1.set_ylabel(" ", fontsize=3, fontweight='bold')
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64 h1.set_xlabel("Log2FC", fontsize=12, fontweight='bold')
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65 plt.axvline(x=0, color="k")
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66
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67 plt.grid(axis='y', linewidth=0.2)
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68 plt.grid(axis='x', linewidth=0.2)
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69 if flag=='t':
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70 plt.savefig('tem.png', bbox_inches='tight', dpi=300)
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71 if flag=='nt':
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72 plt.savefig('non.png', bbox_inches='tight', dpi=300)
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73
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74
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75 ################################################################################################################################################################>
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76
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77 def unique(sequence):
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78 seen = set()
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79 return [x for x in sequence if not (x in seen or seen.add(x))]
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80
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81 ################################################################################################################################################################>
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82
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83 def top_scatter_non(matures,isoforms,non_temp,uni_names,number):
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84
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85 mat_names=[]
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86 mat_log2fc=[]
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87
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88 iso_names=[]
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89 iso_log2fc=[]
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90
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91 non_temp_names=[]
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92 non_temp_log2fc=[]
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93
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94 count=0
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95 for x in uni_names:
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96 flag = False
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97 if count<number:
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98 for y in matures:
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99 if x in y[0]:
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100 mat_log2fc.append(y[1])
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101 mat_names.append(x)
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102 flag=True
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103 for y in isoforms:
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104 if x in y[0]:
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105 iso_log2fc.append(y[1])
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106 iso_names.append(x)
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107 flag=True
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108 for y in non_temp:
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109 if x in y[0]:
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110 non_temp_log2fc.append(y[1])
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111 non_temp_names.append(x)
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112 flag=True
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113 if flag==True:
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114 count+=1
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115
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116 mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
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117 iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
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118 non_df = pd.DataFrame(dict(names=non_temp_names, log2fc= non_temp_log2fc))
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119
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120 iso_df.sort_values(by=['names'])
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121 mat_df.sort_values(by=['names'])
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122 non_df.sort_values(by=['names'])
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123
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124 fig, ax = plt.subplots()
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125
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126 h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red',alpha=0.4)
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127 h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green',alpha=0.4)
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128 h2=ax.scatter(non_df['log2fc'],non_df['names'],edgecolors='k',linewidth=1, marker='o', c='orange',alpha=0.4)
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129
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130 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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131 plt.axvline(x=0, color="k")
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132 plt.grid(axis='y', linewidth=0.2)
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133 plt.grid(axis='x', linewidth=0.2)
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134 plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
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135 plt.yticks(rotation=0,ha="right", fontsize=10)
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136 plt.xticks(rotation=0,ha="right", fontsize=10)
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137 plt.tight_layout()
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138 figure = plt.gcf() # get current figure
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139 figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
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140 plt.savefig('a2.png', bbox_inches='tight', dpi=300)
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141
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142 #########################################################################################################################################################################################################################################
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143
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144 def top_scatter_tem(matures,isoforms,uni_names,number):
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145
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146 mat_names=[]
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147 mat_log2fc=[]
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148
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149 iso_names=[]
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150 iso_log2fc=[]
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151
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152 count=0
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153 for x in uni_names:
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154 flag = False
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155 if count<number:
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156 for y in matures:
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157 if x in y[0]:
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158 mat_log2fc.append(y[1])
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159 mat_names.append(x)
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160 flag=True
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161 for y in isoforms:
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162 if x in y[0]:
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163 iso_log2fc.append(y[1])
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164 iso_names.append(x)
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165 flag=True
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166 if flag==True:
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167 count+=1
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168
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169 mat_df = pd.DataFrame(dict(names=mat_names, log2fc=mat_log2fc))
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170 iso_df = pd.DataFrame(dict(names=iso_names, log2fc=iso_log2fc))
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171
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172 iso_df.sort_values(by=['names'])
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173 mat_df.sort_values(by=['names'])
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174
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175 fig, ax = plt.subplots()
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176
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177 h3=ax.scatter(iso_df['log2fc'],iso_df['names'],edgecolors='k',linewidth=1, marker='o', c='red',alpha=0.4)
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178 h1=ax.scatter(mat_df['log2fc'],mat_df['names'],edgecolors='k',linewidth=1, marker='o', c='green',alpha=0.4)
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179
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180 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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181 plt.axvline(x=0, color="k")
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182 plt.grid(axis='y', linewidth=0.2)
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183 plt.grid(axis='x', linewidth=0.2)
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184 plt.xlabel("Log2FC", fontsize=12, fontweight='bold')
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185 plt.yticks(rotation=0,ha="right", fontsize=10)
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186 plt.xticks(rotation=0,ha="right", fontsize=10)
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187 plt.tight_layout()
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188 figure = plt.gcf() # get current figure
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189 figure.set_size_inches(16, 12) # set figure's size manually to your full screen (32x18)
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190 plt.savefig('a2.png', bbox_inches='tight', dpi=300)
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191
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192
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193 ##############################################################################################################################################################################################################################################
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194
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195 def preproccess(non_templated,matures,isoforms,log2fc,pval,stat):
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196
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197 if stat=="3":
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198 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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199 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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200 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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201 else:
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202 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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203 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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204 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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205
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206 mat_iso = mat+iso
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207
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208 if not non_temp and not mat and not iso:
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209 sys.exit("There aren't entries which meet these criteria")
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210
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211 mat.sort(key = lambda x: abs(float(x[1])),reverse=True)
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212 iso.sort(key = lambda x: abs(float(x[1])),reverse=True)
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213 non_temp.sort(key = lambda x: abs(float(x[1])),reverse=True)
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214
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215 all=mat+iso+non_temp
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216 all.sort(key = lambda x: abs(float(x[1])), reverse=True)
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217 names=[x[0].split("_")[0] for x in all]
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218 uni_names=unique(names)
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219
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220 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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221 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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222 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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223
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224 diff_matures.sort(key = lambda x: abs(float(x[1])),reverse=True)
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225 diff_isoforms.sort(key = lambda x: abs(float(x[1])),reverse=True)
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226 diff_non_templated.sort(key = lambda x: abs(float(x[1])),reverse=True)
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227
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228 return diff_matures,diff_isoforms,diff_non_templated,uni_names,non_temp,mat_iso
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229
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230 ################################################################################################################################################################################################################################################>
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231
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