-
Notifications
You must be signed in to change notification settings - Fork 0
/
results-and-discussion.html
2168 lines (2126 loc) · 81.7 KB
/
results-and-discussion.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<meta charset="utf-8" />
<meta http-equiv="X-UA-Compatible" content="IE=edge" />
<title>4 Results and Discussion | RNA-Sequencing to improve characterisation and production of iPSC-induced cardiomyocytes</title>
<meta name="description" content="4 Results and Discussion | RNA-Sequencing to improve characterisation and production of iPSC-induced cardiomyocytes" />
<meta name="generator" content="bookdown 0.18 and GitBook 2.6.7" />
<meta property="og:title" content="4 Results and Discussion | RNA-Sequencing to improve characterisation and production of iPSC-induced cardiomyocytes" />
<meta property="og:type" content="book" />
<meta name="twitter:card" content="summary" />
<meta name="twitter:title" content="4 Results and Discussion | RNA-Sequencing to improve characterisation and production of iPSC-induced cardiomyocytes" />
<meta name="author" content="Harithaa Anandakumar" />
<meta name="date" content="2020-01-01" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="apple-mobile-web-app-capable" content="yes" />
<meta name="apple-mobile-web-app-status-bar-style" content="black" />
<link rel="prev" href="methods.html"/>
<link rel="next" href="conclusion-and-future-work.html"/>
<script src="libs/jquery-2.2.3/jquery.min.js"></script>
<link href="libs/gitbook-2.6.7/css/style.css" rel="stylesheet" />
<link href="libs/gitbook-2.6.7/css/plugin-bookdown.css" rel="stylesheet" />
<link href="libs/gitbook-2.6.7/css/plugin-highlight.css" rel="stylesheet" />
<link href="libs/gitbook-2.6.7/css/plugin-search.css" rel="stylesheet" />
<link href="libs/gitbook-2.6.7/css/plugin-fontsettings.css" rel="stylesheet" />
<link href="libs/gitbook-2.6.7/css/plugin-clipboard.css" rel="stylesheet" />
<script src="libs/htmlwidgets-1.5.1/htmlwidgets.js"></script>
<script src="libs/plotly-binding-4.9.0/plotly.js"></script>
<script src="libs/typedarray-0.1/typedarray.min.js"></script>
<link href="libs/crosstalk-1.0.0/css/crosstalk.css" rel="stylesheet" />
<script src="libs/crosstalk-1.0.0/js/crosstalk.min.js"></script>
<link href="libs/plotly-htmlwidgets-css-1.46.1/plotly-htmlwidgets.css" rel="stylesheet" />
<script src="libs/plotly-main-1.46.1/plotly-latest.min.js"></script>
<script src="libs/kePrint-0.0.1/kePrint.js"></script>
<link href="libs/bsTable-3.3.7/bootstrapTable.min.css" rel="stylesheet" />
</head>
<body>
<div class="book without-animation with-summary font-size-2 font-family-1" data-basepath=".">
<div class="book-summary">
<nav role="navigation">
<ul class="summary">
<li class="chapter" data-level="1" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i><b>1</b> Introduction</a><ul>
<li class="chapter" data-level="1.1" data-path="index.html"><a href="index.html#need-for-better-therapeutics"><i class="fa fa-check"></i><b>1.1</b> Need for better therapeutics</a><ul>
<li class="chapter" data-level="1.1.1" data-path="index.html"><a href="index.html#immunological-responses-in-transplantations"><i class="fa fa-check"></i><b>1.1.1</b> Immunological Responses in Transplantations</a></li>
</ul></li>
<li class="chapter" data-level="1.2" data-path="index.html"><a href="index.html#engineered-human-myocardium"><i class="fa fa-check"></i><b>1.2</b> Engineered Human Myocardium</a><ul>
<li class="chapter" data-level="1.2.1" data-path="index.html"><a href="index.html#cgmp-and-quality-control-of-tissue-engineered-products"><i class="fa fa-check"></i><b>1.2.1</b> cGMP and Quality Control of Tissue Engineered Products</a></li>
</ul></li>
<li class="chapter" data-level="1.3" data-path="index.html"><a href="index.html#rnaseq"><i class="fa fa-check"></i><b>1.3</b> RNA Sequencing</a><ul>
<li class="chapter" data-level="1.3.1" data-path="index.html"><a href="index.html#bulkrna"><i class="fa fa-check"></i><b>1.3.1</b> Single cell versus bulk RNA Seq</a></li>
</ul></li>
<li class="chapter" data-level="1.4" data-path="index.html"><a href="index.html#compdeconv"><i class="fa fa-check"></i><b>1.4</b> Computational deconvolution</a></li>
<li class="chapter" data-level="1.5" data-path="index.html"><a href="index.html#exploratory-data-analysis-in-rna-sequencing"><i class="fa fa-check"></i><b>1.5</b> Exploratory Data Analysis in RNA-Sequencing</a><ul>
<li class="chapter" data-level="1.5.1" data-path="index.html"><a href="index.html#pca"><i class="fa fa-check"></i><b>1.5.1</b> Principal Component Analysis (PCA)</a></li>
</ul></li>
<li class="chapter" data-level="1.6" data-path="index.html"><a href="index.html#rationale"><i class="fa fa-check"></i><b>1.6</b> Rationale for the current work</a></li>
</ul></li>
<li class="chapter" data-level="2" data-path="aims-and-objectives.html"><a href="aims-and-objectives.html"><i class="fa fa-check"></i><b>2</b> Aims and Objectives</a></li>
<li class="chapter" data-level="3" data-path="methods.html"><a href="methods.html"><i class="fa fa-check"></i><b>3</b> Methods</a><ul>
<li class="chapter" data-level="3.1" data-path="methods.html"><a href="methods.html#general-analysis-pipeline-of-bulk-rna-seq-data"><i class="fa fa-check"></i><b>3.1</b> General Analysis Pipeline of Bulk RNA-Seq Data</a></li>
<li class="chapter" data-level="3.2" data-path="methods.html"><a href="methods.html#singleCell"><i class="fa fa-check"></i><b>3.2</b> Single Cell Reference Data and CIBERSORTX</a><ul>
<li class="chapter" data-level="3.2.1" data-path="methods.html"><a href="methods.html#processing-of-single-cell-data"><i class="fa fa-check"></i><b>3.2.1</b> Processing of Single Cell Data</a></li>
</ul></li>
<li class="chapter" data-level="3.3" data-path="methods.html"><a href="methods.html#analysis-of-rhesus-rna-seq"><i class="fa fa-check"></i><b>3.3</b> Analysis of Rhesus RNA-Seq</a></li>
<li class="chapter" data-level="3.4" data-path="methods.html"><a href="methods.html#estBacVir"><i class="fa fa-check"></i><b>3.4</b> Estimating Bacterial and Viral Contaminants</a></li>
</ul></li>
<li class="chapter" data-level="4" data-path="results-and-discussion.html"><a href="results-and-discussion.html"><i class="fa fa-check"></i><b>4</b> Results and Discussion</a><ul>
<li class="chapter" data-level="4.1" data-path="results-and-discussion.html"><a href="results-and-discussion.html#general-workflow-and-mapping-statistics"><i class="fa fa-check"></i><b>4.1</b> General Workflow and Mapping Statistics</a></li>
<li class="chapter" data-level="4.2" data-path="results-and-discussion.html"><a href="results-and-discussion.html#exploring-potential-microbial-contamination-using-rna-seq-data"><i class="fa fa-check"></i><b>4.2</b> Exploring Potential Microbial Contamination using RNA-Seq Data</a></li>
<li class="chapter" data-level="4.3" data-path="results-and-discussion.html"><a href="results-and-discussion.html#global-view-of-the-transcriptomic-data"><i class="fa fa-check"></i><b>4.3</b> Global view of the transcriptomic data</a><ul>
<li class="chapter" data-level="4.3.1" data-path="results-and-discussion.html"><a href="results-and-discussion.html#correlation-amongst-groups"><i class="fa fa-check"></i><b>4.3.1</b> Correlation amongst groups</a></li>
<li class="chapter" data-level="4.3.2" data-path="results-and-discussion.html"><a href="results-and-discussion.html#gene-level-analysis"><i class="fa fa-check"></i><b>4.3.2</b> Gene-level analysis</a></li>
</ul></li>
<li class="chapter" data-level="4.4" data-path="results-and-discussion.html"><a href="results-and-discussion.html#deconvolution-of-bulk-cms-and-ehms-rna-seq-data"><i class="fa fa-check"></i><b>4.4</b> Deconvolution of Bulk CMs and EHMs RNA-Seq Data</a><ul>
<li class="chapter" data-level="4.4.1" data-path="results-and-discussion.html"><a href="results-and-discussion.html#limits-of-deconvolution"><i class="fa fa-check"></i><b>4.4.1</b> Limits of deconvolution</a></li>
</ul></li>
<li class="chapter" data-level="4.5" data-path="results-and-discussion.html"><a href="results-and-discussion.html#basic-characterisation-of-rhesus-cardiomyocytes"><i class="fa fa-check"></i><b>4.5</b> Basic characterisation of Rhesus Cardiomyocytes</a></li>
</ul></li>
<li class="chapter" data-level="5" data-path="conclusion-and-future-work.html"><a href="conclusion-and-future-work.html"><i class="fa fa-check"></i><b>5</b> Conclusion and Future Work</a></li>
<li class="chapter" data-level="" data-path="summary.html"><a href="summary.html"><i class="fa fa-check"></i>Summary</a><ul>
<li class="chapter" data-level="" data-path="summary.html"><a href="summary.html#task-at-hand"><i class="fa fa-check"></i>Task At Hand</a></li>
<li class="chapter" data-level="" data-path="summary.html"><a href="summary.html#work-done"><i class="fa fa-check"></i>Work Done</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="references.html"><a href="references.html"><i class="fa fa-check"></i>References</a></li>
</ul>
</nav>
</div>
<div class="book-body">
<div class="body-inner">
<div class="book-header" role="navigation">
<h1>
<i class="fa fa-circle-o-notch fa-spin"></i><a href="./">RNA-Sequencing to improve characterisation and production of iPSC-induced cardiomyocytes</a>
</h1>
</div>
<div class="page-wrapper" tabindex="-1" role="main">
<div class="page-inner">
<section class="normal" id="section-">
<div id="results-and-discussion" class="section level1">
<h1><span class="header-section-number">4</span> Results and Discussion</h1>
<div id="general-workflow-and-mapping-statistics" class="section level2">
<h2><span class="header-section-number">4.1</span> General Workflow and Mapping Statistics</h2>
<p>Given that the analysis of RNA-Seq data is multi-faceted with distinct steps, a common work-flow modality was established as shown in Figure <a href="results-and-discussion.html#fig:resWorkFlow">4.1</a>.</p>
<div class="figure"><span id="fig:resWorkFlow"></span>
<img src="data/resultsFlowChart.png" alt="A General RNA-Seq Workflow. In general the source material of RNA-Seq is a sample either from patients, cell culture or tissues. After RNA extraction and optional enrichment, they get sequenced at a general sequencing (core) facility. This results in the generation of large files which are transferred to a HPC, where the first steps of alignment and abundance estimation is performed. Smaller count text files containing information on the transcripts and the quanitative amount of expression can then be easily stored in a local database for retrieval and further processing. The final steps of performing the required analysis specific for the question asked can be performed on any statistical environment (such as R) and visualized and deployed for publishing or internal use. Throughout this whole process it is vital to maintain a consistent and common, easy to use metadata collection system. Shapes and their meanings: cylinders (database), rhombus (decision points), rectangles (processes), oval (starting/ending points)." width="100%" />
<p class="caption">
Figure 4.1: A General RNA-Seq Workflow. In general the source material of RNA-Seq is a sample either from patients, cell culture or tissues. After RNA extraction and optional enrichment, they get sequenced at a general sequencing (core) facility. This results in the generation of large files which are transferred to a HPC, where the first steps of alignment and abundance estimation is performed. Smaller count text files containing information on the transcripts and the quanitative amount of expression can then be easily stored in a local database for retrieval and further processing. The final steps of performing the required analysis specific for the question asked can be performed on any statistical environment (such as R) and visualized and deployed for publishing or internal use. Throughout this whole process it is vital to maintain a consistent and common, easy to use metadata collection system. Shapes and their meanings: cylinders (database), rhombus (decision points), rectangles (processes), oval (starting/ending points).
</p>
</div>
<p>The average alignment for <em>uniquely mapped reads</em> is ~70% across all samples used in this study, while the number of reads assigned to specific genomic coordinates by <em>featureCounts</em> abundance estimation tool is on average ~60%. These fall within normal acceptable ranges.
There were no excessive adaptor content or duplicated reads observed.</p>
</div>
<div id="exploring-potential-microbial-contamination-using-rna-seq-data" class="section level2">
<h2><span class="header-section-number">4.2</span> Exploring Potential Microbial Contamination using RNA-Seq Data</h2>
<p>To explore the potential microbial contaminants amongst the sample data sets, a representative subset of samples were chosen, see Table <a href="results-and-discussion.html#tab:subsetDes">4.1</a>, such that every category i.e., adult heart, fetal heart, CM, EHM is represented by one sample in the subset, chosen across different sequencing runs, different years and different projects.
Human and non-human reads were separated and the latter were used as possible microbial read candidates.
After a series of filtering, as explained in Section <a href="methods.html#estBacVir">3.4</a>, the non-host, high-quality and unique reads were aligned against the reference genomes of bacteria and virus.</p>
<table class="table table-striped table-hover table-bordered table-condensed" style="width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
<span id="tab:subsetDes">Table 4.1: </span>Samples chosen for in-depth analysis
</caption>
<thead>
<tr>
<th style="text-align:left;">
Sample
</th>
<th style="text-align:left;">
Sample Number
</th>
<th style="text-align:left;">
Project Accession
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;width: 1em; ">
SRR1663123_GSM1554465
</td>
<td style="text-align:left;">
1
</td>
<td style="text-align:left;">
PRJNA268504
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
SRR6706796_GSM2991857
</td>
<td style="text-align:left;">
2
</td>
<td style="text-align:left;">
PRJNA433831
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
Sample_r733sCDICM3
</td>
<td style="text-align:left;">
3
</td>
<td style="text-align:left;">
In-House
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
p556sCM10-3-4
</td>
<td style="text-align:left;">
4
</td>
<td style="text-align:left;">
In-House
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
p637sDiff6CM
</td>
<td style="text-align:left;">
5
</td>
<td style="text-align:left;">
In-House
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
p722s3C190604
</td>
<td style="text-align:left;">
6
</td>
<td style="text-align:left;">
In-House
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
p786sC190924A
</td>
<td style="text-align:left;">
7
</td>
<td style="text-align:left;">
In-House
</td>
</tr>
</tbody>
</table>
<table class="table table-striped table-hover table-bordered table-condensed" style="width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
<span id="tab:subsetTab">Table 4.2: </span>Sample Read Statistics
</caption>
<thead>
<tr>
<th style="border-bottom:hidden" colspan="2">
</th>
<th style="border-bottom:hidden; padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Human
</div>
</th>
<th style="border-bottom:hidden; padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Microbial
</div>
</th>
</tr>
<tr>
<th style="text-align:left;">
Sample Number
</th>
<th style="text-align:left;">
Total Reads
</th>
<th style="text-align:left;">
Primary
</th>
<th style="text-align:left;">
Multi-mapped
</th>
<th style="text-align:left;">
rRNA
</th>
<th style="text-align:left;">
Viral
</th>
<th style="text-align:left;">
Bacterial
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;width: 1em; ">
1
</td>
<td style="text-align:left;">
35M
</td>
<td style="text-align:left;">
13M
</td>
<td style="text-align:left;">
16M
</td>
<td style="text-align:left;">
6M
</td>
<td style="text-align:left;">
20K
</td>
<td style="text-align:left;">
1K
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
2
</td>
<td style="text-align:left;">
20M
</td>
<td style="text-align:left;">
17M
</td>
<td style="text-align:left;">
1M
</td>
<td style="text-align:left;">
2M
</td>
<td style="text-align:left;">
24K
</td>
<td style="text-align:left;">
2K
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
3
</td>
<td style="text-align:left;">
51M
</td>
<td style="text-align:left;">
27M
</td>
<td style="text-align:left;">
20M
</td>
<td style="text-align:left;">
4M
</td>
<td style="text-align:left;">
190
</td>
<td style="text-align:left;">
949
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
4
</td>
<td style="text-align:left;">
53M
</td>
<td style="text-align:left;">
42M
</td>
<td style="text-align:left;">
10M
</td>
<td style="text-align:left;">
1M
</td>
<td style="text-align:left;">
70
</td>
<td style="text-align:left;">
5K
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
5
</td>
<td style="text-align:left;">
52M
</td>
<td style="text-align:left;">
31M
</td>
<td style="text-align:left;">
17M
</td>
<td style="text-align:left;">
3M
</td>
<td style="text-align:left;">
320
</td>
<td style="text-align:left;">
3K
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
6
</td>
<td style="text-align:left;">
51M
</td>
<td style="text-align:left;">
49M
</td>
<td style="text-align:left;">
60K
</td>
<td style="text-align:left;">
2M
</td>
<td style="text-align:left;">
966
</td>
<td style="text-align:left;">
14K
</td>
</tr>
<tr>
<td style="text-align:left;width: 1em; ">
7
</td>
<td style="text-align:left;">
79M
</td>
<td style="text-align:left;">
36M
</td>
<td style="text-align:left;">
35M
</td>
<td style="text-align:left;">
8M
</td>
<td style="text-align:left;">
9K
</td>
<td style="text-align:left;">
3K
</td>
</tr>
</tbody>
</table>
<p>The general mapping statistics of the chosen samples can be seen in Figure <a href="results-and-discussion.html#fig:mapStat">4.2</a>A and tabulated in Table <a href="results-and-discussion.html#tab:subsetTab">4.2</a>. Samples vary in terms of their sequencing depth (akin to the total reads column) and the proportion mapped to the human genome, which varies between 40% to 91% across samples.<br />
A large variance is found in the number of viral/bacterial reads mapped per million human mapped reads, see Figure <a href="results-and-discussion.html#fig:mapStat">4.2</a>B.</p>
<div class="figure"><span id="fig:mapStat"></span>
<img src="data/overviewCon.png" alt="General Mapping Statistics. A shows samples and percentage of reads mapped to different groups. Y-aixs is in log scale to resolve low-expression reads. B shows shows separate bacterial and viral reads mapped per million human mapped reads per sample." width="100%" />
<p class="caption">
Figure 4.2: General Mapping Statistics. A shows samples and percentage of reads mapped to different groups. Y-aixs is in log scale to resolve low-expression reads. B shows shows separate bacterial and viral reads mapped per million human mapped reads per sample.
</p>
</div>
<p>Absolute numbers of reads confidently assigned to different viral species across different samples is shown in Figure <a href="results-and-discussion.html#fig:contaViral">4.3</a>A.
The same data is shown as relative abundance in Figure <a href="results-and-discussion.html#fig:contaViral">4.3</a>B.
Samples 1 and 2 have a disproportionate number of reads, about 20,000 reads, mapped to a single viral genome — the col phage or phi-X174 (PhiX - NC_00 1422.1).
Proteus phage is the second entity with high number of absolute reads assigned to it (~7000).</p>
<div class="figure"><span id="fig:contaViral"></span>
<img src="results/00viralSp.png" alt="Possible viral contaminants. A shows the absolute number of reads confidently assigned to different viral species across different samples, while B represents them as relative abundances." width="100%" />
<p class="caption">
Figure 4.3: Possible viral contaminants. A shows the absolute number of reads confidently assigned to different viral species across different samples, while B represents them as relative abundances.
</p>
</div>
<p>PhiX contaminants in samples 1 and 2 are most probably delibrately added as spike-in in <a href="https://www.illumina.com/content/dam/illumina-marketing/documents/products/technotes/technote-hiseq-low-diversity.pdf">illumina</a> HISEQ platforms to increase nucleotide diversity.
Samples 1 and 2 were sequenced on the HISEQ-2500<span class="citation"><sup><a href="#ref-yanEpigenomicLandscapeHuman2016">63</a></sup></span> and HISEQ-4000<span class="citation"><sup><a href="#ref-pavlovicComparativeAssessmentHuman2018">61</a></sup></span> platforms while all in-house samples were sequenced on HISEQ-2000<span class="citation"><sup><a href="#ref-tiburcyDefinedEngineeredHuman2017">30</a></sup></span> which has a separate, dedicated lane for the PhiX spike-in quality control to avoid PhiX reads to appear in the FASTQ files.<a href="#fn3" class="footnote-ref" id="fnref3"><sup>3</sup></a>
Unusually high numbers of viral reads were seen in Sample 7, mostly that of Proteus phage VB_PmiS-Isfahan (NC_041925).
Like other phage viruses, the Proteus phage also infects bacterial cells, specifically <em>Proteus mirabilis</em> a highly motile bacterium belonging to the <em>Enterobacteriaceae family</em>, which is the most common species responsible for catheter-associated urinary tract infections<span class="citation"><sup><a href="#ref-schafferProteusMirabilisUrinary2015">65</a></sup></span>.
No reads were confidently assigned to the <em>Proteus</em> genera of bacteria, however, proteus phage is considered to be highly lytic and there are several other bacteria belonging to the <em>Proteus</em> genera which are ubiquitously present on and in human guts which could be infected by the phage<span class="citation"><sup><a href="#ref-drzewieckaSignificanceRolesProteus2016">66</a></sup></span>, hence making this assignment plausible.
All other viruses detected were less 200 reads per sample except those that mapped to the HERVs.
These are viral sequences that represent ancient viral infections that affected the primates’ germ line and became stably integrated into the host genome.
This was an interesting find as ~ 8% of the human genome is said to be of viral origin, the HERVs.
The reason behind its baseline expression in most of the adult tissues nor its role in different pathologies are not well defined<span class="citation"><sup><a href="#ref-grandiHumanEndogenousRetroviruses2018">67</a>–<a href="#ref-nelsonDemystifiedHumanEndogenous2003">69</a></sup></span>.</p>
<div class="figure"><span id="fig:contaBacGe"></span>
<img src="results/00bacGenus1.png" alt="Possible bacterial contaminants at the Genus level. A shows the absolute number of reads confidently assigned to different bacterial genera across different samples, accounting for around 80 percent of all the reads assigned to bacteria, while B represents them as percentage proportions." width="100%" />
<p class="caption">
Figure 4.4: Possible bacterial contaminants at the Genus level. A shows the absolute number of reads confidently assigned to different bacterial genera across different samples, accounting for around 80 percent of all the reads assigned to bacteria, while B represents them as percentage proportions.
</p>
</div>
<p>Unlike viral, the bacterial reads were analyzed at two levels, genus (Figure <a href="results-and-discussion.html#fig:contaBacGe">4.4</a>) and species (Figure <a href="results-and-discussion.html#fig:contaBacSp">4.5</a>).
The 10 genera shown account for about ~80% of all reads mapped to bacteria while the 10 species shown account for about ~50% of all reads mapped.
All samples except Sample 6, are within acceptable limits of bacterial reads/sample, less than 100 bacterial reads/million human mapped reads<span class="citation"><sup><a href="#ref-strongMicrobialContaminationNext2014">70</a></sup></span>.
Sample 6 has ~13500 reads in absolute numbers and approximately 250 bacterial reads/million human mapped reads, most of which are accounted for by 4 genera — <em>Acinetobacter</em>, <em>Klebsiella</em>, <em>Neisseria</em>, <em>Pseudomonas</em>.
This is also reflected at the species level, where <em>Klebsiella pneumoniae</em> and <em>Neisseria gonorrhoeae</em> account for 57% of the entire bacterial contamination found in the sample while the rest is accounted for by 189 other species.
Low levels of both these bacteria are also found in the other in-house samples.
The presence of <em>Cutibacterium acnes</em>, <em>Pseudomonas</em> and <em>Acinetobacter</em> bacterial contamination has been well documented owing to their epidermal persence in the first case and to water associated presence, even ultra-purified, in the last two cases<span class="citation"><sup><a href="#ref-kekiApplicationSpecialOligotrophic2013">71</a>–<a href="#ref-salterReagentLaboratoryContamination2014">73</a></sup></span>.
While <em>Klebsiella</em> has been associated with pathologies, it is also a known opportunistic pathogen which is a normal part of the microbial flora of mucosal surfaces such as the mouth and throat and found ubiquitously in nature/environment<span class="citation"><sup><a href="#ref-bengoecheaKlebsiellaPneumoniaeInfection2019">74</a></sup></span>.
Likewise, although <em>Neisseria gonorrhoeae</em> is not a part of the normal flora, it could also present as benign/unnoticed infections of the mucosal surfaces urogenital tract, pharynx, and rectum, apart from causing a full-blown pathological disease<span class="citation"><sup><a href="#ref-escobarMacrophageNeisseriaGonorrhoeae2018">75</a></sup></span>.
The discovery of bacterial reads in cell line data and the finding of different bacterial taxa in data from different sequencing runs/groups/labs supports the idea that a good portion of bacterial reads are possibly not derived from the specimens themselves.</p>
<div class="figure"><span id="fig:contaBacSp"></span>
<img src="results/00bacSpecies1.png" alt="Possible bacterial contaminants at the species level. A shows the absolute number of reads confidently assigned to different bacterial species across different samples, accounting for around 50 percent of all the reads assigned to bacteria, while B represents them as percentage proportions." width="100%" />
<p class="caption">
Figure 4.5: Possible bacterial contaminants at the species level. A shows the absolute number of reads confidently assigned to different bacterial species across different samples, accounting for around 50 percent of all the reads assigned to bacteria, while B represents them as percentage proportions.
</p>
</div>
<p>The results from these 7 representative samples are in-line with papers published which looked at the microbial contamination in RNA-Seq samples in terms of their diversity and the range of proportions of microbial contamination differing between different samples.
For instance, work by Strong et al<span class="citation"><sup><a href="#ref-strongMicrobialContaminationNext2014">70</a></sup></span>, showed that <em>Acinetobacter</em> contributed to the highest number of reads.
While the study by Park et al<span class="citation"><sup><a href="#ref-parkSystematicSequencingbasedApproach2019">76</a></sup></span>, also picked up a large number of samples having PhiX phage reads in them.
All the samples assessed here have microbial reads less than 0.1% of total reads (including the first two samples if we account for the spike-in PhiX reads).
This is considered very low and acceptible by other papers which have published on this topic<span class="citation"><sup><a href="#ref-strongMicrobialContaminationNext2014">70</a></sup></span>, however, no rule/regulation stipulated by cGMP exists currently which deal with such non-standard exploration of microbial contamination in TEPs.<br />
The samples also differed in their sequencing depths which would have influenced the extent to which metagenomic reads were picked up.
This work is neither exhaustive nor confirmatory but since it has the possibility of working on already collected data, it could be a novel complementary and regulatory step that might one day be incorporated into a cGMP practice alongside other standard microbial detection techniques.</p>
</div>
<div id="global-view-of-the-transcriptomic-data" class="section level2">
<h2><span class="header-section-number">4.3</span> Global view of the transcriptomic data</h2>
<p>Data from 68 samples were collected (20 iPSC-CMs, 17 EHM, 16 Fetal Heart samples, 15 Adult Heart Samples) across different studies as shown in Table <a href="methods.html#tab:dataSource">3.1</a>.
To examine global trends in gene expression levels, the normalized data across all samples were visualized using PCA (Figure <a href="results-and-discussion.html#fig:pcaCheck">4.7</a>).
72.4% of total variation in the dataset can be explained by the first 4 PCs and the cumulative percentage of the variances explained by the first eight PCs is shown as a scree plot (Figure <a href="results-and-discussion.html#fig:pcaCheck2">4.6</a>A).
The major source of variation in the data is correlated with the sample type
and is captured by the first PC accounting for 42.9% of the variation in the data set, where the first PC effectively separates the sub-groups with an almost uni-directional progression from cardiomyocytes to EHMs to fetal heart tissue and adult heart subtypes, showing that this vector captures the increase in complexity of the tissue types (Figure <a href="results-and-discussion.html#fig:pcaCheck2">4.6</a>B).</p>
<div class="figure"><span id="fig:pcaCheck2"></span>
<img src="data/modPCAtop.png" alt="A shows the cummulative variance explained by the PCs. B and C show the different groups plotted against PC1 and PC2 respectively." width="100%" />
<p class="caption">
Figure 4.6: A shows the cummulative variance explained by the PCs. B and C show the different groups plotted against PC1 and PC2 respectively.
</p>
</div>
<p>The second largest source of variation is captured by PC2 accounting for 14.3% of the total variance and separates the fetal heart samples from the rest of the sample types as shown by the ordering of sample types according to PC2, (see Figure <a href="results-and-discussion.html#fig:pcaCheck2">4.6</a>C).</p>
<div class="figure"><span id="fig:pcaCheck"></span>
<img src="results/twoPCA.png" alt="A and B show the PCA plots of all the samples againt PC1 and PC2/PC4. The samples are coloured based on their groups." width="100%" />
<p class="caption">
Figure 4.7: A and B show the PCA plots of all the samples againt PC1 and PC2/PC4. The samples are coloured based on their groups.
</p>
</div>
<p>Cardiomyocyte samples which are >90% actinin+ in FACS are in-essence representative of a single cell type, while EHMs have the additional stromal cells.
Apart from these two, fetal heart tissues also contain a variety of other cell types and sub-types including differentiated and differentiating cells along different lineages.
The adult heart on the other hand is composed of not just cardiomyocytes and fibroblasts but also endothelial cells, immune cells, vascular smooth muscle cells and cells making up the conduction system.
Fetal heart samples show relatively loose clustering compared to adult heart samples.
This might be because the fetal samples were collected across different gestational periods, in the range of 9 weeks - 16 weeks (Figure <a href="results-and-discussion.html#fig:pcaCheck">4.7</a>A).</p>
<p>The EHM samples also show two semi-distinct clusters, corresponding to the two EHM sources — in-house and from the PRJNA362579 project.
The source of this variation within the EHM samples appears to be strongly associated with PC4, see Figures <a href="results-and-discussion.html#fig:ehmFacet">4.8</a> and <a href="results-and-discussion.html#fig:pcaCheck">4.7</a>B.</p>
<div class="figure"><span id="fig:ehmFacet"></span>
<img src="data/facetWrapEHMs.png" alt="Separation of EHMs by the first 4 PCs. The two groups of EHMs and the varying degress of separation by different PCs, visualized by a density plot." width="100%" />
<p class="caption">
Figure 4.8: Separation of EHMs by the first 4 PCs. The two groups of EHMs and the varying degress of separation by different PCs, visualized by a density plot.
</p>
</div>
<p>As explained in Section <a href="index.html#pca">1.5.1</a>, the loading scores of genes can be explored further to look for possible meaningful explanations of the basis of separations of the clusters.
The top 50 genes with the highest absolute loading values is tabulated for the first 4 PCs in Table <a href="results-and-discussion.html#tab:pcaAndGenes">4.3</a>.</p>
<table class="table table-striped table-hover table-bordered table-condensed" style="width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
<span id="tab:pcaAndGenes">Table 4.3: </span>Top 50 Genes with the highest absolute loading in the first 4 PCs
</caption>
<thead>
<tr>
<th style="text-align:left;">
Gene
</th>
<th style="text-align:right;">
PC
</th>
<th style="text-align:left;">
Gene
</th>
<th style="text-align:right;">
PC
</th>
<th style="text-align:left;">
Gene
</th>
<th style="text-align:right;">
PC
</th>
<th style="text-align:left;">
Gene
</th>
<th style="text-align:right;">
PC
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
GPR4
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
ARHGAP33
</td>
<td style="text-align:right;">
-0.055
</td>
<td style="text-align:left;">
PLAT
</td>
<td style="text-align:right;">
-0.064
</td>
<td style="text-align:left;">
ANGPT2
</td>
<td style="text-align:right;">
-0.071
</td>
</tr>
<tr>
<td style="text-align:left;">
PDE2A
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
PRRT2
</td>
<td style="text-align:right;">
-0.054
</td>
<td style="text-align:left;">
MMP14
</td>
<td style="text-align:right;">
-0.064
</td>
<td style="text-align:left;">
CLMP
</td>
<td style="text-align:right;">
-0.068
</td>
</tr>
<tr>
<td style="text-align:left;">
CD300LG
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
MXD3
</td>
<td style="text-align:right;">
-0.054
</td>
<td style="text-align:left;">
ITIH3
</td>
<td style="text-align:right;">
-0.061
</td>
<td style="text-align:left;">
MEG8
</td>
<td style="text-align:right;">
-0.067
</td>
</tr>
<tr>
<td style="text-align:left;">
GIMAP1
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
PIF1
</td>
<td style="text-align:right;">
-0.054
</td>
<td style="text-align:left;">
TNC
</td>
<td style="text-align:right;">
-0.060
</td>
<td style="text-align:left;">
F2RL2
</td>
<td style="text-align:right;">
-0.066
</td>
</tr>
<tr>
<td style="text-align:left;">
RAMP3
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
KIF18B
</td>
<td style="text-align:right;">
-0.053
</td>
<td style="text-align:left;">
TGFBI
</td>
<td style="text-align:right;">
-0.059
</td>
<td style="text-align:left;">
DOCK10
</td>
<td style="text-align:right;">
-0.065
</td>
</tr>
<tr>
<td style="text-align:left;">
APOD
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
CHTF18
</td>
<td style="text-align:right;">
-0.053
</td>
<td style="text-align:left;">
LIF
</td>
<td style="text-align:right;">
-0.059
</td>
<td style="text-align:left;">
SAMD9L
</td>
<td style="text-align:right;">
-0.062
</td>
</tr>
<tr>
<td style="text-align:left;">
SPAAR
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
TROAP
</td>
<td style="text-align:right;">
-0.053
</td>
<td style="text-align:left;">
ENC1
</td>
<td style="text-align:right;">
-0.059
</td>
<td style="text-align:left;">
PI15
</td>
<td style="text-align:right;">
-0.062
</td>
</tr>
<tr>
<td style="text-align:left;">
RAI2
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
HBG1
</td>
<td style="text-align:right;">
-0.052
</td>
<td style="text-align:left;">
LUM
</td>
<td style="text-align:right;">
-0.058
</td>
<td style="text-align:left;">
CTSK
</td>
<td style="text-align:right;">
-0.060
</td>
</tr>
<tr>
<td style="text-align:left;">
TMEM273
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
SPTA1
</td>
<td style="text-align:right;">
-0.051
</td>
<td style="text-align:left;">
TIMP1
</td>
<td style="text-align:right;">
-0.057
</td>
<td style="text-align:left;">
PAMR1
</td>
<td style="text-align:right;">
-0.060
</td>
</tr>
<tr>
<td style="text-align:left;">
APOL3
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
HBG2
</td>
<td style="text-align:right;">
-0.051
</td>
<td style="text-align:left;">
EFEMP1
</td>
<td style="text-align:right;">
-0.057
</td>
<td style="text-align:left;">
CCDC144B
</td>
<td style="text-align:right;">
-0.059
</td>
</tr>
<tr>
<td style="text-align:left;">
COL4A5
</td>
<td style="text-align:right;">
0.033
</td>
<td style="text-align:left;">
FAM95C
</td>
<td style="text-align:right;">
-0.051
</td>
<td style="text-align:left;">
FN1
</td>
<td style="text-align:right;">
-0.057
</td>
<td style="text-align:left;">
CLCA2
</td>
<td style="text-align:right;">
-0.059
</td>
</tr>
<tr>
<td style="text-align:left;">
SLC15A3
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
EPB42
</td>
<td style="text-align:right;">
-0.051
</td>
<td style="text-align:left;">
SRPX2
</td>
<td style="text-align:right;">
-0.057
</td>
<td style="text-align:left;">
SAMD9
</td>
<td style="text-align:right;">
-0.058
</td>
</tr>
<tr>
<td style="text-align:left;">
SLC9A3R2
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
HEMGN
</td>
<td style="text-align:right;">
-0.051
</td>
<td style="text-align:left;">
CFH
</td>
<td style="text-align:right;">
-0.056
</td>
<td style="text-align:left;">
CRB2
</td>
<td style="text-align:right;">
0.057
</td>
</tr>
<tr>
<td style="text-align:left;">
PRELP
</td>
<td style="text-align:right;">
-0.033
</td>
<td style="text-align:left;">
TMEM155
</td>
<td style="text-align:right;">
-0.051
</td>
<td style="text-align:left;">
RHOU
</td>
<td style="text-align:right;">
-0.056
</td>
<td style="text-align:left;">
TMEM119
</td>
<td style="text-align:right;">
-0.057
</td>
</tr>
<tr>
<td style="text-align:left;">
FABP4
</td>
<td style="text-align:right;">
-0.032
</td>
<td style="text-align:left;">
PKMYT1
</td>
<td style="text-align:right;">
-0.050
</td>
<td style="text-align:left;">
CCL2
</td>
<td style="text-align:right;">
-0.056
</td>
<td style="text-align:left;">
FAP
</td>
<td style="text-align:right;">
-0.057
</td>
</tr>
<tr>
<td style="text-align:left;">
GPX3
</td>
<td style="text-align:right;">
-0.032
</td>
<td style="text-align:left;">
YJEFN3
</td>
<td style="text-align:right;">
-0.050
</td>
<td style="text-align:left;">
IL1R1
</td>
<td style="text-align:right;">
-0.056
</td>
<td style="text-align:left;">
CITED4
</td>
<td style="text-align:right;">
0.057
</td>
</tr>
<tr>