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1 import itertools
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2 import pandas as pd
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3 from math import pi
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4 import numpy as np
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5 import matplotlib.pyplot as plt
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6 import math
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7 import logomaker as lm
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8 from fpdf import FPDF, fpdf
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9 import glob
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10
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11 ###############################################################################################################################################################################
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12 def pie_non_temp(merge_con,merge_non_con,merge_tre,merge_non_tre,c_unmap,t_unmap,c_unmap_counts,t_unmap_counts,group_name1,group_name2):
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13
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14 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_con]
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15 t_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_tre]
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16 c_non_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_non_con]
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17 t_non_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_non_tre]
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18
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19 c_templ = 0
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20 c_tem_counts = 0
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21 c_mature = 0
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22 c_mat_counts = 0
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23 t_templ = 0
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24 t_tem_counts = 0
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25 t_mature = 0
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26 t_mat_counts = 0
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27
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28 c_non = len(c_non_samples)
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29 c_non_counts = sum(x[2] for x in c_non_samples)
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30 t_non = len(t_non_samples)
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31 t_non_counts = sum(x[2] for x in t_non_samples)
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32
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33 c_unmap = c_unmap - c_non
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34 t_unmap = c_unmap - t_non
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35
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36 c_unmap_counts=c_unmap_counts - c_non_counts
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37 t_unmap_counts=t_unmap_counts - t_non_counts
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38
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39
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40 for x in c_samples:
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41
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42 if "/" not in x[0]:
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43 if len(x[0].split("_"))==2:
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44 c_mature+=1
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45 c_mat_counts += x[2]
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46 else:
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47 c_templ+=1
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48 c_tem_counts += x[2]
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49 else:
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50 f=0
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51 for y in x[0].split("/"):
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52 if len(y.split("_"))==2:
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53 c_mature+=1
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54 c_mat_counts += x[2]
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55 f=1
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56 break
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57 if f==0:
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58 c_templ+=1
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59 c_tem_counts += x[2]
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60
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61 for x in t_samples:
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62
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63 if "/" not in x[0]:
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64 if len(x[0].split("_"))==2:
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65 t_mature+=1
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66 t_mat_counts += x[2]
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67 else:
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68 t_templ+=1
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69 t_tem_counts += x[2]
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70 else:
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71 f=0
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72 for y in x[0].split("/"):
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73 if len(y.split("_"))==2:
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74 t_mature+=1
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75 t_mat_counts += x[2]
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76 f=1
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77 break
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78 if f==0:
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79 t_templ+=1
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80 t_tem_counts += x[2]
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81
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82 fig = plt.figure(figsize=(7,5))
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83
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84 labels = 'miRNA RefSeq','templated', 'unassigned' ,'non-templated'
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85 sizes = [c_mat_counts, c_tem_counts, c_unmap_counts,c_non_counts]
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86 colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
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87 # Plot
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88 ax1 = plt.subplot2grid((1,2),(0,0))
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89 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
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90 [x.set_fontsize(10) for x in texts]
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91 plt.title(group_name1.capitalize() + ' group (reads)',fontsize=12)
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92
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93 labels = 'miRNA RefSeq','templated', 'unassigned','non-templated'
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94 sizes = [t_mat_counts, t_tem_counts, t_unmap_counts, t_non_counts]
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95 colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
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96 # Plot
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97 ax2 = plt.subplot2grid((1,2),(0,1))
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98 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
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99 [x.set_fontsize(10) for x in texts]
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100 plt.title(group_name2.capitalize() + ' group (reads)', fontsize=12)
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101 plt.savefig('pie_non.png',dpi=300)
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102
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103 ####################################################################################################################################################################################################################
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104
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105 def pie_temp(merge_con,c_unmap,c_unmap_counts,merge_tre,t_unmap,t_unmap_counts,group_name1,group_name2):
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106
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107 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_con]
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108 t_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_tre]
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109
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110 c_templ = 0
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111 c_tem_counts = 0
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112 c_mature = 0
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113 c_mat_counts = 0
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114 t_templ = 0
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115 t_tem_counts = 0
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116 t_mature = 0
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117 t_mat_counts = 0
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118
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119 for x in c_samples:
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120
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121 if "/" not in x[0]:
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122 if len(x[0].split("_"))==2:
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123 c_mature+=1
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124 c_mat_counts += x[2]
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125 else:
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126 c_templ+=1
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127 c_tem_counts += x[2]
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128 else:
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129 f=0
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130 for y in x[0].split("/"):
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131 if len(y.split("_"))==2:
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132 c_mature+=1
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133 c_mat_counts += x[2]
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134 f=1
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135 break
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136 if f==0:
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137 c_templ+=1
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138 c_tem_counts += x[2]
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139
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140 for x in t_samples:
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141
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142 if "/" not in x[0]:
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143 if len(x[0].split("_"))==2:
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144 t_mature+=1
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145 t_mat_counts += x[2]
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146 else:
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147 t_templ+=1
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148 t_tem_counts += x[2]
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149 else:
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150 f=0
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151 for y in x[0].split("/"):
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152 if len(y.split("_"))==2:
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153 t_mature+=1
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154 t_mat_counts += x[2]
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155 f=1
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156 break
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157 if f==0:
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158 t_templ+=1
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159 t_tem_counts += x[2]
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160
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161
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162 fig = plt.figure()
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163 labels = 'miRNA RefSeq','templated', 'unassigned'
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164 sizes = [c_mat_counts, c_tem_counts, c_unmap_counts]
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165 colors = ['gold', 'yellowgreen', 'lightskyblue']
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166 explode = (0.2, 0.05, 0.1)
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167 # Plot
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168 ax1 = plt.subplot2grid((1,2),(0,0))
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169 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
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170 [x.set_fontsize(10) for x in texts]
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171 plt.title(group_name1.capitalize() + ' group (reads)', fontsize=12)
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172 labels = 'miRNA RefSeq','templated', 'Unassigned'
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173 sizes = [t_mat_counts, t_tem_counts, t_unmap_counts]
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174 colors = ['gold', 'yellowgreen', 'lightskyblue']
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175 explode = (0.2, 0.05, 0.1)
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176 # Plot
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177 ax2 = plt.subplot2grid((1,2),(0,1))
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178 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
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179 [x.set_fontsize(10) for x in texts]
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180 plt.title(group_name2.capitalize() + ' group (reads)',fontsize = 12)
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181 plt.savefig('pie_tem.png',dpi=300)
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182
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183 ###################################################################################################################################################################################################################
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184
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185 def make_spider(merge_con,merge_tre,group_name1,group_name2):
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186
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187 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_con]
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188 t_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_tre]
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189
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190 c_5 = 0
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191 c_5_counts = 0
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192 c_3 = 0
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193 c_3_counts = 0
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194 c_both =0
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195 c_both_counts=0
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196 c_mature = 0
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197 c_mat_counts = 0
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198 c_exception=0
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199 c_exception_counts=0
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200
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201
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202 t_5 = 0
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203 t_5_counts = 0
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204 t_3 = 0
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205 t_3_counts = 0
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206 t_both = 0
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207 t_both_counts = 0
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208 t_mature = 0
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209 t_mat_counts = 0
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210 t_exception = 0
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211 t_exception_counts=0
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212
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213 for x in c_samples:
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214
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215 if "/" not in x[0]:
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216 if len(x[0].split("_"))==2:
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217 c_mature+=1
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218 c_mat_counts += x[2]
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219 elif 0 == int(x[0].split("_")[3]):
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220 c_5+=1
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221 c_5_counts += x[2]
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222 elif 0 == int(x[0].split("_")[4]):
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223 c_3+=1
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224 c_3_counts += x[2]
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225 else:
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226 c_both+=1
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227 c_both_counts+=x[2]
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228
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229 else:
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230 f=0
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231 for y in x[0].split("/"):
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232 if len(y.split("_"))==2:
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233 c_mature+=1
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234 c_mat_counts += x[2]
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235 f=1
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236 break
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237 if f==0:
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238 part=x[0].split("/")[0].split("_")[-2]+"_"+x[0].split("/")[0].split("_")[-1]
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239 num=0
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240 for y in x[0].split("/"):
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241 if y.split("_")[-2]+"_"+y.split("_")[-1]==part:
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242 num+=1
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243 if num==len(x[0].split("/")):
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244
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245 if 0 == int(x[0].split("/")[0].split("_")[3]):
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246 c_5+=1
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247 c_5_counts += x[2]
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248 elif 0 == int(x[0].split("/")[0].split("_")[4]):
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249 c_3+=1
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250 c_3_counts += x[2]
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251 else:
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252 c_both+=1
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253 c_both_counts+=x[2]
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254 else:
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255 c_exception+=1
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256 c_exception_counts += x[2]
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257
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258
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259 for x in t_samples:
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260
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261 if "/" not in x[0]:
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262 if len(x[0].split("_"))==2:
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263 t_mature+=1
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264 t_mat_counts += x[2]
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265 elif 0 == int(x[0].split("_")[3]):
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266 t_5+=1
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267 t_5_counts += x[2]
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268 elif 0 == int(x[0].split("_")[4]):
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269 t_3+=1
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270 t_3_counts += x[2]
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271 else:
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272 t_both+=1
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273 t_both_counts+=x[2]
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274
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275 else:
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276 f=0
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277 for y in x[0].split("/"):
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278 if len(y.split("_"))==2:
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279 t_mature+=1
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280 t_mat_counts += x[2]
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281 f=1
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282 break
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283 if f==0:
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284 part=x[0].split("/")[0].split("_")[-2]+"_"+x[0].split("/")[0].split("_")[-1]
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285 num=0
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286 for y in x[0].split("/"):
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287 if y.split("_")[-2]+"_"+y.split("_")[-1]==part:
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288 num+=1
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289 if num==len(x[0].split("/")):
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290
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291 if 0 == int(x[0].split("/")[0].split("_")[3]):
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292 t_5+=1
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293 t_5_counts += x[2]
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294 elif 0 == int(x[0].split("/")[0].split("_")[4]):
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295 t_3+=1
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296 t_3_counts += x[2]
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297 else:
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298 t_both+=1
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299 t_both_counts+=x[2]
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300 else:
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301 t_exception+=1
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302 t_exception_counts += x[2]
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303
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304
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305 c_all = c_5+c_3+c_both+c_mature+c_exception
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306 c_all_counts = c_5_counts + c_3_counts + c_both_counts + c_mat_counts + c_exception_counts
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307
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308 t_all = t_5+t_3+t_both+t_mature + t_exception
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309 t_all_counts = t_5_counts + t_3_counts + t_both_counts + t_mat_counts + t_exception_counts
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310
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311 c_5 = round(c_5/c_all*100,2)
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312 c_3 = round(c_3/c_all*100,2)
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313 c_both = round(c_both/c_all*100,2)
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314 c_mature = round(c_mature/c_all*100,2)
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315 c_exception = round(c_exception/c_all*100,2)
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316
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317 c_5_counts = round(c_5_counts/c_all_counts*100,2)
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318 c_3_counts = round(c_3_counts/c_all_counts*100,2)
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319 c_both_counts = round(c_both_counts/c_all_counts*100,2)
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320 c_mat_counts = round(c_mat_counts/c_all_counts*100,2)
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321 c_exception_counts = round(c_exception_counts/c_all_counts*100,2)
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322
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323 t_5 = round(t_5/t_all*100,2)
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324 t_3 = round(t_3/t_all*100,2)
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325 t_both = round(t_both/t_all*100,2)
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326 t_mature = round(t_mature/t_all*100,2)
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327 t_exception = round(t_exception/t_all*100,2)
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328
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329 t_5_counts = round(t_5_counts/t_all_counts*100,2)
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330 t_3_counts = round(t_3_counts/t_all_counts*100,2)
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331 t_both_counts = round(t_both_counts/t_all_counts*100,2)
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332 t_mat_counts = round(t_mat_counts/t_all_counts*100,2)
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333 t_exception_counts = round(t_exception_counts/t_all_counts*100,2)
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334
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335 radar_max = max(c_5, c_3, c_both,c_mature,c_exception,t_5,t_3,t_both,t_mature,t_exception)
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336 radar_max_counts = max(c_5_counts,c_3_counts,c_both_counts,c_mat_counts,c_exception_counts,t_5_counts,t_3_counts,t_both_counts,t_mat_counts,t_exception_counts)
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337
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338 df=pd.DataFrame({
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339 'group':[group_name1.capitalize(),group_name2.capitalize()],
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340 """5'3'-isomiRs""":[c_both,t_both],
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341 """3'-isomiRs""":[c_3,t_3],
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342 'RefSeq miRNA':[c_mature,t_mature],
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343 """5'-isomiRs""":[c_5,t_5],
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344 'others*':[c_exception,t_exception]})
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6
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345
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346 df1=pd.DataFrame({
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347 'group':[group_name1.capitalize(),group_name2.capitalize()],
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348 """5'-3'-isomiRs""":[c_both_counts,t_both_counts],
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349 """3'-isomiRs""":[c_3_counts,t_3_counts],
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350 'RefSeq miRNA':[c_mat_counts,t_mat_counts],
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351 """5'-isomiRs""":[c_5_counts,t_5_counts],
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17
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352 'others*':[c_exception_counts,t_exception_counts]})
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6
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353
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354 spider_last(df,radar_max,1,group_name1,group_name2)
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355 spider_last(df1,radar_max_counts,2,group_name1,group_name2)
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356
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357
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358 ##############################################################################################################################################################################################################
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359
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360 def spider_last(df,radar_max,flag,group_name1,group_name2):
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361 # ------- PART 1: Create background
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362 fig = plt.figure()
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363 # number of variable
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364 categories=list(df)[1:]
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365 N = len(categories)
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366
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367 # What will be the angle of each axis in the plot? (we divide the plot / number of variable)
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368 angles = [n / float(N) * 2 * pi for n in range(N)]
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369 angles += angles[:1]
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370
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371 # Initialise the spider plot
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372 ax = plt.subplot(111, polar=True)
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373
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374 # If you want the first axis to be on top:
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375 ax.set_theta_offset(pi/2)
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376 ax.set_theta_direction(-1)
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377
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378 # Draw one axe per variable + add labels labels yet
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17
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379 plt.xticks(angles[:-1], categories, fontsize=13)
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6
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380
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381 # Draw ylabels
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382 radar_max=round(radar_max+radar_max*0.1)
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383 mul=len(str(radar_max))-1
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384 maxi=int(math.ceil(radar_max / pow(10,mul))) * pow(10,mul)
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385 sep = round(maxi/4)
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17
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386 plt.yticks([sep, 2*sep, 3*sep, 4*sep, 5*sep], [str(sep)+'%', str(2*sep)+'%', str(3*sep)+'%', str(4*sep)+'%', str(5*sep)+'%'], color="grey", size=12)
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6
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387 plt.ylim(0, maxi)
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388
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389 # ------- PART 2: Add plots
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390
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391 # Plot each individual = each line of the data
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392 # I don't do a loop, because plotting more than 3 groups makes the chart unreadable
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393
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394 # Ind1
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395 values=df.loc[0].drop('group').values.flatten().tolist()
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396 values += values[:1]
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17
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397 ax.plot(angles, values,'-o', linewidth=1, linestyle='solid', label=group_name1.capitalize())
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6
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398 ax.fill(angles, values, 'b', alpha=0.1)
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399
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400 # Ind2
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401 values=df.loc[1].drop('group').values.flatten().tolist()
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402 values += values[:1]
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17
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403 ax.plot(angles, values, '-o' ,linewidth=1, linestyle='solid', label=group_name2.capitalize())
|
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6
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404 ax.fill(angles, values, 'r', alpha=0.1)
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405
|
|
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406 # Add legend
|
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407 if flag==1:
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17
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408 plt.legend(loc='upper right', prop={'size': 11}, bbox_to_anchor=(0.0, 0.1))
|
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6
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409 plt.savefig('spider_non_red.png',dpi=300)
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410 else:
|
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17
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411 plt.legend(loc='upper right', prop={'size': 11}, bbox_to_anchor=(0.0, 0.1))
|
|
6
|
412 plt.savefig('spider_red.png',dpi=300)
|
|
|
413
|
|
|
414 #############################################################################################################################################################################################################
|
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415
|
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416 def hist_red(samples,flag,group):
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417
|
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418 lengths=[]
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419 cat=[]
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420 total_reads=0
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|
|
421 seq=[]
|
|
|
422
|
|
|
423 if flag == "c":
|
|
17
|
424 title = "Length distribution of "+group.lower()+" group (redundant reads)"
|
|
6
|
425 if flag == "t":
|
|
17
|
426 title = "Length distribution of "+group.lower()+" group (redundant reads)"
|
|
6
|
427
|
|
|
428 for i in samples:
|
|
|
429 for x in i:
|
|
|
430 lengths.append(x[3])
|
|
|
431 if x[1]=="0":
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|
|
432 seq.append([x[3],x[0].split("-")[1],"Mapped"])
|
|
|
433 cat.append("Mapped")
|
|
|
434 if x[1] == "4":
|
|
|
435 seq.append([x[3],x[0].split("-")[1],"Unassigned"])
|
|
|
436 cat.append("Unassigned")
|
|
|
437
|
|
|
438 uni_len=list(set(lengths))
|
|
|
439 uni_len=[x for x in uni_len if x<=35]
|
|
|
440 low=min(lengths)
|
|
|
441 up=max(lengths)
|
|
|
442 seq.sort()
|
|
|
443 uni_seq=list(seq for seq,_ in itertools.groupby(seq))
|
|
|
444 dim=up-low
|
|
|
445
|
|
|
446 if dim>20:
|
|
|
447 s=5
|
|
|
448 else:
|
|
|
449 s=8
|
|
|
450
|
|
|
451 total_reads+=sum([int(x[1]) for x in uni_seq])
|
|
|
452
|
|
|
453 map_reads=[]
|
|
|
454 unmap_reads=[]
|
|
|
455 length=[]
|
|
|
456 for y in uni_len:
|
|
|
457 map_temp=0
|
|
|
458 unmap_temp=0
|
|
|
459 for x in uni_seq:
|
|
|
460 if x[0]==y and x[2]=="Mapped":
|
|
|
461 map_temp+=int(x[1])
|
|
|
462 if x[0]==y and x[2]=="Unassigned":
|
|
|
463 unmap_temp+=int(x[1])
|
|
|
464 if y<=35:
|
|
|
465 length.append(y)
|
|
|
466 map_reads.append(round(map_temp/total_reads*100,2))
|
|
|
467 unmap_reads.append(round(unmap_temp/total_reads*100,2))
|
|
|
468
|
|
|
469 ylim=max([sum(x) for x in zip(unmap_reads, map_reads)])
|
|
|
470 ylim=ylim+ylim*20/100
|
|
|
471 fig, ax = plt.subplots()
|
|
|
472 width=0.8
|
|
|
473 ax.bar(length, unmap_reads, width, label='Unassigned')
|
|
|
474 h=ax.bar(length, map_reads, width, bottom=unmap_reads, label='Mapped')
|
|
|
475 plt.xticks(np.arange(length[0], length[-1]+1, 1))
|
|
|
476 plt.yticks(np.arange(0, ylim, 5))
|
|
|
477 plt.xlabel('Length (nt)',fontsize=14)
|
|
|
478 plt.ylabel('Percentage',fontsize=14)
|
|
|
479 plt.title(title,fontsize=14)
|
|
|
480 ax.legend()
|
|
|
481 plt.ylim([0, ylim])
|
|
|
482 ax.grid(axis='y',linewidth=0.2)
|
|
|
483
|
|
|
484 if flag=='c':
|
|
|
485 plt.savefig('c_hist_red.png',dpi=300)
|
|
|
486
|
|
|
487 if flag=='t':
|
|
|
488 plt.savefig('t_hist_red.png',dpi=300)
|
|
|
489
|
|
|
490 ##############################################################################################################################################################################################################################################
|
|
|
491
|
|
|
492 def logo_seq_red(merge, flag, group):
|
|
|
493
|
|
|
494 if flag=="c":
|
|
17
|
495 titlos = group + " group (redundant)"
|
|
6
|
496 file_logo = "c_logo.png"
|
|
|
497 file_bar = "c_bar.png"
|
|
|
498 if flag=="t":
|
|
17
|
499 titlos = group + " group (redundant)"
|
|
6
|
500 file_logo = "t_logo.png"
|
|
|
501 file_bar = "t_bar.png"
|
|
|
502
|
|
|
503 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge]
|
|
|
504
|
|
|
505 A=[0]*3
|
|
|
506 C=[0]*3
|
|
|
507 G=[0]*3
|
|
|
508 T=[0]*3
|
|
|
509 total_reads=0
|
|
|
510
|
|
|
511 for y in c_samples:
|
|
|
512 if "/" in y[0]:
|
|
|
513 length=[]
|
|
|
514 for x in y[0].split("/"):
|
|
|
515 length.append([len(x.split("_")[-1]),x.split("_")[-1],y[2]])
|
|
|
516
|
|
|
517 best=min(length)
|
|
|
518 total_reads+=best[2]
|
|
|
519 for i in range(3):
|
|
|
520 if i<len(best[1]):
|
|
|
521 if best[1][i] == "A":
|
|
|
522 A[i]+=best[2]
|
|
|
523 elif best[1][i] == "C":
|
|
|
524 C[i]+=best[2]
|
|
|
525 elif best[1][i] == "G":
|
|
|
526 G[i]+=best[2]
|
|
|
527 else:
|
|
|
528 T[i]+=best[2]
|
|
|
529 else:
|
|
|
530 total_reads+=y[2]
|
|
|
531 for i in range(3):
|
|
|
532 if i<len(y[0].split("_")[-1]):
|
|
|
533 if y[0].split("_")[-1][i] == "A":
|
|
|
534 A[i]+=(y[2])
|
|
|
535 elif y[0].split("_")[-1][i] == "C":
|
|
|
536 C[i]+=(y[2])
|
|
|
537 elif y[0].split("_")[-1][i] == "G":
|
|
|
538 G[i]+=(y[2])
|
|
|
539 else:
|
|
|
540 T[i]+=y[2]
|
|
|
541
|
|
|
542 A[:] = [round(x*100,1) / total_reads for x in A]
|
|
|
543 C[:] = [round(x*100,1) / total_reads for x in C]
|
|
|
544 G[:] = [round(x*100,1) / total_reads for x in G]
|
|
|
545 T[:] = [round(x*100,1) / total_reads for x in T]
|
|
|
546
|
|
|
547 data = {'A':A,'C':C,'G':G,'T':T}
|
|
|
548 df = pd.DataFrame(data, index=[1,2,3])
|
|
|
549
|
|
|
550 h=df.plot.bar(color=tuple(["g", "b","gold","r"]) )
|
|
|
551 h.grid(axis='y',linewidth=0.2)
|
|
|
552 plt.xticks(rotation=0, ha="right")
|
|
|
553 plt.ylabel("Counts (%)",fontsize=18)
|
|
|
554 plt.xlabel("Numbers of additional nucleotides",fontsize=18)
|
|
|
555 plt.title(titlos,fontsize=20)
|
|
|
556 plt.tight_layout()
|
|
|
557 plt.savefig(file_bar, dpi=300)
|
|
|
558
|
|
|
559 crp_logo = lm.Logo(df, font_name = 'DejaVu Sans')
|
|
|
560
|
|
|
561 crp_logo.style_spines(visible=False)
|
|
|
562 crp_logo.style_spines(spines=['left', 'bottom'], visible=True)
|
|
|
563 crp_logo.style_xticks(rotation=0, fmt='%d', anchor=0)
|
|
|
564 crp_logo.ax.set_title(titlos,fontsize=18)
|
|
|
565 crp_logo.ax.set_ylabel("Counts (%)", fontsize=16,labelpad=5)
|
|
|
566 crp_logo.ax.set_xlabel("Numbers of additional nucleotides",fontsize=16, labelpad=5)
|
|
|
567 crp_logo.ax.xaxis.set_ticks_position('none')
|
|
|
568 crp_logo.ax.xaxis.set_tick_params(pad=-1)
|
|
|
569 figure = plt.gcf()
|
|
|
570 figure.set_size_inches(6, 4)
|
|
|
571 crp_logo.fig.savefig(file_logo,dpi=300)
|
|
|
572
|
|
|
573 ##########################################################################################################################################################################################################
|
|
|
574
|
|
|
575 def pdf_before_DE(analysis,group_name1,group_name2):
|
|
|
576
|
|
|
577 # Image extensions
|
|
|
578 if analysis=="2":
|
|
|
579 image_extensions = ("c_hist_red.png","t_hist_red.png","pie_non.png","spider_red.png","spider_non_red.png","c_logo.png","t_logo.png","c_bar.png","t_bar.png")
|
|
|
580 else:
|
|
|
581 image_extensions = ("c_hist_red.png","t_hist_red.png","pie_tem.png","spider_red.png","spider_non_red.png")
|
|
|
582 # This list will hold the images file names
|
|
|
583 images = []
|
|
|
584
|
|
|
585 # Build the image list by merging the glob results (a list of files)
|
|
|
586 # for each extension. We are taking images from current folder.
|
|
|
587 for extension in image_extensions:
|
|
|
588 images.extend(glob.glob(extension))
|
|
|
589 #sys.exit(images)
|
|
|
590 # Create instance of FPDF class
|
|
|
591 pdf = FPDF('P', 'in', 'A4')
|
|
|
592 # Add new page. Without this you cannot create the document.
|
|
|
593 pdf.add_page()
|
|
|
594 # Set font to Arial, 'B'old, 16 pts
|
|
|
595 pdf.set_font('Arial', 'B', 20.0)
|
|
|
596
|
|
|
597 # Page header
|
|
17
|
598 pdf.cell(pdf.w-0.5, 0.5, 'IsomiR profile report',align='C')
|
|
6
|
599 pdf.ln(0.7)
|
|
|
600 pdf.set_font('Arial','', 16.0)
|
|
|
601 pdf.cell(pdf.w-0.5, 0.5, 'sRNA length distribution',align='C')
|
|
|
602
|
|
|
603 # Smaller font for image captions
|
|
|
604 pdf.set_font('Arial', '', 11.0)
|
|
|
605
|
|
|
606 # Image caption
|
|
|
607 pdf.ln(0.5)
|
|
|
608
|
|
|
609 yh=FPDF.get_y(pdf)
|
|
|
610 pdf.image(images[0],x=0.3,w=4, h=3)
|
|
|
611 pdf.image(images[1],x=4,y=yh, w=4, h=3)
|
|
|
612 pdf.ln(0.3)
|
|
|
613
|
|
|
614 # Image caption
|
|
|
615 pdf.cell(0.2)
|
|
17
|
616 pdf.cell(3.0, 0.0, " Mapped and unmapped reads to custom precursor arm reference DB (5p and 3p arms) in "+group_name1.lower())
|
|
6
|
617 pdf.ln(0.2)
|
|
|
618 pdf.cell(0.2)
|
|
17
|
619 pdf.cell(3.0, 0.0, " (left) and "+group_name2.lower()+" (right) groups")
|
|
6
|
620
|
|
|
621
|
|
|
622 pdf.ln(0.5)
|
|
|
623 h1=FPDF.get_y(pdf)
|
|
|
624 pdf.image(images[2],x=1, w=6.5, h=5)
|
|
|
625 h2=FPDF.get_y(pdf)
|
|
|
626 FPDF.set_y(pdf,h1+0.2)
|
|
|
627 pdf.set_font('Arial','', 16.0)
|
|
|
628 pdf.cell(pdf.w-0.5, 0.5, 'Template and non-template isomiRs',align='C')
|
|
|
629 pdf.set_font('Arial', '', 11.0)
|
|
|
630 FPDF.set_y(pdf,h2)
|
|
|
631 FPDF.set_y(pdf,9.5)
|
|
|
632 # Image caption
|
|
|
633 pdf.cell(0.2)
|
|
|
634 if analysis=="2":
|
|
|
635 pdf.cell(3.0, 0.0, " RefSeq miRNAs, templated isomiRs, non-templated isomiRs and unassigned sequences as percentage")
|
|
|
636 pdf.ln(0.2)
|
|
|
637 pdf.cell(0.2)
|
|
17
|
638 pdf.cell(3.0, 0.0, " of total sRNA reads in "+group_name1.lower()+" (left) and "+group_name2.lower()+" (right) groups")
|
|
6
|
639
|
|
|
640 else:
|
|
|
641 pdf.cell(3.0, 0.0, " RefSeq miRNAS, Templated isomiRs and unassigned sequences as percentage of total sRNA reads in")
|
|
|
642 pdf.ln(0.2)
|
|
|
643 pdf.cell(0.2)
|
|
17
|
644 pdf.cell(3.0, 0.0, " "+group_name1.lower()+" (left) and "+group_name2.lower()+" (right) groups")
|
|
6
|
645
|
|
|
646 pdf.add_page()
|
|
|
647 pdf.set_font('Arial', 'B', 18.0)
|
|
|
648 pdf.cell(pdf.w-0.5, 0.5, "Templated isomiR subtypes",align='C')
|
|
|
649 pdf.ln(0.7)
|
|
|
650 pdf.set_font('Arial', 'B', 14.0)
|
|
|
651 pdf.cell(pdf.w-0.5, 0.5, "Templated isomiR profile (redundant reads)",align='C')
|
|
|
652 pdf.ln(0.5)
|
|
|
653 pdf.image(images[3],x=1.5, w=5.5, h=4)
|
|
|
654 pdf.ln(0.6)
|
|
|
655 pdf.cell(pdf.w-0.5, 0.0, "Templated isomiR profile (non-redundant reads)",align='C')
|
|
|
656 pdf.set_font('Arial', '', 12.0)
|
|
|
657 pdf.ln(0.2)
|
|
|
658 pdf.image(images[4],x=1.5, w=5.5, h=4)
|
|
|
659 pdf.ln(0.3)
|
|
|
660 pdf.set_font('Arial', '', 11.0)
|
|
|
661 pdf.cell(0.2)
|
|
|
662 pdf.cell(3.0, 0.0, " * IsomiRs potentially generated from multiple loci")
|
|
|
663
|
|
|
664 if analysis=="2":
|
|
|
665 pdf.add_page('L')
|
|
|
666
|
|
|
667 pdf.set_font('Arial', 'B', 18.0)
|
|
|
668 pdf.cell(pdf.w-0.5, 0.5, "Non-templated isomiRs",align='C')
|
|
|
669 pdf.ln(0.5)
|
|
|
670 pdf.set_font('Arial', 'B', 14.0)
|
|
|
671 pdf.cell(pdf.w-0.5, 0.5, "3'-end additions to RefSeq miRNAs and templated isomiRs",align='C')
|
|
|
672 pdf.ln(0.7)
|
|
|
673
|
|
|
674 yh=FPDF.get_y(pdf)
|
|
|
675 pdf.image(images[5],x=1.5,w=3.65, h=2.65)
|
|
|
676 pdf.image(images[7],x=6.5,y=yh, w=3.65, h=2.65)
|
|
|
677 pdf.ln(0.5)
|
|
|
678 yh=FPDF.get_y(pdf)
|
|
|
679 pdf.image(images[6],x=1.5,w=3.65, h=2.65)
|
|
|
680 pdf.image(images[8],x=6.5,y=yh, w=3.65, h=2.65)
|
|
|
681
|
|
|
682 pdf.close()
|
|
|
683 pdf.output('report1.pdf','F')
|
|
|
684
|
|
|
685 #############################################################################################################################################################3
|
|
|
686
|
