Mercurial > repos > glogobyte > viztool

comparison mirbase_graphs.py @ 1:561b0abcae87 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
Uploaded
author glogobyte
date Fri, 16 Oct 2020 12:15:50 +0000
parents
children
comparison
equal deleted inserted replaced
0:258aaaa465f3 1:561b0abcae87
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