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comparison mirbase_graphs.py @ 3:d77b33e65501 draft

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author glogobyte
date Wed, 13 Oct 2021 16:04:28 +0000
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children fa48ad87ae3e
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2:47232a73a46b 3:d77b33e65501
1 import itertools
2 import pandas as pd
3 from math import pi
4 import numpy as np
5 import matplotlib.pyplot as plt
6 import math
7 import logomaker as lm
8 from fpdf import FPDF, fpdf
9 import glob
10
11 #################################################################################################################################################################
12
13 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):
14
15 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_con]
16 t_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_tre]
17 c_non_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_non_con]
18 t_non_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_non_tre]
19
20 c_templ = 0
21 c_tem_counts = 0
22 c_mature = 0
23 c_mat_counts = 0
24 t_templ = 0
25 t_tem_counts = 0
26 t_mature = 0
27 t_mat_counts = 0
28
29 c_non = len(c_non_samples)
30 c_non_counts = sum(x[2] for x in c_non_samples)
31 t_non = len(t_non_samples)
32 t_non_counts = sum(x[2] for x in t_non_samples)
33
34 c_unmap = c_unmap - c_non
35 t_unmap = c_unmap - t_non
36
37 c_unmap_counts=c_unmap_counts - c_non_counts
38 t_unmap_counts=t_unmap_counts - t_non_counts
39
40
41 for x in c_samples:
42
43 if "/" not in x[0]:
44 if "chr" in x[0].split("_")[-1]:
45 c_mature+=1
46 c_mat_counts += x[2]
47 else:
48 c_templ+=1
49 c_tem_counts += x[2]
50 else:
51 f=0
52 for y in x[0].split("/"):
53 if "chr" in y.split("_")[-1]:
54 c_mature+=1
55 c_mat_counts += x[2]
56 f=1
57 break
58 if f==0:
59 c_templ+=1
60 c_tem_counts += x[2]
61
62 for x in t_samples:
63
64 if "/" not in x[0]:
65 if "chr" in x[0].split("_")[-1]:
66 t_mature+=1
67 t_mat_counts += x[2]
68 else:
69 t_templ+=1
70 t_tem_counts += x[2]
71 else:
72 f=0
73 for y in x[0].split("/"):
74 if "chr" in y.split("_")[-1]:
75 t_mature+=1
76 t_mat_counts += x[2]
77 f=1
78 break
79 if f==0:
80 t_templ+=1
81 t_tem_counts += x[2]
82
83 fig = plt.figure(figsize=(7,5))
84 labels = 'miRNA RefSeq','Template', 'Unassigned','Non-template'
85 sizes = [c_mat_counts, c_tem_counts, c_unmap_counts,c_non_counts]
86 colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
87 ax1 = plt.subplot2grid((1,2),(0,0))
88 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
89 [x.set_fontsize(8) for x in texts]
90 plt.title(group_name1 + ' Group (reads)',fontsize=12)
91 labels = 'miRNA RefSeq','Template', 'Unassigned','non-template'
92 sizes = [t_mat_counts, t_tem_counts, t_unmap_counts, t_non_counts]
93 colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
94 ax2 = plt.subplot2grid((1,2),(0,1))
95 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
96 [x.set_fontsize(8) for x in texts]
97 plt.title(group_name2 + ' Group (reads)', fontsize=12)
98 plt.savefig('pie_non.png',dpi=300)
99
100 ######################################################################################################################################################
101
102
103 def pie_temp(merge_con,c_unmap,c_unmap_counts,merge_tre,t_unmap,t_unmap_counts,group_name1,group_name2):
104
105 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_con]
106 t_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_tre]
107
108 c_templ = 0
109 c_tem_counts = 0
110 c_mature = 0
111 c_mat_counts = 0
112 t_templ = 0
113 t_tem_counts = 0
114 t_mature = 0
115 t_mat_counts = 0
116
117 for x in c_samples:
118
119 if "/" not in x[0]:
120 if "chr" in x[0].split("_")[-1]:
121 c_mature+=1
122 c_mat_counts += x[2]
123 else:
124 c_templ+=1
125 c_tem_counts += x[2]
126 else:
127 f=0
128 for y in x[0].split("/"):
129 if "chr" in y.split("_")[-1]:
130 c_mature+=1
131 c_mat_counts += x[2]
132 f=1
133 break
134 if f==0:
135 c_templ+=1
136 c_tem_counts += x[2]
137
138 for x in t_samples:
139
140 if "/" not in x[0]:
141 if "chr" in x[0].split("_")[-1]:
142 t_mature+=1
143 t_mat_counts += x[2]
144 else:
145 t_templ+=1
146 t_tem_counts += x[2]
147 else:
148 f=0
149 for y in x[0].split("/"):
150 if "chr" in y.split("_")[-1]:
151 t_mature+=1
152 t_mat_counts += x[2]
153 f=1
154 break
155 if f==0:
156 t_templ+=1
157 t_tem_counts += x[2]
158
159
160 fig = plt.figure()
161 labels = 'miRNA RefSeq','Template', 'Unassigned'
162 sizes = [c_mat_counts, c_tem_counts, c_unmap_counts]
163 colors = ['gold', 'yellowgreen', 'lightskyblue']
164 explode = (0.2, 0.05, 0.1)
165 ax1 = plt.subplot2grid((1,2),(0,0))
166 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
167 [x.set_fontsize(8) for x in texts]
168 plt.title(group_name1 + ' group (reads)', fontsize=12)
169 labels = 'miRNA RefSeq','Template', 'Unassigned'
170 sizes = [t_mat_counts, t_tem_counts, t_unmap_counts]
171 colors = ['gold', 'yellowgreen', 'lightskyblue']
172 explode = (0.2, 0.05, 0.1)
173 ax2 = plt.subplot2grid((1,2),(0,1))
174 patches, texts, autotexts=plt.pie(sizes, labels=labels, colors=colors, startangle=140,autopct='%1.1f%%',radius=0.8)
175 [x.set_fontsize(8) for x in texts]
176 plt.title(group_name2 + ' group (reads)',fontsize = 12)
177 plt.savefig('pie_tem.png',dpi=300)
178
179 ###################################################################################################################################################################################################################
180
181
182 def make_spider(merge_con,merge_tre,group_name1,group_name2):
183
184 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_con]
185 t_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge_tre]
186
187 c_5 = 0
188 c_5_counts = 0
189 c_3 = 0
190 c_3_counts = 0
191 c_both =0
192 c_both_counts=0
193 c_mature = 0
194 c_mat_counts = 0
195 c_exception=0
196 c_exception_counts=0
197
198
199 t_5 = 0
200 t_5_counts = 0
201 t_3 = 0
202 t_3_counts = 0
203 t_both = 0
204 t_both_counts = 0
205 t_mature = 0
206 t_mat_counts = 0
207 t_exception = 0
208 t_exception_counts=0
209
210 for x in c_samples:
211
212 if "/" not in x[0]:
213 if "chr" in x[0].split("_")[-1]:
214 c_mature+=1
215 c_mat_counts += x[2]
216 elif 0 == int(x[0].split("_")[-1]):
217 c_5+=1
218 c_5_counts += x[2]
219 elif 0 == int(x[0].split("_")[-2]):
220 c_3+=1
221 c_3_counts += x[2]
222 else:
223 c_both+=1
224 c_both_counts+=x[2]
225
226 else:
227 f=0
228 for y in x[0].split("/"):
229 if "chr" in y.split("_")[-1]:
230 c_mature+=1
231 c_mat_counts += x[2]
232 f=1
233 break
234 if f==0:
235 for y in x[0].split("/"):
236 c_exception+=1
237 c_exception_counts += x[2]
238
239
240 for x in t_samples:
241
242 if "/" not in x[0]:
243 if "chr" in x[0].split("_")[-1]:
244 t_mature+=1
245 t_mat_counts += x[2]
246 elif 0 == int(x[0].split("_")[-1]):
247 t_5+=1
248 t_5_counts += x[2]
249 elif 0 == int(x[0].split("_")[-2]):
250 t_3+=1
251 t_3_counts += x[2]
252 else:
253 t_both+=1
254 t_both_counts+=x[2]
255
256 else:
257 f=0
258 for y in x[0].split("/"):
259 if "chr" in y.split("_")[-1]:
260 t_mature+=1
261 t_mat_counts += x[2]
262 f=1
263 break
264 if f==0:
265 for y in x[0].split("/"):
266 t_exception+=1
267 t_exception_counts += x[2]
268
269
270 c_all = c_5+c_3+c_both+c_mature+c_exception
271 c_all_counts = c_5_counts + c_3_counts + c_both_counts + c_mat_counts + c_exception_counts
272
273 t_all = t_5+t_3+t_both+t_mature + t_exception
274 t_all_counts = t_5_counts + t_3_counts + t_both_counts + t_mat_counts + t_exception_counts
275
276 c_5 = round(c_5/c_all*100,2)
277 c_3 = round(c_3/c_all*100,2)
278 c_both = round(c_both/c_all*100,2)
279 c_mature = round(c_mature/c_all*100,2)
280 c_exception = round(c_exception/c_all*100,2)
281
282 c_5_counts = round(c_5_counts/c_all_counts*100,2)
283 c_3_counts = round(c_3_counts/c_all_counts*100,2)
284 c_both_counts = round(c_both_counts/c_all_counts*100,2)
285 c_mat_counts = round(c_mat_counts/c_all_counts*100,2)
286 c_exception_counts = round(c_exception_counts/c_all_counts*100,2)
287
288 t_5 = round(t_5/t_all*100,2)
289 t_3 = round(t_3/t_all*100,2)
290 t_both = round(t_both/t_all*100,2)
291 t_mature = round(t_mature/t_all*100,2)
292 t_exception = round(t_exception/t_all*100,2)
293
294 t_5_counts = round(t_5_counts/t_all_counts*100,2)
295 t_3_counts = round(t_3_counts/t_all_counts*100,2)
296 t_both_counts = round(t_both_counts/t_all_counts*100,2)
297 t_mat_counts = round(t_mat_counts/t_all_counts*100,2)
298 t_exception_counts = round(t_exception_counts/t_all_counts*100,2)
299
300 radar_max = max(c_5, c_3, c_both,c_mature,c_exception,t_5,t_3,t_both,t_mature,t_exception)
301 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)
302
303 df=pd.DataFrame({
304 'group':[group_name1,group_name2],
305 """5'3'-isomiRs""":[c_both,t_both],
306 """3'-isomiRs""":[c_3,t_3],
307 'RefSeq miRNA':[c_mature,t_mature],
308 """5'-isomiRs""":[c_5,t_5],
309 'Others*':[c_exception,t_exception]})
310
311 df1=pd.DataFrame({
312 'group':[group_name1,group_name2],
313 """5'3'-isomiRs""":[c_both_counts,t_both_counts],
314 """3'-isomiRs""":[c_3_counts,t_3_counts],
315 'RefSeq miRNA':[c_mat_counts,t_mat_counts],
316 """5'-isomiRs""":[c_5_counts,t_5_counts],
317 'Others*':[c_exception_counts,t_exception_counts]})
318
319 spider_last(df,radar_max,1,group_name1,group_name2)
320 spider_last(df1,radar_max_counts,2,group_name1,group_name2)
321
322 #####################################################################################################################################################
323
324 def spider_last(df,radar_max,flag,group_name1,group_name2):
325 # ------- PART 1: Create background
326 fig = plt.figure()
327 # number of variable
328 categories=list(df)[1:]
329 N = len(categories)
330
331 # What will be the angle of each axis in the plot? (we divide the plot / number of variable)
332 angles = [n / float(N) * 2 * pi for n in range(N)]
333 angles += angles[:1]
334
335 # Initialise the spider plot
336 ax = plt.subplot(111, polar=True)
337
338 # If you want the first axis to be on top:
339 ax.set_theta_offset(pi/2)
340 ax.set_theta_direction(-1)
341
342 # Draw one axe per variable + add labels labels yet
343 plt.xticks(angles[:-1], categories, fontsize=11)
344
345 # Draw ylabels
346 radar_max=round(radar_max+radar_max*0.1)
347 mul=len(str(radar_max))-1
348 maxi=int(math.ceil(radar_max / pow(10,mul))) * pow(10,mul)
349 sep = round(maxi/4)
350 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)
351 plt.ylim(0, maxi)
352
353 # ------- PART 2: Add plots
354
355 # Plot each individual = each line of the data
356 # I don't do a loop, because plotting more than 3 groups makes the chart unreadable
357
358 # Ind1
359 values=df.loc[0].drop('group').values.flatten().tolist()
360 values += values[:1]
361 ax.plot(angles, values,'-o', linewidth=1, linestyle='solid', label=group_name1)
362 ax.fill(angles, values, 'b', alpha=0.1)
363
364 # Ind2
365 values=df.loc[1].drop('group').values.flatten().tolist()
366 values += values[:1]
367 ax.plot(angles, values, '-o' ,linewidth=1, linestyle='solid', label=group_name2)
368 ax.fill(angles, values, 'r', alpha=0.1)
369
370 # Add legend
371 if flag==1:
372 plt.legend(loc='upper right', bbox_to_anchor=(0.0, 0.1))
373 plt.savefig('spider_non_red.png',dpi=300)
374 else:
375 plt.legend(loc='upper right', bbox_to_anchor=(0.0, 0.1))
376 plt.savefig('spider_red.png',dpi=300)
377
378
379 #############################################################################################################################################################################################################
380
381 def hist_red(samples,flag,group_name):
382
383 lengths=[]
384 cat=[]
385 total_reads=0
386 seq=[]
387
388 if flag == "c":
389 title = "Length Distribution of "+ group_name +" group (Redudant reads)"
390 if flag == "t":
391 title = "Length Distribution of "+ group_name +" group (Redudant reads)"
392
393 # classification of the sequences on two categories mapped or unmapped
394 for i in samples:
395 for x in i:
396 lengths.append(x[3])
397 if x[1]=="0":
398 seq.append([x[3],x[0].split("-")[1],"Mapped"])
399 cat.append("Mapped")
400 if x[1] == "4":
401 seq.append([x[3],x[0].split("-")[1],"Unassigned"])
402 cat.append("Unassigned")
403
404 # Keep lengths below 35nts
405 uni_len=list(set(lengths))
406 uni_len=[x for x in uni_len if x<=35]
407
408 # Remove duplicates from sequences
409 seq.sort()
410 uni_seq=list(seq for seq,_ in itertools.groupby(seq))
411
412 # Calculation of the reads per group (mapped or unmapped)
413 total_reads+=sum([int(x[1]) for x in uni_seq])
414 map_reads=[]
415 unmap_reads=[]
416 length=[]
417 for y in uni_len:
418 map_temp=0
419 unmap_temp=0
420 for x in uni_seq:
421 if x[0]==y and x[2]=="Mapped":
422 map_temp+=int(x[1])
423 if x[0]==y and x[2]=="Unassigned":
424 unmap_temp+=int(x[1])
425 length.append(y)
426 map_reads.append(round(map_temp/total_reads*100,2)) # percentage of mapped reads over total number of sequences
427 unmap_reads.append(round(unmap_temp/total_reads*100,2)) # percentage of unmapped reads over total number of sequences
428
429 # Generation of the graph
430 ylim=max([sum(x) for x in zip(unmap_reads, map_reads)])
431 ylim=ylim+ylim*20/100
432 fig, ax = plt.subplots()
433 width=0.8
434 ax.bar(length, unmap_reads, width, label='Unassigned')
435 h=ax.bar(length, map_reads, width, bottom=unmap_reads, label='Mapped')
436 plt.xticks(np.arange(length[0], length[-1]+1, 1))
437 plt.yticks(np.arange(0, ylim, 5))
438 plt.xlabel('Length (nt)',fontsize=14)
439 plt.ylabel('Percentage',fontsize=14)
440 plt.title(title,fontsize=14)
441 ax.legend()
442 plt.ylim([0, ylim])
443 ax.grid(axis='y',linewidth=0.2)
444
445 # Save of the graph
446 if flag=='c':
447 plt.savefig('c_hist_red.png',dpi=300)
448
449 if flag=='t':
450 plt.savefig('t_hist_red.png',dpi=300)
451
452 #################################################################################################################
453
454 def logo_seq_red(merge, flag, group_name):
455
456 if flag=="c":
457 titlos= group_name + " group (Redundant)"
458 file_logo="c_logo.png"
459 file_bar="c_bar.png"
460 if flag=="t":
461 titlos= group_name + " group (Redundant)"
462 file_logo="t_logo.png"
463 file_bar="t_bar.png"
464
465 c_samples=[[x[0],x[1],sum(int(i) for i in x[2:])] for x in merge]
466
467 A=[0]*3
468 C=[0]*3
469 G=[0]*3
470 T=[0]*3
471 total_reads=0
472
473 for y in c_samples:
474 if "/" in y[0]:
475 length=[]
476 for x in y[0].split("/"):
477 length.append([len(x.split("_")[-1]),x.split("_")[-1],y[2]])
478
479 best=min(length)
480 total_reads+=best[2]
481 for i in range(3):
482 if i<len(best[1]):
483 if best[1][i] == "A":
484 A[i]+=best[2]
485 elif best[1][i] == "C":
486 C[i]+=best[2]
487 elif best[1][i] == "G":
488 G[i]+=best[2]
489 else:
490 T[i]+=best[2]
491 else:
492 total_reads+=y[2]
493 for i in range(3):
494 if i<len(y[0].split("_")[-1]):
495 if y[0].split("_")[-1][i] == "A":
496 A[i]+=(y[2])
497 elif y[0].split("_")[-1][i] == "C":
498 C[i]+=(y[2])
499 elif y[0].split("_")[-1][i] == "G":
500 G[i]+=(y[2])
501 else:
502 T[i]+=y[2]
503
504 A[:] = [round(x*100,1) / total_reads for x in A]
505 C[:] = [round(x*100,1) / total_reads for x in C]
506 G[:] = [round(x*100,1) / total_reads for x in G]
507 T[:] = [round(x*100,1) / total_reads for x in T]
508
509
510
511 data = {'A':A,'C':C,'G':G,'T':T}
512 df = pd.DataFrame(data, index=[1,2,3])
513 h=df.plot.bar(color=tuple(["g", "b","gold","r"]) )
514 h.grid(axis='y',linewidth=0.2)
515 plt.xticks(rotation=0, ha="right")
516 plt.ylabel("Counts (%)",fontsize=18)
517 plt.xlabel("Numbers of additional nucleotides",fontsize=18)
518 plt.title(titlos,fontsize=20)
519 plt.tight_layout()
520 plt.savefig(file_bar, dpi=300)
521
522 crp_logo = lm.Logo(df, font_name = 'DejaVu Sans')
523 crp_logo.style_spines(visible=False)
524 crp_logo.style_spines(spines=['left', 'bottom'], visible=True)
525 crp_logo.style_xticks(rotation=0, fmt='%d', anchor=0)
526
527 # style using Axes methods
528 crp_logo.ax.set_title(titlos,fontsize=18)
529 crp_logo.ax.set_ylabel("Counts (%)", fontsize=16,labelpad=5)
530 crp_logo.ax.set_xlabel("Numbers of additional nucleotides",fontsize=16, labelpad=5)
531 crp_logo.ax.xaxis.set_ticks_position('none')
532 crp_logo.ax.xaxis.set_tick_params(pad=-1)
533 figure = plt.gcf()
534 figure.set_size_inches(6, 4)
535 crp_logo.fig.savefig(file_logo,dpi=300)
536
537 ##########################################################################################################################################################################################################
538
539 def pdf_before_DE(analysis,group_name1,group_name2):
540
541 # Image extensions
542 if analysis=="2":
543 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")
544 else:
545 image_extensions = ("c_hist_red.png","t_hist_red.png","pie_tem.png","spider_red.png","spider_non_red.png")
546 # This list will hold the images file names
547 images = []
548
549 # Build the image list by merging the glob results (a list of files)
550 # for each extension. We are taking images from current folder.
551 for extension in image_extensions:
552 images.extend(glob.glob(extension))
553
554 # Create instance of FPDF class
555 pdf = FPDF('P', 'in', 'A4')
556 # Add new page. Without this you cannot create the document.
557 pdf.add_page()
558 # Set font to Arial, 'B'old, 16 pts
559 pdf.set_font('Arial', 'B', 20.0)
560
561 # Page header
562 pdf.cell(pdf.w-0.5, 0.5, 'IsomiR Profile Report',align='C')
563 pdf.ln(0.7)
564 pdf.set_font('Arial','B', 16.0)
565 pdf.cell(pdf.w-0.5, 0.5, 'sRNA length distribution',align='C')
566
567 # Smaller font for image captions
568 pdf.set_font('Arial', '', 11.0)
569 pdf.ln(0.5)
570
571 yh=FPDF.get_y(pdf)
572 pdf.image(images[0],x=0.3,w=4, h=3)
573 pdf.image(images[1],x=4,y=yh, w=4, h=3)
574 pdf.ln(0.3)
575
576 pdf.cell(0.2)
577 pdf.cell(3.0, 0.0, " Mapped and unmapped reads to custom precussor arm reference DB (5p and 3p arms) in "+group_name1)
578 pdf.ln(0.2)
579 pdf.cell(0.2)
580 pdf.cell(3.0, 0.0, " (left) and "+group_name2+" (right) groups")
581
582 pdf.ln(0.5)
583 h1=FPDF.get_y(pdf)
584 pdf.image(images[2],x=1, w=6.5, h=5)
585 h2=FPDF.get_y(pdf)
586 FPDF.set_y(pdf,h1+0.2)
587 pdf.set_font('Arial','B', 16.0)
588 pdf.cell(pdf.w-0.5, 0.5, 'Templated and non-templated isomiRs',align='C')
589 pdf.set_font('Arial', '', 11.0)
590 FPDF.set_y(pdf,h2)
591 FPDF.set_y(pdf,9.5)
592 pdf.cell(0.2)
593
594 if analysis=="2":
595 pdf.cell(3.0, 0.0, " RefSeq miRNAs, templated isomiRs, non-templated isomiRs and unassigned sequences as percentage")
596 pdf.ln(0.2)
597 pdf.cell(0.2)
598 pdf.cell(3.0, 0.0, " of total sRNA reads in "+group_name1+" (left) and "+group_name2+" (right) groups")
599 else:
600 pdf.cell(3.0, 0.0, " RefSeq miRNAS, Templated isomiRs and unassigned sequences as percentage of total sRNA reads in")
601 pdf.ln(0.2)
602 pdf.cell(0.2)
603 pdf.cell(3.0, 0.0, " "+group_name1+" (left) and "+group_name2 + " (right) groups")
604
605 pdf.add_page()
606 pdf.set_font('Arial', 'B', 18.0)
607 pdf.cell(pdf.w-0.5, 0.5, "Templated isomiR subtypes",align='C')
608 pdf.ln(0.7)
609 pdf.set_font('Arial', 'B', 14.0)
610 pdf.cell(pdf.w-0.5, 0.5, "Templated isomiR profile (redundant reads)",align='C')
611 pdf.ln(0.5)
612 pdf.image(images[3],x=1.5, w=5.5, h=4)
613 pdf.ln(0.6)
614 pdf.cell(pdf.w-0.5, 0.0, "Templated isomiR profile (non-redundant reads)",align='C')
615 pdf.set_font('Arial', '', 12.0)
616 pdf.ln(0.2)
617 pdf.image(images[4],x=1.5, w=5.5, h=4)
618 pdf.ln(0.3)
619 pdf.set_font('Arial', '', 11.0)
620 pdf.cell(0.2)
621 pdf.cell(3.0, 0.0, " * IsomiRs potentially generated from multiple loci")
622
623
624 if analysis=="2":
625 pdf.add_page('L')
626
627 pdf.set_font('Arial', 'B', 18.0)
628 pdf.cell(pdf.w-0.5, 0.5, "Non-templated isomiRs",align='C')
629 pdf.ln(0.5)
630 pdf.set_font('Arial', 'B', 14.0)
631 pdf.cell(pdf.w-0.5, 0.5, "3'-end additions to RefSeq miRNAs and templated isomiRs",align='C')
632 pdf.ln(0.7)
633
634 yh=FPDF.get_y(pdf)
635 pdf.image(images[5],x=1.5,w=3.65, h=2.65)
636 pdf.image(images[7],x=6.5,y=yh, w=3.65, h=2.65)
637 pdf.ln(0.5)
638 yh=FPDF.get_y(pdf)
639 pdf.image(images[6],x=1.5,w=3.65, h=2.65)
640 pdf.image(images[8],x=6.5,y=yh, w=3.65, h=2.65)
641
642 pdf.close()
643 pdf.output('report1.pdf','F')
644
645 #############################################################################################################################################################3
646
647