|
1
|
1 import itertools
|
|
|
2 import time
|
|
|
3 import sys
|
|
|
4 import os
|
|
|
5 import urllib.request
|
|
|
6 import gzip
|
|
|
7 from multiprocessing import Process, Queue, Lock, Pool, Manager, Value
|
|
|
8 import subprocess
|
|
|
9 import argparse
|
|
|
10 from collections import OrderedDict
|
|
|
11 from matplotlib.backends.backend_pdf import PdfPages
|
|
|
12 import pandas as pd
|
|
|
13 from math import pi
|
|
|
14 import numpy as np
|
|
|
15 import matplotlib.pyplot as plt
|
|
|
16 from matplotlib.ticker import PercentFormatter
|
|
|
17 import seaborn as sns
|
|
|
18 import scipy.stats as stats
|
|
|
19 from plotnine import *
|
|
|
20 import math
|
|
|
21 import re
|
|
|
22 import matplotlib.ticker as mtick
|
|
|
23 import copy
|
|
|
24
|
|
|
25
|
|
|
26 #################################################################################################################################################################
|
|
|
27 def pie_non_temp(merge_LH2E,merge_non_LH2E,merge_LH8E,merge_non_LH8E,c_unmap,t_unmap,c_unmap_counts,t_unmap_counts):
|
|
|
28
|
|
|
29 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_LH2E]
|
|
|
30 t_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_LH8E]
|
|
|
31 c_non_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_non_LH2E]
|
|
|
32 t_non_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_non_LH8E]
|
|
|
33
|
|
|
34 c_templ = 0
|
|
|
35 c_tem_counts = 0
|
|
|
36 c_mature = 0
|
|
|
37 c_mat_counts = 0
|
|
|
38 t_templ = 0
|
|
|
39 t_tem_counts = 0
|
|
|
40 t_mature = 0
|
|
|
41 t_mat_counts = 0
|
|
|
42
|
|
|
43 c_non = len(c_non_samples)
|
|
|
44 c_non_counts = sum(x[2] for x in c_non_samples)
|
|
|
45 t_non = len(t_non_samples)
|
|
|
46 t_non_counts = sum(x[2] for x in t_non_samples)
|
|
|
47
|
|
|
48 c_unmap = c_unmap - c_non
|
|
|
49 t_unmap = c_unmap - t_non
|
|
|
50
|
|
|
51 c_unmap_counts=c_unmap_counts - c_non_counts
|
|
|
52 t_unmap_counts=t_unmap_counts - t_non_counts
|
|
|
53
|
|
|
54
|
|
|
55 for x in c_samples:
|
|
|
56
|
|
|
57 if "/" not in x[0]:
|
|
|
58 if "chr" in x[0].split("_")[-1]:
|
|
|
59 c_mature+=1
|
|
|
60 c_mat_counts += x[2]
|
|
|
61 else:
|
|
|
62 c_templ+=1
|
|
|
63 c_tem_counts += x[2]
|
|
|
64 else:
|
|
|
65 f=0
|
|
|
66 for y in x[0].split("/"):
|
|
|
67 if "chr" in y.split("_")[-1]:
|
|
|
68 c_mature+=1
|
|
|
69 c_mat_counts += x[2]
|
|
|
70 f=1
|
|
|
71 break
|
|
|
72 if f==0:
|
|
|
73 c_templ+=1
|
|
|
74 c_tem_counts += x[2]
|
|
|
75
|
|
|
76 for x in t_samples:
|
|
|
77
|
|
|
78 if "/" not in x[0]:
|
|
|
79 if "chr" in x[0].split("_")[-1]:
|
|
|
80 t_mature+=1
|
|
|
81 t_mat_counts += x[2]
|
|
|
82 else:
|
|
|
83 t_templ+=1
|
|
|
84 t_tem_counts += x[2]
|
|
|
85 else:
|
|
|
86 f=0
|
|
|
87 for y in x[0].split("/"):
|
|
|
88 if "chr" in y.split("_")[-1]:
|
|
|
89 t_mature+=1
|
|
|
90 t_mat_counts += x[2]
|
|
|
91 f=1
|
|
|
92 break
|
|
|
93 if f==0:
|
|
|
94 t_templ+=1
|
|
|
95 t_tem_counts += x[2]
|
|
|
96
|
|
|
97 fig = plt.figure(figsize=(7,5))
|
|
|
98 labels = 'miRNA RefSeq','Template', 'Unmapped','Non-template'
|
|
|
99 sizes = [c_mat_counts, c_tem_counts, c_unmap_counts,c_non_counts]
|
|
|
100 colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
|
|
|
101 ax1 = plt.subplot2grid((1,2),(0,0))
|
|
|
102 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
|
|
|
103 [x.set_fontsize(8) for x in texts]
|
|
|
104 plt.title('Control Group (reads)',fontsize=12)
|
|
|
105 labels = 'miRNA RefSeq','Template', 'Unmapped','non-template'
|
|
|
106 sizes = [t_mat_counts, t_tem_counts, t_unmap_counts, t_non_counts]
|
|
|
107 colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
|
|
|
108 ax2 = plt.subplot2grid((1,2),(0,1))
|
|
|
109 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
|
|
|
110 [x.set_fontsize(8) for x in texts]
|
|
|
111 plt.title('Treated Group (reads)', fontsize=12)
|
|
|
112 plt.savefig('pie_non.png',dpi=300)
|
|
|
113
|
|
|
114 ######################################################################################################################################################
|
|
|
115
|
|
|
116
|
|
|
117 def pie_temp(merge_LH2E,c_unmap,c_unmap_counts,merge_LH8E,t_unmap,t_unmap_counts):
|
|
|
118
|
|
|
119 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_LH2E]
|
|
|
120 t_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_LH8E]
|
|
|
121
|
|
|
122 c_templ = 0
|
|
|
123 c_tem_counts = 0
|
|
|
124 c_mature = 0
|
|
|
125 c_mat_counts = 0
|
|
|
126 t_templ = 0
|
|
|
127 t_tem_counts = 0
|
|
|
128 t_mature = 0
|
|
|
129 t_mat_counts = 0
|
|
|
130
|
|
|
131 for x in c_samples:
|
|
|
132
|
|
|
133 if "/" not in x[0]:
|
|
|
134 if "chr" in x[0].split("_")[-1]:
|
|
|
135 c_mature+=1
|
|
|
136 c_mat_counts += x[2]
|
|
|
137 else:
|
|
|
138 c_templ+=1
|
|
|
139 c_tem_counts += x[2]
|
|
|
140 else:
|
|
|
141 f=0
|
|
|
142 for y in x[0].split("/"):
|
|
|
143 if "chr" in y.split("_")[-1]:
|
|
|
144 c_mature+=1
|
|
|
145 c_mat_counts += x[2]
|
|
|
146 f=1
|
|
|
147 break
|
|
|
148 if f==0:
|
|
|
149 c_templ+=1
|
|
|
150 c_tem_counts += x[2]
|
|
|
151
|
|
|
152 for x in t_samples:
|
|
|
153
|
|
|
154 if "/" not in x[0]:
|
|
|
155 if "chr" in x[0].split("_")[-1]:
|
|
|
156 t_mature+=1
|
|
|
157 t_mat_counts += x[2]
|
|
|
158 else:
|
|
|
159 t_templ+=1
|
|
|
160 t_tem_counts += x[2]
|
|
|
161 else:
|
|
|
162 f=0
|
|
|
163 for y in x[0].split("/"):
|
|
|
164 if "chr" in y.split("_")[-1]:
|
|
|
165 t_mature+=1
|
|
|
166 t_mat_counts += x[2]
|
|
|
167 f=1
|
|
|
168 break
|
|
|
169 if f==0:
|
|
|
170 t_templ+=1
|
|
|
171 t_tem_counts += x[2]
|
|
|
172
|
|
|
173
|
|
|
174 fig = plt.figure()
|
|
|
175 labels = 'miRNA RefSeq','Template', 'Unmapped'
|
|
|
176 sizes = [c_mat_counts, c_tem_counts, c_unmap_counts]
|
|
|
177 colors = ['gold', 'yellowgreen', 'lightskyblue']
|
|
|
178 explode = (0.2, 0.05, 0.1)
|
|
|
179 ax1 = plt.subplot2grid((1,2),(0,0))
|
|
|
180 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
|
|
|
181 [x.set_fontsize(8) for x in texts]
|
|
|
182 plt.title('Control group (reads)', fontsize=12)
|
|
|
183 labels = 'miRNA RefSeq','Template', 'Unmapped'
|
|
|
184 sizes = [t_mat_counts, t_tem_counts, t_unmap_counts]
|
|
|
185 colors = ['gold', 'yellowgreen', 'lightskyblue']
|
|
|
186 explode = (0.2, 0.05, 0.1)
|
|
|
187 ax2 = plt.subplot2grid((1,2),(0,1))
|
|
|
188 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
|
|
|
189 [x.set_fontsize(8) for x in texts]
|
|
|
190 plt.title('Treated group (reads)',fontsize = 12)
|
|
|
191 plt.savefig('pie_tem.png',dpi=300)
|
|
|
192
|
|
|
193 ###################################################################################################################################################################################################################
|
|
|
194
|
|
|
195
|
|
|
196 def make_spider(merge_LH2E,merge_LH8E):
|
|
|
197
|
|
|
198 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_LH2E]
|
|
|
199 t_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_LH8E]
|
|
|
200
|
|
|
201 c_5 = 0
|
|
|
202 c_5_counts = 0
|
|
|
203 c_3 = 0
|
|
|
204 c_3_counts = 0
|
|
|
205 c_both =0
|
|
|
206 c_both_counts=0
|
|
|
207 c_mature = 0
|
|
|
208 c_mat_counts = 0
|
|
|
209 c_exception=0
|
|
|
210 c_exception_counts=0
|
|
|
211
|
|
|
212
|
|
|
213 t_5 = 0
|
|
|
214 t_5_counts = 0
|
|
|
215 t_3 = 0
|
|
|
216 t_3_counts = 0
|
|
|
217 t_both = 0
|
|
|
218 t_both_counts = 0
|
|
|
219 t_mature = 0
|
|
|
220 t_mat_counts = 0
|
|
|
221 t_exception = 0
|
|
|
222 t_exception_counts=0
|
|
|
223
|
|
|
224 for x in c_samples:
|
|
|
225
|
|
|
226 if "/" not in x[0]:
|
|
|
227 if "chr" in x[0].split("_")[-1]:
|
|
|
228 c_mature+=1
|
|
|
229 c_mat_counts += x[2]
|
|
|
230 elif 0 == int(x[0].split("_")[-1]):
|
|
|
231 c_5+=1
|
|
|
232 c_5_counts += x[2]
|
|
|
233 elif 0 == int(x[0].split("_")[-2]):
|
|
|
234 c_3+=1
|
|
|
235 c_3_counts += x[2]
|
|
|
236 else:
|
|
|
237 c_both+=1
|
|
|
238 c_both_counts+=x[2]
|
|
|
239
|
|
|
240 else:
|
|
|
241 f=0
|
|
|
242 for y in x[0].split("/"):
|
|
|
243 if "chr" in y.split("_")[-1]:
|
|
|
244 c_mature+=1
|
|
|
245 c_mat_counts += x[2]
|
|
|
246 f=1
|
|
|
247 break
|
|
|
248 if f==0:
|
|
|
249 for y in x[0].split("/"):
|
|
|
250 c_exception+=1
|
|
|
251 c_exception_counts += x[2]
|
|
|
252
|
|
|
253
|
|
|
254 for x in t_samples:
|
|
|
255
|
|
|
256 if "/" not in x[0]:
|
|
|
257 if "chr" in x[0].split("_")[-1]:
|
|
|
258 t_mature+=1
|
|
|
259 t_mat_counts += x[2]
|
|
|
260 elif 0 == int(x[0].split("_")[-1]):
|
|
|
261 t_5+=1
|
|
|
262 t_5_counts += x[2]
|
|
|
263 elif 0 == int(x[0].split("_")[-2]):
|
|
|
264 t_3+=1
|
|
|
265 t_3_counts += x[2]
|
|
|
266 else:
|
|
|
267 t_both+=1
|
|
|
268 t_both_counts+=x[2]
|
|
|
269
|
|
|
270 else:
|
|
|
271 f=0
|
|
|
272 for y in x[0].split("/"):
|
|
|
273 if "chr" in y.split("_")[-1]:
|
|
|
274 t_mature+=1
|
|
|
275 t_mat_counts += x[2]
|
|
|
276 f=1
|
|
|
277 break
|
|
|
278 if f==0:
|
|
|
279 for y in x[0].split("/"):
|
|
|
280 t_exception+=1
|
|
|
281 t_exception_counts += x[2]
|
|
|
282
|
|
|
283
|
|
|
284 c_all = c_5+c_3+c_both+c_mature+c_exception
|
|
|
285 c_all_counts = c_5_counts + c_3_counts + c_both_counts + c_mat_counts + c_exception_counts
|
|
|
286
|
|
|
287 t_all = t_5+t_3+t_both+t_mature + t_exception
|
|
|
288 t_all_counts = t_5_counts + t_3_counts + t_both_counts + t_mat_counts + t_exception_counts
|
|
|
289
|
|
|
290 c_5 = round(c_5/c_all*100,2)
|
|
|
291 c_3 = round(c_3/c_all*100,2)
|
|
|
292 c_both = round(c_both/c_all*100,2)
|
|
|
293 c_mature = round(c_mature/c_all*100,2)
|
|
|
294 c_exception = round(c_exception/c_all*100,2)
|
|
|
295
|
|
|
296 c_5_counts = round(c_5_counts/c_all_counts*100,2)
|
|
|
297 c_3_counts = round(c_3_counts/c_all_counts*100,2)
|
|
|
298 c_both_counts = round(c_both_counts/c_all_counts*100,2)
|
|
|
299 c_mat_counts = round(c_mat_counts/c_all_counts*100,2)
|
|
|
300 c_exception_counts = round(c_exception_counts/c_all_counts*100,2)
|
|
|
301
|
|
|
302 t_5 = round(t_5/t_all*100,2)
|
|
|
303 t_3 = round(t_3/t_all*100,2)
|
|
|
304 t_both = round(t_both/t_all*100,2)
|
|
|
305 t_mature = round(t_mature/t_all*100,2)
|
|
|
306 t_exception = round(t_exception/t_all*100,2)
|
|
|
307
|
|
|
308 t_5_counts = round(t_5_counts/t_all_counts*100,2)
|
|
|
309 t_3_counts = round(t_3_counts/t_all_counts*100,2)
|
|
|
310 t_both_counts = round(t_both_counts/t_all_counts*100,2)
|
|
|
311 t_mat_counts = round(t_mat_counts/t_all_counts*100,2)
|
|
|
312 t_exception_counts = round(t_exception_counts/t_all_counts*100,2)
|
|
|
313
|
|
|
314 radar_max = max(c_5, c_3, c_both,c_mature,c_exception,t_5,t_3,t_both,t_mature,t_exception)
|
|
|
315 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)
|
|
|
316
|
|
|
317 df=pd.DataFrame({
|
|
|
318 'group':['Controls','Treated'],
|
|
|
319 """5' and 3' isomiRs""":[c_both,t_both],
|
|
|
320 """3' isomiRs""":[c_3,t_3],
|
|
|
321 'miRNA RefSeq':[c_mature,t_mature],
|
|
|
322 """5' isomiRs""":[c_5,t_5],
|
|
|
323 'Others*':[c_exception,t_exception]})
|
|
|
324
|
|
|
325 df1=pd.DataFrame({
|
|
|
326 'group':['Controls','Treated'],
|
|
|
327 """5' and 3' isomiRs""":[c_both_counts,t_both_counts],
|
|
|
328 """3' isomiRs""":[c_3_counts,t_3_counts],
|
|
|
329 'miRNA RefSeq':[c_mat_counts,t_mat_counts],
|
|
|
330 """5' isomiRs""":[c_5_counts,t_5_counts],
|
|
|
331 'Others*':[c_exception_counts,t_exception_counts]})
|
|
|
332
|
|
|
333 spider_last(df,radar_max,1)
|
|
|
334 spider_last(df1,radar_max_counts,2)
|
|
|
335
|
|
|
336 #####################################################################################################################################################
|
|
|
337
|
|
|
338 def spider_last(df,radar_max,flag):
|
|
|
339 # ------- PART 1: Create background
|
|
|
340 fig = plt.figure()
|
|
|
341 # number of variable
|
|
|
342 categories=list(df)[1:]
|
|
|
343 N = len(categories)
|
|
|
344
|
|
|
345 # What will be the angle of each axis in the plot? (we divide the plot / number of variable)
|
|
|
346 angles = [n / float(N) * 2 * pi for n in range(N)]
|
|
|
347 angles += angles[:1]
|
|
|
348
|
|
|
349 # Initialise the spider plot
|
|
|
350 ax = plt.subplot(111, polar=True)
|
|
|
351
|
|
|
352 # If you want the first axis to be on top:
|
|
|
353 ax.set_theta_offset(pi/2)
|
|
|
354 ax.set_theta_direction(-1)
|
|
|
355
|
|
|
356 # Draw one axe per variable + add labels labels yet
|
|
|
357 plt.xticks(angles[:-1], categories, fontsize=11)
|
|
|
358
|
|
|
359 # Draw ylabels
|
|
|
360 radar_max=round(radar_max+radar_max*0.1)
|
|
|
361 mul=len(str(radar_max))-1
|
|
|
362 maxi=int(math.ceil(radar_max / pow(10,mul))) * pow(10,mul)
|
|
|
363 sep = round(maxi/4)
|
|
|
364 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=10)
|
|
|
365 plt.ylim(0, maxi)
|
|
|
366
|
|
|
367 # ------- PART 2: Add plots
|
|
|
368
|
|
|
369 # Plot each individual = each line of the data
|
|
|
370 # I don't do a loop, because plotting more than 3 groups makes the chart unreadable
|
|
|
371
|
|
|
372 # Ind1
|
|
|
373 values=df.loc[0].drop('group').values.flatten().tolist()
|
|
|
374 values += values[:1]
|
|
|
375 ax.plot(angles, values,'-o', linewidth=1, linestyle='solid', label="Controls")
|
|
|
376 ax.fill(angles, values, 'b', alpha=0.1)
|
|
|
377
|
|
|
378 # Ind2
|
|
|
379 values=df.loc[1].drop('group').values.flatten().tolist()
|
|
|
380 values += values[:1]
|
|
|
381 ax.plot(angles, values, '-o' ,linewidth=1, linestyle='solid', label="Treated")
|
|
|
382 ax.fill(angles, values, 'r', alpha=0.1)
|
|
|
383
|
|
|
384 # Add legend
|
|
|
385 if flag==1:
|
|
|
386 plt.legend(loc='upper right', bbox_to_anchor=(0.0, 0.1))
|
|
|
387 plt.savefig('spider_non_red.png',dpi=300)
|
|
|
388 else:
|
|
|
389 plt.legend(loc='upper right', bbox_to_anchor=(0.0, 0.1))
|
|
|
390 plt.savefig('spider_red.png',dpi=300)
|
|
|
391
|
|
|
392
|
|
|
393 #############################################################################################################################################################################################################
|
|
|
394
|
|
|
395 def hist_red(samples,flag):
|
|
|
396 lengths=[]
|
|
|
397 cat=[]
|
|
|
398 total_reads=0
|
|
|
399 seq=[]
|
|
|
400
|
|
|
401 if flag == "c":
|
|
|
402 title = "Length Distribution of Control group (Redudant reads)"
|
|
|
403 if flag == "t":
|
|
|
404 title = "Length Distribution of Treated group (Redudant reads)"
|
|
|
405
|
|
|
406 for i in samples:
|
|
|
407 for x in i:
|
|
|
408 lengths.append(len(x[9]))
|
|
|
409 if x[1]=="0":
|
|
|
410 seq.append([x[9],x[0].split("-")[1],"Mapped"])
|
|
|
411 cat.append("Mapped")
|
|
|
412 if x[1] == "4":
|
|
|
413 seq.append([x[9],x[0].split("-")[1],"Unmapped"])
|
|
|
414 cat.append("Unmapped")
|
|
|
415
|
|
|
416 uni_len=list(set(lengths))
|
|
|
417 uni_len=[x for x in uni_len if x<=35]
|
|
|
418 low=min(lengths)
|
|
|
419 up=max(lengths)
|
|
|
420 seq.sort()
|
|
|
421 uni_seq=list(seq for seq,_ in itertools.groupby(seq))
|
|
|
422 dim=up-low
|
|
|
423
|
|
|
424 if dim>20:
|
|
|
425 s=5
|
|
|
426 else:
|
|
|
427 s=8
|
|
|
428
|
|
|
429 total_reads+=sum([int(x[1]) for x in uni_seq])
|
|
|
430
|
|
|
431 map_reads=[]
|
|
|
432 unmap_reads=[]
|
|
|
433 length=[]
|
|
|
434 for y in uni_len:
|
|
|
435 map_temp=0
|
|
|
436 unmap_temp=0
|
|
|
437 for x in uni_seq:
|
|
|
438 if len(x[0])==y and x[2]=="Mapped":
|
|
|
439 map_temp+=int(x[1])
|
|
|
440 if len(x[0])==y and x[2]=="Unmapped":
|
|
|
441 unmap_temp+=int(x[1])
|
|
|
442 if y<=35:
|
|
|
443 length.append(y)
|
|
|
444 map_reads.append(round(map_temp/total_reads*100,2))
|
|
|
445 unmap_reads.append(round(unmap_temp/total_reads*100,2))
|
|
|
446
|
|
|
447 ylim=max([sum(x) for x in zip(unmap_reads, map_reads)])
|
|
|
448 ylim=ylim+ylim*20/100
|
|
|
449 fig, ax = plt.subplots()
|
|
|
450 width=0.8
|
|
|
451 ax.bar(length, unmap_reads, width, label='Unmapped')
|
|
|
452 h=ax.bar(length, map_reads, width, bottom=unmap_reads, label='Mapped')
|
|
|
453 plt.xticks(np.arange(length[0], length[-1]+1, 1))
|
|
|
454 plt.xlabel('Length (nt)',fontsize=14)
|
|
|
455 plt.ylabel('Percentage',fontsize=14)
|
|
|
456 plt.title(title,fontsize=14)
|
|
|
457 ax.legend()
|
|
|
458 plt.ylim([0, ylim])
|
|
|
459 ax.grid(axis='y',linewidth=0.2)
|
|
|
460
|
|
|
461 if flag=='c':
|
|
|
462 plt.savefig('c_hist_red.png',dpi=300)
|
|
|
463
|
|
|
464 if flag=='t':
|
|
|
465 plt.savefig('t_hist_red.png',dpi=300)
|
|
|
466
|
|
|
467 #################################################################################################################
|
|
|
468
|
|
|
469 def logo_seq_red(merge, flag):
|
|
|
470
|
|
|
471 if flag=="c":
|
|
|
472 titlos="Control group (Redundant)"
|
|
|
473 file_logo="c_logo.png"
|
|
|
474 file_bar="c_bar.png"
|
|
|
475 if flag=="t":
|
|
|
476 titlos="Treated group (Redundant)"
|
|
|
477 file_logo="t_logo.png"
|
|
|
478 file_bar="t_bar.png"
|
|
|
479
|
|
|
480 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge]
|
|
|
481
|
|
|
482 A=[0]*5
|
|
|
483 C=[0]*5
|
|
|
484 G=[0]*5
|
|
|
485 T=[0]*5
|
|
|
486 total_reads=0
|
|
|
487
|
|
|
488 for y in c_samples:
|
|
|
489 if "/" in y[0]:
|
|
|
490 length=[]
|
|
|
491 for x in y[0].split("/"):
|
|
|
492 length.append([len(x.split("_")[-1]),x.split("_")[-1],y[2]])
|
|
|
493
|
|
|
494 best=min(length)
|
|
|
495 total_reads+=best[2]
|
|
|
496 for i in range(5):
|
|
|
497 if i<len(best[1]):
|
|
|
498 if best[1][i] == "A":
|
|
|
499 A[i]+=best[2]
|
|
|
500 elif best[1][i] == "C":
|
|
|
501 C[i]+=best[2]
|
|
|
502 elif best[1][i] == "G":
|
|
|
503 G[i]+=best[2]
|
|
|
504 else:
|
|
|
505 T[i]+=best[2]
|
|
|
506 else:
|
|
|
507 total_reads+=y[2]
|
|
|
508 for i in range(5):
|
|
|
509 if i<len(y[0].split("_")[-1]):
|
|
|
510 if y[0].split("_")[-1][i] == "A":
|
|
|
511 A[i]+=(y[2])
|
|
|
512 elif y[0].split("_")[-1][i] == "C":
|
|
|
513 C[i]+=(y[2])
|
|
|
514 elif y[0].split("_")[-1][i] == "G":
|
|
|
515 G[i]+=(y[2])
|
|
|
516 else:
|
|
|
517 T[i]+=y[2]
|
|
|
518
|
|
|
519 A[:] = [round(x*100,1) / total_reads for x in A]
|
|
|
520 C[:] = [round(x*100,1) / total_reads for x in C]
|
|
|
521 G[:] = [round(x*100,1) / total_reads for x in G]
|
|
|
522 T[:] = [round(x*100,1) / total_reads for x in T]
|
|
|
523
|
|
|
524
|
|
|
525
|
|
|
526 data = {'A':A,'C':C,'G':G,'T':T}
|
|
|
527 df = pd.DataFrame(data, index=[1,2,3,4,5])
|
|
|
528 h=df.plot.bar(color=tuple(["g", "b","gold","r"]) )
|
|
|
529 h.grid(axis='y',linewidth=0.2)
|
|
|
530 plt.xticks(rotation=0, ha="right")
|
|
|
531 plt.ylabel("Counts (%)",fontsize=18)
|
|
|
532 plt.xlabel("Positions (nt)",fontsize=18)
|
|
|
533 plt.title(titlos,fontsize=20)
|
|
|
534 plt.tight_layout()
|
|
|
535 plt.savefig(file_bar, dpi=300)
|
|
|
536
|
|
|
537 import logomaker as lm
|
|
|
538 crp_logo = lm.Logo(df, font_name = 'DejaVu Sans')
|
|
|
539 crp_logo.style_spines(visible=False)
|
|
|
540 crp_logo.style_spines(spines=['left', 'bottom'], visible=True)
|
|
|
541 crp_logo.style_xticks(rotation=0, fmt='%d', anchor=0)
|
|
|
542
|
|
|
543 # style using Axes methods
|
|
|
544 crp_logo.ax.set_title(titlos,fontsize=18)
|
|
|
545 crp_logo.ax.set_ylabel("Counts (%)", fontsize=16,labelpad=5)
|
|
|
546 crp_logo.ax.set_xlabel("Positions (nt)",fontsize=16, labelpad=5)
|
|
|
547 crp_logo.ax.xaxis.set_ticks_position('none')
|
|
|
548 crp_logo.ax.xaxis.set_tick_params(pad=-1)
|
|
|
549 figure = plt.gcf()
|
|
|
550 figure.set_size_inches(6, 4)
|
|
|
551 crp_logo.fig.savefig(file_logo,dpi=300)
|
|
|
552
|
|
|
553 ##########################################################################################################################################################################################################
|
|
|
554
|
|
|
555 def logo_seq_non_red(merge_LH2E):
|
|
|
556
|
|
|
557 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_LH2E]
|
|
|
558
|
|
|
559 A=[0]*5
|
|
|
560 C=[0]*5
|
|
|
561 G=[0]*5
|
|
|
562 T=[0]*5
|
|
|
563
|
|
|
564 for y in c_samples:
|
|
|
565 if "/" in y[0]:
|
|
|
566 length=[]
|
|
|
567 for x in y[0].split("/"):
|
|
|
568 length.append([len(x.split("_")[-1]),x.split("_")[-1],y[2]])
|
|
|
569
|
|
|
570 best=min(length)
|
|
|
571 for i in range(5):
|
|
|
572 if i<len(best[1]):
|
|
|
573 if best[1][i] == "A":
|
|
|
574 A[i]+=1
|
|
|
575 elif best[1][i] == "C":
|
|
|
576 C[i]+=1
|
|
|
577 elif best[1][i] == "G":
|
|
|
578 G[i]+=1
|
|
|
579 else:
|
|
|
580 T[i]+=1
|
|
|
581 else:
|
|
|
582 for i in range(5):
|
|
|
583 if i<len(y[0].split("_")[-1]):
|
|
|
584 if y[0].split("_")[-1][i] == "A":
|
|
|
585 A[i]+=1
|
|
|
586 elif y[0].split("_")[-1][i] == "C":
|
|
|
587 C[i]+=1
|
|
|
588 elif y[0].split("_")[-1][i] == "G":
|
|
|
589 G[i]+=1
|
|
|
590 else:
|
|
|
591 T[i]+=1
|
|
|
592
|
|
|
593 data = {'A':A,'C':C,'G':G,'T':T}
|
|
|
594 df = pd.DataFrame(data, index=[1,2,3,4,5])
|
|
|
595 h=df.plot.bar(title="Non-templated nucleotides after templated sequence",color=tuple(["g", "b","gold","r"]))
|
|
|
596 h.set_xlabel("Positions (nt)")
|
|
|
597 h.set_ylabel("Unique sequences")
|
|
|
598 plt.xticks(rotation=0, ha="right")
|
|
|
599 plt.tight_layout()
|
|
|
600 plt.savefig("bar2.png", dpi=300)
|
|
|
601
|
|
|
602
|
|
|
603 import logomaker as lm
|
|
|
604 crp_logo = lm.Logo(df, font_name = 'DejaVu Sans')
|
|
|
605
|
|
|
606 # style using Logo methods
|
|
|
607 crp_logo.style_spines(visible=False)
|
|
|
608 crp_logo.style_spines(spines=['left', 'bottom'], visible=True)
|
|
|
609 crp_logo.style_xticks(rotation=0, fmt='%d', anchor=0)
|
|
|
610
|
|
|
611 # style using Axes methods
|
|
|
612 crp_logo.ax.set_ylabel("Unique sequences", labelpad=5)
|
|
|
613 crp_logo.ax.set_xlabel("Positions (nt)", labelpad=5)
|
|
|
614 crp_logo.ax.xaxis.set_ticks_position('none')
|
|
|
615 crp_logo.ax.xaxis.set_tick_params(pad=-1)
|
|
|
616 crp_logo.ax.set_title("Non-redundant")
|
|
|
617 figure = plt.gcf()
|
|
|
618 crp_logo.fig.savefig('logo2.png', dpi=300)
|
|
|
619
|
|
|
620
|
|
|
621 ################################################################################################################################################################
|
|
|
622
|
|
|
623 def pdf_before_DE(analysis):
|
|
|
624
|
|
|
625 # Import FPDF class
|
|
|
626 from fpdf import FPDF, fpdf
|
|
|
627
|
|
|
628 # Import glob module to find all the files matching a pattern
|
|
|
629 import glob
|
|
|
630
|
|
|
631 # Image extensions
|
|
|
632 if analysis=="2":
|
|
|
633 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")
|
|
|
634 else:
|
|
|
635 image_extensions = ("c_hist_red.png","t_hist_red.png","pie_tem.png","spider_red.png","spider_non_red.png")
|
|
|
636 # This list will hold the images file names
|
|
|
637 images = []
|
|
|
638
|
|
|
639 # Build the image list by merging the glob results (a list of files)
|
|
|
640 # for each extension. We are taking images from current folder.
|
|
|
641 for extension in image_extensions:
|
|
|
642 images.extend(glob.glob(extension))
|
|
|
643 #sys.exit(images)
|
|
|
644 # Create instance of FPDF class
|
|
|
645 pdf = FPDF('P', 'in', 'A4')
|
|
|
646 # Add new page. Without this you cannot create the document.
|
|
|
647 pdf.add_page()
|
|
|
648 # Set font to Arial, 'B'old, 16 pts
|
|
|
649 pdf.set_font('Arial', 'B', 20.0)
|
|
|
650
|
|
|
651 # Page header
|
|
|
652 pdf.cell(pdf.w-0.5, 0.5, 'IsomiR Profile Report',align='C')
|
|
|
653 pdf.ln(0.7)
|
|
|
654 pdf.set_font('Arial','', 16.0)
|
|
|
655 pdf.cell(pdf.w-0.5, 0.5, 'sRNA Length Distribution',align='C')
|
|
|
656
|
|
|
657 # Smaller font for image captions
|
|
|
658 pdf.set_font('Arial', '', 11.0)
|
|
|
659
|
|
|
660 # Image caption
|
|
|
661 pdf.ln(0.5)
|
|
|
662
|
|
|
663 yh=FPDF.get_y(pdf)
|
|
|
664 pdf.image(images[0],x=0.3,w=4, h=3)
|
|
|
665 pdf.image(images[1],x=4,y=yh, w=4, h=3)
|
|
|
666 pdf.ln(0.3)
|
|
|
667
|
|
|
668 # Image caption
|
|
|
669 pdf.cell(0.2)
|
|
|
670 pdf.cell(3.0, 0.0, " Mapped and unmapped reads to custom precussor arm reference DB (5p and 3p arms) in Control (left)")
|
|
|
671 pdf.ln(0.2)
|
|
|
672 pdf.cell(0.2)
|
|
|
673 pdf.cell(3.0, 0.0, " and Treated (right) groups")
|
|
|
674
|
|
|
675
|
|
|
676 pdf.ln(0.5)
|
|
|
677 h1=FPDF.get_y(pdf)
|
|
|
678 pdf.image(images[2],x=1, w=6.5, h=5)
|
|
|
679 h2=FPDF.get_y(pdf)
|
|
|
680 FPDF.set_y(pdf,h1+0.2)
|
|
|
681 pdf.set_font('Arial','', 14.0)
|
|
|
682 pdf.cell(pdf.w-0.5, 0.5, 'Template and non-template IsomiRs',align='C')
|
|
|
683 pdf.set_font('Arial', '', 11.0)
|
|
|
684 FPDF.set_y(pdf,h2)
|
|
|
685 FPDF.set_y(pdf,9.5)
|
|
|
686 # Image caption
|
|
|
687 pdf.cell(0.2)
|
|
|
688 if analysis=="2":
|
|
|
689 pdf.cell(3.0, 0.0, " Template, non-template, miRNA RefSeq and unmapped sequences as percentage of total sRNA reads")
|
|
|
690 pdf.ln(0.2)
|
|
|
691 pdf.cell(0.2)
|
|
|
692 pdf.cell(3.0, 0.0, " in Control (left) and treated (right) groups")
|
|
|
693 else:
|
|
|
694 pdf.cell(3.0, 0.0, " Template, miRNA RefSeq and unmapped sequences as percentage of total sRNA reads in")
|
|
|
695 pdf.ln(0.2)
|
|
|
696 pdf.cell(0.2)
|
|
|
697 pdf.cell(3.0, 0.0, " Control (left) and treated (right) groups")
|
|
|
698
|
|
|
699
|
|
|
700
|
|
|
701 pdf.add_page()
|
|
|
702 pdf.set_font('Arial', 'B', 16.0)
|
|
|
703 pdf.cell(pdf.w-0.5, 0.5, "Reference form and isomiR among total miRNA reads",align='C')
|
|
|
704 pdf.ln(0.7)
|
|
|
705 pdf.set_font('Arial', 'B', 12.0)
|
|
|
706 pdf.cell(pdf.w-0.5, 0.5, "Template isomiR profile (redundant)",align='C')
|
|
|
707 pdf.ln(0.5)
|
|
|
708 pdf.image(images[3],x=1.5, w=5.5, h=4)
|
|
|
709 pdf.ln(0.6)
|
|
|
710 pdf.cell(pdf.w-0.5, 0.0, "Template isomiR profile (non-redundant)",align='C')
|
|
|
711 pdf.set_font('Arial', '', 12.0)
|
|
|
712 pdf.ln(0.2)
|
|
|
713 pdf.image(images[4],x=1.5, w=5.5, h=4)
|
|
|
714 pdf.ln(0.3)
|
|
|
715 pdf.set_font('Arial', '', 11.0)
|
|
|
716 pdf.cell(0.2)
|
|
|
717 pdf.cell(3.0, 0.0, " * IsomiRs potentialy initiated from multiple loci")
|
|
|
718
|
|
|
719
|
|
|
720 if analysis=="2":
|
|
|
721 pdf.add_page('L')
|
|
|
722
|
|
|
723 pdf.set_font('Arial', 'B', 16.0)
|
|
|
724 pdf.cell(pdf.w-0.5, 0.5, "Non-template IsomiRs",align='C')
|
|
|
725 pdf.ln(0.5)
|
|
|
726 pdf.set_font('Arial', 'B', 12.0)
|
|
|
727 pdf.cell(pdf.w-0.5, 0.5, "3' Additions of reference of isomiR sequence",align='C')
|
|
|
728 pdf.ln(0.7)
|
|
|
729
|
|
|
730 yh=FPDF.get_y(pdf)
|
|
|
731 pdf.image(images[5],x=1.5,w=3.65, h=2.65)
|
|
|
732 pdf.image(images[7],x=6.5,y=yh, w=3.65, h=2.65)
|
|
|
733 pdf.ln(0.5)
|
|
|
734 yh=FPDF.get_y(pdf)
|
|
|
735 pdf.image(images[6],x=1.5,w=3.65, h=2.65)
|
|
|
736 pdf.image(images[8],x=6.5,y=yh, w=3.65, h=2.65)
|
|
|
737
|
|
|
738 pdf.close()
|
|
|
739 pdf.output('report1.pdf','F')
|
|
|
740
|
|
|
741
|
|
|
742
|
|
|
743
|
|
|
744 #############################################################################################################################################################3
|
|
|
745
|
|
|
746 def pdf_after_DE(analysis):
|
|
|
747
|
|
|
748 # Import FPDF class
|
|
|
749 from fpdf import FPDF
|
|
|
750
|
|
|
751 # Import glob module to find all the files matching a pattern
|
|
|
752 import glob
|
|
|
753
|
|
|
754 # Image extensions
|
|
|
755 image_extensions = ("tem.png","non.png","a2.png")
|
|
|
756
|
|
|
757 # This list will hold the images file names
|
|
|
758 images = []
|
|
|
759
|
|
|
760 # Build the image list by merging the glob results (a list of files)
|
|
|
761 # for each extension. We are taking images from current folder.
|
|
|
762 for extension in image_extensions:
|
|
|
763 images.extend(glob.glob(extension))
|
|
|
764
|
|
|
765 # Create instance of FPDF class
|
|
|
766 pdf = FPDF('P', 'in', 'letter')
|
|
|
767 # Add new page. Without this you cannot create the document.
|
|
|
768 pdf.add_page()
|
|
|
769 # Set font to Arial, 'B'old, 16 pts
|
|
|
770 pdf.set_font('Arial', 'B', 16.0)
|
|
|
771
|
|
|
772 # Page header
|
|
|
773 pdf.cell(pdf.w-0.5, 0.5, 'Differential expression of miRNAs and Isoforms',align='C')
|
|
|
774 #pdf.ln(0.25)
|
|
|
775
|
|
|
776 pdf.ln(0.7)
|
|
|
777 pdf.set_font('Arial','B', 12.0)
|
|
|
778 pdf.cell(pdf.w-0.5, 0.5, 'Top 50 most differentially expressed miRNA and template isoforms',align='C')
|
|
|
779
|
|
|
780
|
|
|
781 # Smaller font for image captions
|
|
|
782 pdf.set_font('Arial', '', 10.0)
|
|
|
783
|
|
|
784 # Image caption
|
|
|
785 pdf.ln(0.4)
|
|
|
786 pdf.image(images[0],x=0.8, w=7, h=8)
|
|
|
787 pdf.ln(0.3)
|
|
|
788
|
|
|
789 if analysis=="2":
|
|
|
790
|
|
|
791 pdf.add_page()
|
|
|
792 pdf.ln(0.7)
|
|
|
793 pdf.set_font('Arial','B', 12.0)
|
|
|
794 pdf.cell(pdf.w-0.5, 0.5, 'Top 50 most differentially expressed non-template isomiRs',align='C')
|
|
|
795 pdf.ln(0.4)
|
|
|
796 pdf.image(images[1],x=0.5, w=7.5, h=6.5)
|
|
|
797
|
|
|
798 pdf.add_page()
|
|
|
799 pdf.ln(0.5)
|
|
|
800 pdf.cell(pdf.w-0.5, 0.5, 'Top 50 most differentially expressed miRNAs and isomiRs grouped by arm',align='C')
|
|
|
801 pdf.ln(0.4)
|
|
|
802 pdf.image(images[2],x=0.8, w=7, h=8)
|
|
|
803 pdf.ln(0.3)
|
|
|
804
|
|
|
805
|
|
|
806 pdf.output('report2.pdf', 'F')
|
|
|
807
|
|
|
808
|
|
|
809
|
