-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathfeed.xml
More file actions
1474 lines (1155 loc) · 256 KB
/
Copy pathfeed.xml
File metadata and controls
1474 lines (1155 loc) · 256 KB
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
<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
<title>Jesper Maag PhD</title>
<description>Data analysis and visualisations</description>
<link>http://jespermaag.github.io/</link>
<atom:link href="http://jespermaag.github.io/feed.xml" rel="self" type="application/rss+xml"/>
<pubDate>Sun, 13 Dec 2020 21:05:40 -0500</pubDate>
<lastBuildDate>Sun, 13 Dec 2020 21:05:40 -0500</lastBuildDate>
<generator>Jekyll v3.5.2</generator>
<item>
<title>gganatogram</title>
<description><h1 id="gganatogram">gganatogram</h1>
<p><a href="https://github.com/jespermaag/gganatogram">https://github.com/jespermaag/gganatogram</a></p>
<p>Create anatogram images for different organisms. <br />
For now only human male is available. <br />.
The idea for this package came to me after seeing a twitter post for <a href="https://github.com/LCBC-UiO/ggseg">ggseg</a>.
I thougt something similar would be good for whole organisms.
Since I could not find anything similar, I deided to give creating my first R package a go.</p>
<p>This package uses the tissue coordinates from the figure in ArrayExpress Expression Atlas. <br />
<a href="https://www.ebi.ac.uk/gxa/home">https://www.ebi.ac.uk/gxa/home</a> <br />
<a href="https://github.com/ebi-gene-expression-group/anatomogram">https://github.com/ebi-gene-expression-group/anatomogram</a> <br /></p>
<h2 id="generation-of-package">Generation of package</h2>
<h3 id="download-all-svg">Download all svg</h3>
<p>To create the package, I first had to retrive the coordinates of all tissues from the Expression Atlas.
The anatogram package was downloaded using the following command.</p>
<pre><code>npm install --save anatomogram
</code></pre>
<h3 id="extract-coordinates-from-svg">Extract coordinates from svg</h3>
<p>I used python to extract the coordinates, names, and transformations for each tissue in the homo_sapiens.mal.svg file.
This code takes the svg and writes the name, coordinates, and transformation to a file, which is then processed in R.</p>
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span></span><span class="kn">from</span> <span class="nn">xml.dom</span> <span class="kn">import</span> <span class="n">minidom</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">csv</span>
<span class="n">organism</span><span class="o">=</span><span class="s2">&quot;homo_sapiens.male&quot;</span>
<span class="n">doc</span> <span class="o">=</span> <span class="n">minidom</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">organism</span> <span class="o">+</span> <span class="s2">&quot;.svg&quot;</span><span class="p">)</span>
<span class="n">your_csv_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">organism</span> <span class="o">+</span> <span class="s1">&#39;_coords.tsv&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
<span class="n">wr</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">writer</span><span class="p">(</span><span class="n">your_csv_file</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">doc</span><span class="o">.</span><span class="n">getElementsByTagName</span><span class="p">(</span><span class="s1">&#39;path&#39;</span><span class="p">):</span>
<span class="k">if</span> <span class="s2">&quot;outline&quot;</span> <span class="ow">in</span> <span class="n">path</span><span class="o">.</span><span class="n">getAttribute</span><span class="p">(</span><span class="s1">&#39;id&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="s2">&quot;LAYER_OUTLINE&quot;</span> <span class="ow">in</span> <span class="n">path</span><span class="o">.</span><span class="n">getAttribute</span><span class="p">(</span><span class="s1">&#39;id&#39;</span><span class="p">)</span> <span class="p">:</span>
<span class="n">wr</span><span class="o">.</span><span class="n">writerow</span><span class="p">([</span><span class="n">path</span><span class="o">.</span><span class="n">getAttribute</span><span class="p">(</span><span class="s1">&#39;id&#39;</span><span class="p">)</span> <span class="p">,</span><span class="n">path</span><span class="o">.</span><span class="n">getAttribute</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="s1">&#39;matrix(1,0,0,1,0,0)&#39;</span><span class="p">)])</span>
<span class="k">if</span> <span class="n">path</span><span class="o">.</span><span class="n">getAttribute</span><span class="p">(</span><span class="s1">&#39;id&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;UB&#39;</span><span class="p">):</span>
<span class="n">wr</span><span class="o">.</span><span class="n">writerow</span><span class="p">([</span><span class="n">path</span><span class="o">.</span><span class="n">getElementsByTagName</span><span class="p">(</span><span class="s1">&#39;title&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">firstChild</span><span class="o">.</span><span class="n">nodeValue</span><span class="p">,</span> <span class="n">path</span><span class="o">.</span><span class="n">getAttribute</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="s1">&#39;matrix(1,0,0,1,0,0)&#39;</span><span class="p">)])</span>
<span class="k">if</span> <span class="n">path</span><span class="o">.</span><span class="n">parentNode</span><span class="o">.</span><span class="n">attributes</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;UB&#39;</span><span class="p">):</span>
<span class="k">if</span> <span class="s2">&quot;transform&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">path</span><span class="o">.</span><span class="n">parentNode</span><span class="o">.</span><span class="n">attributes</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
<span class="n">wr</span><span class="o">.</span><span class="n">writerow</span><span class="p">([</span><span class="n">path</span><span class="o">.</span><span class="n">parentNode</span><span class="o">.</span><span class="n">attributes</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">path</span><span class="o">.</span><span class="n">getAttribute</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="s1">&#39;matrix(1,0,0,1,0,0)&#39;</span><span class="p">)])</span>
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">doc</span><span class="o">.</span><span class="n">getElementsByTagName</span><span class="p">(</span><span class="s1">&#39;g&#39;</span><span class="p">)[</span><span class="mi">5</span><span class="p">:]:</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">path</span><span class="o">.</span><span class="n">childNodes</span><span class="p">)</span> <span class="o">&gt;</span><span class="mi">0</span> <span class="p">:</span>
<span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">path</span><span class="o">.</span><span class="n">childNodes</span><span class="p">:</span>
<span class="k">if</span> <span class="s2">&quot;text&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">node</span><span class="o">.</span><span class="n">nodeName</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">nodeName</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">attributes</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="k">if</span> <span class="s1">&#39;d&#39;</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">attributes</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
<span class="n">nodeVal</span> <span class="o">=</span> <span class="n">node</span><span class="o">.</span><span class="n">attributes</span><span class="p">[</span><span class="s1">&#39;d&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value</span>
<span class="n">wr</span><span class="o">.</span><span class="n">writerow</span><span class="p">([</span><span class="n">path</span><span class="o">.</span><span class="n">childNodes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">attributes</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">nodeVal</span><span class="p">,</span> <span class="n">path</span><span class="o">.</span><span class="n">attributes</span><span class="p">[</span><span class="s1">&#39;transform&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">])</span>
<span class="n">your_csv_file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></code></pre></figure>
<h3 id="process-the-coordinates-in-r-and-create-a-package">Process the coordinates in R, and create a package</h3>
<p>I created a function to extract the coordinates into a data frame and transformed the data.
Some manual editing was required to get the right coordinates, and remove some tissues that did not work</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>extractCoords <span class="o">&lt;-</span> <span class="kr">function</span><span class="p">(</span>coords<span class="p">,</span> name<span class="p">,</span> transMatrix<span class="p">)</span> <span class="p">{</span>
<span class="kt">c</span> <span class="o">&lt;-</span> <span class="kp">strsplit</span><span class="p">(</span>coords<span class="p">,</span> <span class="s">&quot; &quot;</span><span class="p">)</span>
<span class="kt">c</span><span class="p">[[</span><span class="m">1</span><span class="p">]]</span>
<span class="kt">c</span><span class="p">[[</span><span class="m">1</span><span class="p">]][</span><span class="kt">c</span><span class="p">(</span><span class="kp">grep</span><span class="p">(</span><span class="s">&quot;M&quot;</span><span class="p">,</span> <span class="kt">c</span><span class="p">[[</span><span class="m">1</span><span class="p">]]</span> <span class="p">)</span><span class="m">+1</span><span class="p">,</span><span class="kp">grep</span><span class="p">(</span><span class="s">&quot;M&quot;</span><span class="p">,</span> <span class="kt">c</span><span class="p">[[</span><span class="m">1</span><span class="p">]]</span> <span class="p">)</span><span class="m">+2</span><span class="p">)]</span> <span class="o">&lt;-</span> <span class="kc">NA</span>
<span class="kt">c</span><span class="p">[[</span><span class="m">1</span><span class="p">]]</span> <span class="o">&lt;-</span> <span class="kt">c</span><span class="p">[[</span><span class="m">1</span><span class="p">]][</span><span class="kp">grep</span><span class="p">(</span><span class="s">&quot;[[:alpha:]]&quot;</span><span class="p">,</span> <span class="kt">c</span><span class="p">[[</span><span class="m">1</span><span class="p">]],</span> invert<span class="o">=</span><span class="kc">TRUE</span><span class="p">)]</span>
anatCoord <span class="o">&lt;-</span> <span class="kp">as.data.frame</span><span class="p">(</span><span class="kp">lapply</span><span class="p">(</span> <span class="kt">c</span><span class="p">,</span> <span class="kr">function</span><span class="p">(</span>u<span class="p">)</span>
<span class="kt">matrix</span><span class="p">(</span><span class="kp">as.numeric</span><span class="p">(</span><span class="kp">unlist</span><span class="p">(</span><span class="kp">strsplit</span><span class="p">(</span>u<span class="p">,</span> <span class="s">&quot;,&quot;</span><span class="p">))),</span>ncol<span class="o">=</span><span class="m">2</span><span class="p">,</span>byrow<span class="o">=</span><span class="kc">TRUE</span><span class="p">)</span> <span class="p">))</span>
anatCoord<span class="o">$</span>X2<span class="p">[</span><span class="kp">is.na</span><span class="p">(</span>anatCoord<span class="o">$</span>X1<span class="p">)]</span> <span class="o">&lt;-</span> <span class="kc">NA</span>
anatCoord<span class="o">$</span>X1<span class="p">[</span><span class="kp">is.na</span><span class="p">(</span>anatCoord<span class="o">$</span>X2<span class="p">)]</span> <span class="o">&lt;-</span> <span class="kc">NA</span>
anatCoord<span class="o">$</span>id <span class="o">&lt;-</span> name
<span class="kr">if</span> <span class="p">(</span><span class="kp">length</span><span class="p">(</span>transMatrix<span class="p">[</span><span class="kp">grep</span><span class="p">(</span><span class="s">&#39;matrix&#39;</span><span class="p">,</span> transMatrix<span class="p">)])</span><span class="o">&gt;</span><span class="m">0</span><span class="p">)</span> <span class="p">{</span>
transForm <span class="o">&lt;-</span> <span class="kp">gsub</span><span class="p">(</span><span class="s">&#39;matrix\\(|\\)&#39;</span><span class="p">,</span> <span class="s">&#39;&#39;</span><span class="p">,</span> transMatrix<span class="p">)</span>
transForm <span class="o">&lt;-</span> <span class="kp">as.numeric</span><span class="p">(</span><span class="kp">strsplit</span><span class="p">(</span>transForm<span class="p">,</span> <span class="s">&quot;,&quot;</span><span class="p">)[[</span><span class="m">1</span><span class="p">]])</span>
anatCoord<span class="o">$</span>x <span class="o">&lt;-</span> <span class="p">(</span>anatCoord<span class="o">$</span>X1<span class="o">*</span> transForm<span class="p">[</span><span class="m">1</span><span class="p">])</span> <span class="o">+</span> <span class="p">(</span>anatCoord<span class="o">$</span>X1<span class="o">*</span> transForm<span class="p">[</span><span class="m">3</span><span class="p">])</span> <span class="o">+</span> transForm<span class="p">[</span><span class="m">5</span><span class="p">]</span>
anatCoord<span class="o">$</span>y <span class="o">&lt;-</span> <span class="p">(</span>anatCoord<span class="o">$</span>X2<span class="o">*</span> transForm<span class="p">[</span><span class="m">2</span><span class="p">])</span> <span class="o">+</span> <span class="p">(</span>anatCoord<span class="o">$</span>X2<span class="o">*</span> transForm<span class="p">[</span><span class="m">4</span><span class="p">])</span> <span class="o">+</span> transForm<span class="p">[</span><span class="m">6</span><span class="p">]</span>
<span class="p">}</span> <span class="kr">else</span> <span class="kr">if</span> <span class="p">(</span><span class="kp">grep</span><span class="p">(</span><span class="s">&#39;translate&#39;</span><span class="p">,</span> transMatrix<span class="p">))</span> <span class="p">{</span>
transForm <span class="o">&lt;-</span> <span class="kp">gsub</span><span class="p">(</span><span class="s">&#39;translate\\(|\\)&#39;</span><span class="p">,</span> <span class="s">&#39;&#39;</span><span class="p">,</span> transMatrix<span class="p">)</span>
transForm <span class="o">&lt;-</span> <span class="kp">as.numeric</span><span class="p">(</span><span class="kp">strsplit</span><span class="p">(</span>transForm<span class="p">,</span> <span class="s">&quot;,&quot;</span><span class="p">)[[</span><span class="m">1</span><span class="p">]])</span>
<span class="kr">if</span><span class="p">(</span>name <span class="o">==</span><span class="s">&#39;leukocyte&#39;</span> <span class="o">&amp;</span> transForm<span class="p">[</span><span class="m">1</span><span class="p">]</span><span class="o">==</span><span class="m">4.5230265</span><span class="p">)</span> <span class="p">{</span>
transForm <span class="o">&lt;-</span> <span class="kt">c</span><span class="p">(</span><span class="m">103.63591+4.5230265</span><span class="p">,</span><span class="m">-47.577078+11.586659</span><span class="p">)</span>
<span class="p">}</span>
anatCoord<span class="o">$</span>x <span class="o">&lt;-</span> anatCoord<span class="o">$</span>X1 <span class="o">+</span> transForm<span class="p">[</span><span class="m">1</span><span class="p">]</span>
anatCoord<span class="o">$</span>y <span class="o">&lt;-</span> anatCoord<span class="o">$</span>X2 <span class="o">+</span> transForm<span class="p">[</span><span class="m">2</span><span class="p">]</span>
<span class="p">}</span>
<span class="c1">#anatCoord &lt;- anatCoord[complete.cases(anatCoord),]</span>
<span class="kr">if</span> <span class="p">(</span>name <span class="o">==</span> <span class="s">&#39;bronchus&#39;</span><span class="p">)</span> <span class="p">{</span>
<span class="kr">if</span> <span class="p">(</span><span class="kp">max</span><span class="p">(</span>anatCoord<span class="o">$</span>x<span class="p">,</span> na.rm<span class="o">=</span><span class="bp">T</span><span class="p">)</span> <span class="o">&gt;</span><span class="m">100</span> <span class="p">)</span> <span class="p">{</span>
anatCoord<span class="o">$</span>x <span class="o">&lt;-</span> <span class="kc">NA</span>
anatCoord<span class="o">$</span>y <span class="o">&lt;-</span> <span class="kc">NA</span>
<span class="p">}</span>
<span class="p">}</span>
<span class="kr">if</span><span class="p">(</span> <span class="kp">any</span><span class="p">(</span>anatCoord<span class="p">[</span>complete.cases<span class="p">(</span>anatCoord<span class="p">),]</span><span class="o">$</span>x <span class="o">&lt;</span> <span class="m">-5</span><span class="p">))</span> <span class="p">{</span>
anatCoord<span class="o">$</span>x <span class="o">&lt;-</span> <span class="kc">NA</span>
anatCoord<span class="o">$</span>y <span class="o">&lt;-</span> <span class="kc">NA</span>
<span class="p">}</span>
<span class="kr">if</span><span class="p">(</span> <span class="kp">any</span><span class="p">(</span>anatCoord<span class="p">[</span>complete.cases<span class="p">(</span>anatCoord<span class="p">),]</span><span class="o">$</span>x <span class="o">&gt;</span> <span class="m">150</span><span class="p">))</span> <span class="p">{</span>
anatCoord<span class="o">$</span>x <span class="o">&lt;-</span> <span class="kc">NA</span>
anatCoord<span class="o">$</span>y <span class="o">&lt;-</span> <span class="kc">NA</span>
<span class="p">}</span>
<span class="kr">return</span><span class="p">(</span>anatCoord<span class="p">)</span>
<span class="p">}</span></code></pre></figure>
<p>Finally, I processed the python output using the extractCoords function.</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>hsMale <span class="o">&lt;-</span> read.table<span class="p">(</span><span class="s">&#39;homo_sapiens.male_coords.tsv&#39;</span><span class="p">,</span> sep<span class="o">=</span><span class="s">&#39;\t&#39;</span><span class="p">,</span> stringsAsFactors<span class="o">=</span><span class="bp">F</span><span class="p">)</span>
hgMale_list <span class="o">&lt;-</span> <span class="kt">list</span><span class="p">()</span>
<span class="kr">for</span> <span class="p">(</span>i <span class="kr">in</span> <span class="m">1</span><span class="o">:</span><span class="kp">nrow</span><span class="p">(</span>hsMale<span class="p">))</span> <span class="p">{</span>
df <span class="o">&lt;-</span> extractCoords<span class="p">(</span>hsMale<span class="o">$</span>V2<span class="p">[</span>i<span class="p">],</span> hsMale<span class="o">$</span>V1<span class="p">[</span>i<span class="p">],</span> hsMale<span class="o">$</span>V3<span class="p">[</span>i<span class="p">])</span>
hgMale_list<span class="p">[[</span>i<span class="p">]]</span> <span class="o">&lt;-</span> extractCoords<span class="p">(</span>hsMale<span class="o">$</span>V2<span class="p">[</span>i<span class="p">],</span> hsMale<span class="o">$</span>V1<span class="p">[</span>i<span class="p">],</span> hsMale<span class="o">$</span>V3<span class="p">[</span>i<span class="p">])</span>
<span class="kp">names</span><span class="p">(</span>hgMale_list<span class="p">)[</span>i<span class="p">]</span> <span class="o">&lt;-</span> <span class="kp">paste0</span><span class="p">(</span>hsMale<span class="o">$</span>V1<span class="p">[</span>i<span class="p">],</span><span class="s">&#39;-&#39;</span><span class="p">,</span> i<span class="p">)</span>
<span class="p">}</span>
<span class="kp">names</span><span class="p">(</span>hgMale_list<span class="p">)</span> <span class="o">&lt;-</span> <span class="kp">gsub</span><span class="p">(</span><span class="s">&#39;-.*&#39;</span><span class="p">,</span> <span class="s">&#39;&#39;</span><span class="p">,</span> <span class="kp">names</span><span class="p">(</span>hgMale_list<span class="p">))</span></code></pre></figure>
<p>The resulting list was then used as the base for the gganatogram package.
The package can be installed from github using the instructions below.</p>
<h2 id="install">Install</h2>
<p>Install from github using devtools.</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="c1">## install from Github</span>
devtools<span class="o">::</span>install_github<span class="p">(</span><span class="s">&quot;jespermaag/gganatogram&quot;</span><span class="p">)</span></code></pre></figure>
<h2 id="usage">Usage</h2>
<p>This package requires <code>ggplot2</code> and <code>ggpolypath</code></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kn">library</span><span class="p">(</span>ggplot2<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>ggpolypath<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>gganatogram<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>dplyr<span class="p">)</span></code></pre></figure>
<p>In order to use the function gganatogram, you need to have a data frame with
organ, colour, and value if you want to.</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>organPlot <span class="o">&lt;-</span> <span class="kt">data.frame</span><span class="p">(</span>organ <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;heart&quot;</span><span class="p">,</span> <span class="s">&quot;leukocyte&quot;</span><span class="p">,</span> <span class="s">&quot;nerve&quot;</span><span class="p">,</span> <span class="s">&quot;brain&quot;</span><span class="p">,</span> <span class="s">&quot;liver&quot;</span><span class="p">,</span> <span class="s">&quot;stomach&quot;</span><span class="p">,</span> <span class="s">&quot;colon&quot;</span><span class="p">),</span>
type <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;circulation&quot;</span><span class="p">,</span> <span class="s">&quot;circulation&quot;</span><span class="p">,</span> <span class="s">&quot;nervous system&quot;</span><span class="p">,</span> <span class="s">&quot;nervous system&quot;</span><span class="p">,</span> <span class="s">&quot;digestion&quot;</span><span class="p">,</span> <span class="s">&quot;digestion&quot;</span><span class="p">,</span> <span class="s">&quot;digestion&quot;</span><span class="p">),</span>
colour <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;red&quot;</span><span class="p">,</span> <span class="s">&quot;red&quot;</span><span class="p">,</span> <span class="s">&quot;purple&quot;</span><span class="p">,</span> <span class="s">&quot;purple&quot;</span><span class="p">,</span> <span class="s">&quot;orange&quot;</span><span class="p">,</span> <span class="s">&quot;orange&quot;</span><span class="p">,</span> <span class="s">&quot;orange&quot;</span><span class="p">),</span>
value <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="m">10</span><span class="p">,</span> <span class="m">5</span><span class="p">,</span> <span class="m">1</span><span class="p">,</span> <span class="m">8</span><span class="p">,</span> <span class="m">2</span><span class="p">,</span> <span class="m">5</span><span class="p">,</span> <span class="m">5</span><span class="p">),</span>
stringsAsFactors<span class="o">=</span><span class="bp">F</span><span class="p">)</span>
<span class="kp">head</span><span class="p">(</span>organPlot<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## organ type colour value
## 1 heart circulation red 10
## 2 leukocyte circulation red 5
## 3 nerve nervous system purple 1
## 4 brain nervous system purple 8
## 5 liver digestion orange 2
## 6 stomach digestion orange 5</code></pre></figure>
<p>Using the function gganatogram with the filling the organs based on colour.</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>gganatogram<span class="p">(</span>data<span class="o">=</span>organPlot<span class="p">,</span> fillOutline<span class="o">=</span><span class="s">&#39;#a6bddb&#39;</span><span class="p">,</span> organism<span class="o">=</span><span class="s">&#39;human&#39;</span><span class="p">,</span> sex<span class="o">=</span><span class="s">&#39;male&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;colour&quot;</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/organPlot-1.svg" alt="plot of chunk organPlot" /></p>
<p>We can use the ggplot themes and functions to adjust the plots</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>gganatogram<span class="p">(</span>data<span class="o">=</span>organPlot<span class="p">,</span> fillOutline<span class="o">=</span><span class="s">&#39;#a6bddb&#39;</span><span class="p">,</span> organism<span class="o">=</span><span class="s">&#39;human&#39;</span><span class="p">,</span> sex<span class="o">=</span><span class="s">&#39;male&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;colour&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_void<span class="p">()</span></code></pre></figure>
<p><img src="/assets/Rfig/organPlotvoid-1.svg" alt="plot of chunk organPlotvoid" /></p>
<p>We can also plot all tissues available using hgMale_key, which is an available object</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>hgMale_key<span class="o">$</span>organ</code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## [1] &quot;bone marrow&quot; &quot;frontal cortex&quot;
## [3] &quot;prefrontal cortex&quot; &quot;gastroesophageal junction&quot;
## [5] &quot;caecum&quot; &quot;ileum&quot;
## [7] &quot;rectum&quot; &quot;nose&quot;
## [9] &quot;tongue&quot; &quot;penis&quot;
## [11] &quot;nasal pharynx&quot; &quot;spinal cord&quot;
## [13] &quot;throat&quot; &quot;diaphragm&quot;
## [15] &quot;liver&quot; &quot;stomach&quot;
## [17] &quot;spleen&quot; &quot;duodenum&quot;
## [19] &quot;gall bladder&quot; &quot;pancreas&quot;
## [21] &quot;colon&quot; &quot;small intestine&quot;
## [23] &quot;appendix&quot; &quot;urinary bladder&quot;
## [25] &quot;bone&quot; &quot;cartilage&quot;
## [27] &quot;esophagus&quot; &quot;skin&quot;
## [29] &quot;brain&quot; &quot;heart&quot;
## [31] &quot;lymph_node&quot; &quot;skeletal_muscle&quot;
## [33] &quot;leukocyte&quot; &quot;temporal_lobe&quot;
## [35] &quot;atrial_appendage&quot; &quot;coronary_artery&quot;
## [37] &quot;hippocampus&quot; &quot;vas_deferens&quot;
## [39] &quot;seminal_vesicle&quot; &quot;epididymis&quot;
## [41] &quot;tonsil&quot; &quot;lung&quot;
## [43] &quot;trachea&quot; &quot;bronchus&quot;
## [45] &quot;nerve&quot; &quot;kidney&quot;</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>gganatogram<span class="p">(</span>data<span class="o">=</span>hgMale_key<span class="p">,</span> fillOutline<span class="o">=</span><span class="s">&#39;#a6bddb&#39;</span><span class="p">,</span> organism<span class="o">=</span><span class="s">&#39;human&#39;</span><span class="p">,</span> sex<span class="o">=</span><span class="s">&#39;male&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;colour&quot;</span><span class="p">)</span> <span class="o">+</span>theme_void<span class="p">()</span></code></pre></figure>
<p><img src="/assets/Rfig/organPlotAll-1.svg" alt="plot of chunk organPlotAll" /></p>
<p>To skip the outline of the graph, use outline=F</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>organPlot <span class="o">%&gt;%</span>
dplyr<span class="o">::</span>filter<span class="p">(</span>type <span class="o">%in%</span> <span class="kt">c</span><span class="p">(</span><span class="s">&#39;circulation&#39;</span><span class="p">,</span> <span class="s">&#39;nervous system&#39;</span><span class="p">))</span> <span class="o">%&gt;%</span>
gganatogram<span class="p">(</span>outline<span class="o">=</span><span class="bp">F</span><span class="p">,</span> fillOutline<span class="o">=</span><span class="s">&#39;#a6bddb&#39;</span><span class="p">,</span> organism<span class="o">=</span><span class="s">&#39;human&#39;</span><span class="p">,</span> sex<span class="o">=</span><span class="s">&#39;male&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;colour&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_void<span class="p">()</span></code></pre></figure>
<p><img src="/assets/Rfig/organPlotSubset-1.svg" alt="plot of chunk organPlotSubset" /></p>
<p>We can fill the tissues based on the values given to each organ</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>gganatogram<span class="p">(</span>data<span class="o">=</span>organPlot<span class="p">,</span> fillOutline<span class="o">=</span><span class="s">&#39;#a6bddb&#39;</span><span class="p">,</span> organism<span class="o">=</span><span class="s">&#39;human&#39;</span><span class="p">,</span> sex<span class="o">=</span><span class="s">&#39;male&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;value&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_void<span class="p">()</span> <span class="o">+</span>
scale_fill_gradient<span class="p">(</span>low <span class="o">=</span> <span class="s">&quot;white&quot;</span><span class="p">,</span> high <span class="o">=</span> <span class="s">&quot;red&quot;</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/organPlotValue-1.svg" alt="plot of chunk organPlotValue" /></p>
<p>We can also use facet_wrap to compare groups.<br />
First create add two data frames together with different values and the conditions in the type column</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>compareGroups <span class="o">&lt;-</span> <span class="kp">rbind</span><span class="p">(</span><span class="kt">data.frame</span><span class="p">(</span>organ <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;heart&quot;</span><span class="p">,</span> <span class="s">&quot;leukocyte&quot;</span><span class="p">,</span> <span class="s">&quot;nerve&quot;</span><span class="p">,</span> <span class="s">&quot;brain&quot;</span><span class="p">,</span> <span class="s">&quot;liver&quot;</span><span class="p">,</span> <span class="s">&quot;stomach&quot;</span><span class="p">,</span> <span class="s">&quot;colon&quot;</span><span class="p">),</span>
colour <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;red&quot;</span><span class="p">,</span> <span class="s">&quot;red&quot;</span><span class="p">,</span> <span class="s">&quot;purple&quot;</span><span class="p">,</span> <span class="s">&quot;purple&quot;</span><span class="p">,</span> <span class="s">&quot;orange&quot;</span><span class="p">,</span> <span class="s">&quot;orange&quot;</span><span class="p">,</span> <span class="s">&quot;orange&quot;</span><span class="p">),</span>
value <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="m">10</span><span class="p">,</span> <span class="m">5</span><span class="p">,</span> <span class="m">1</span><span class="p">,</span> <span class="m">8</span><span class="p">,</span> <span class="m">2</span><span class="p">,</span> <span class="m">5</span><span class="p">,</span> <span class="m">5</span><span class="p">),</span>
type <span class="o">=</span> <span class="kp">rep</span><span class="p">(</span><span class="s">&#39;Normal&#39;</span><span class="p">,</span> <span class="m">7</span><span class="p">),</span>
stringsAsFactors<span class="o">=</span><span class="bp">F</span><span class="p">),</span>
<span class="kt">data.frame</span><span class="p">(</span>organ <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;heart&quot;</span><span class="p">,</span> <span class="s">&quot;leukocyte&quot;</span><span class="p">,</span> <span class="s">&quot;nerve&quot;</span><span class="p">,</span> <span class="s">&quot;brain&quot;</span><span class="p">,</span> <span class="s">&quot;liver&quot;</span><span class="p">,</span> <span class="s">&quot;stomach&quot;</span><span class="p">,</span> <span class="s">&quot;colon&quot;</span><span class="p">),</span>
colour <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;red&quot;</span><span class="p">,</span> <span class="s">&quot;red&quot;</span><span class="p">,</span> <span class="s">&quot;purple&quot;</span><span class="p">,</span> <span class="s">&quot;purple&quot;</span><span class="p">,</span> <span class="s">&quot;orange&quot;</span><span class="p">,</span> <span class="s">&quot;orange&quot;</span><span class="p">,</span> <span class="s">&quot;orange&quot;</span><span class="p">),</span>
value <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="m">5</span><span class="p">,</span> <span class="m">5</span><span class="p">,</span> <span class="m">10</span><span class="p">,</span> <span class="m">8</span><span class="p">,</span> <span class="m">2</span><span class="p">,</span> <span class="m">5</span><span class="p">,</span> <span class="m">5</span><span class="p">),</span>
type <span class="o">=</span> <span class="kp">rep</span><span class="p">(</span><span class="s">&#39;Cancer&#39;</span><span class="p">,</span> <span class="m">7</span><span class="p">),</span>
stringsAsFactors<span class="o">=</span><span class="bp">F</span><span class="p">))</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>gganatogram<span class="p">(</span>data<span class="o">=</span>compareGroups<span class="p">,</span> fillOutline<span class="o">=</span><span class="s">&#39;#a6bddb&#39;</span><span class="p">,</span> organism<span class="o">=</span><span class="s">&#39;human&#39;</span><span class="p">,</span> sex<span class="o">=</span><span class="s">&#39;male&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;value&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_void<span class="p">()</span> <span class="o">+</span>
facet_wrap<span class="p">(</span><span class="o">~</span>type<span class="p">)</span> <span class="o">+</span>
scale_fill_gradient<span class="p">(</span>low <span class="o">=</span> <span class="s">&quot;white&quot;</span><span class="p">,</span> high <span class="o">=</span> <span class="s">&quot;red&quot;</span><span class="p">)</span> </code></pre></figure>
<p><img src="/assets/Rfig/Condition-1.svg" alt="plot of chunk Condition" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>gganatogram<span class="p">(</span>data<span class="o">=</span>hgMale_key<span class="p">,</span> fillOutline<span class="o">=</span><span class="s">&#39;#a6bddb&#39;</span><span class="p">,</span> organism<span class="o">=</span><span class="s">&#39;human&#39;</span><span class="p">,</span> sex<span class="o">=</span><span class="s">&#39;male&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;colour&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_void<span class="p">()</span> <span class="o">+</span>
facet_wrap<span class="p">(</span><span class="o">~</span>type<span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/organPlotAllWrap-1.svg" alt="plot of chunk organPlotAllWrap" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>gganatogram<span class="p">(</span>data<span class="o">=</span>hgMale_key<span class="p">,</span> outline<span class="o">=</span><span class="bp">F</span><span class="p">,</span> fillOutline<span class="o">=</span><span class="s">&#39;#a6bddb&#39;</span><span class="p">,</span> organism<span class="o">=</span><span class="s">&#39;human&#39;</span><span class="p">,</span> sex<span class="o">=</span><span class="s">&#39;male&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;colour&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_void<span class="p">()</span> <span class="o">+</span>
facet_wrap<span class="p">(</span><span class="o">~</span>type<span class="p">,</span> scale<span class="o">=</span><span class="s">&#39;free&#39;</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/organPlotAllWrapFree-1.svg" alt="plot of chunk organPlotAllWrapFree" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>organtype <span class="o">&lt;-</span> organPlot
organtype <span class="o">%&gt;%</span>
mutate<span class="p">(</span>type<span class="o">=</span>organ<span class="p">)</span> <span class="o">%&gt;%</span>
gganatogram<span class="p">(</span> outline<span class="o">=</span><span class="bp">F</span><span class="p">,</span> fillOutline<span class="o">=</span><span class="s">&#39;#a6bddb&#39;</span><span class="p">,</span> organism<span class="o">=</span><span class="s">&#39;human&#39;</span><span class="p">,</span> sex<span class="o">=</span><span class="s">&#39;male&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;colour&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_void<span class="p">()</span> <span class="o">+</span>
facet_wrap<span class="p">(</span><span class="o">~</span>type<span class="p">,</span> scale<span class="o">=</span><span class="s">&#39;free&#39;</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/organPlotWrapFree-1.svg" alt="plot of chunk organPlotWrapFree" /></p>
</description>
<pubDate>Sun, 09 Sep 2018 00:00:00 -0400</pubDate>
<link>http://jespermaag.github.io/blog/2018/gganatogram/</link>
<guid isPermaLink="true">http://jespermaag.github.io/blog/2018/gganatogram/</guid>
<category>jekyll</category>
<category>update</category>
</item>
<item>
<title>Single cell RNA-seq (ES mouse) using Seurat</title>
<description><h2 id="in-this-post-i-will-analyze-mouse-es-cell-single-cell-rna-seq-data-using-seurat">In this post I will analyze mouse ES-cell single cell RNA-seq data using Seurat.</h2>
<p>Data was downlaoded from https://github.com/debsin/dropClust</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kn">library</span><span class="p">(</span>RCurl<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>Seurat<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>cellrangerRkit<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>data.table<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>tidyverse<span class="p">)</span></code></pre></figure>
<p>This post also follows examples from https://davetang.org/muse/2017/08/01/getting-started-seurat/
First we remove all genes that are expressed in less than 3 cells.</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>esMus <span class="o">&lt;-</span> Read10X<span class="p">(</span><span class="kt">c</span><span class="p">(</span><span class="s">&#39;~/github/jespermaag.github.io/es_mouse/&#39;</span><span class="p">))</span>
<span class="kp">dim</span><span class="p">(</span>esMus<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## [1] 24175 2717</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>keep <span class="o">&lt;-</span> <span class="kp">apply</span><span class="p">(</span>esMus<span class="p">,</span> <span class="m">1</span><span class="p">,</span> <span class="kr">function</span><span class="p">(</span>x<span class="p">)</span> <span class="kp">sum</span><span class="p">(</span>x<span class="o">&gt;</span><span class="m">0</span><span class="p">))</span>
<span class="kp">table</span><span class="p">(</span>keep<span class="o">&gt;=</span><span class="m">3</span><span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>##
## FALSE TRUE
## 153 24022</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>keep <span class="o">&lt;-</span> keep <span class="o">&gt;=</span> <span class="m">3</span>
esMus <span class="o">&lt;-</span> esMus<span class="p">[</span>keep<span class="p">,]</span>
<span class="kp">summary</span><span class="p">(</span><span class="kp">colSums</span><span class="p">(</span>esMus<span class="p">))</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1647 6263 17373 20033 31086 85686</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>genes <span class="o">&lt;-</span> <span class="kp">apply</span><span class="p">(</span>esMus<span class="p">,</span> <span class="m">2</span><span class="p">,</span> <span class="kr">function</span><span class="p">(</span>x<span class="p">)</span> <span class="kp">sum</span><span class="p">(</span>x<span class="o">&gt;</span><span class="m">0</span><span class="p">))</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>genes <span class="o">%&gt;%</span>
melt<span class="p">()</span> <span class="o">%&gt;%</span>
ggplot<span class="p">(</span>aes<span class="p">(</span>x<span class="o">=</span>value<span class="p">))</span> <span class="o">+</span>
geom_histogram<span class="p">(</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;steelblue&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_classic<span class="p">()</span><span class="o">+</span>
xlab<span class="p">(</span><span class="s">&#39;Number genes (1&gt;= counts)&#39;</span><span class="p">)</span> <span class="o">+</span>
ggtitle<span class="p">(</span><span class="s">&#39;Number of genes detected per cell&#39;</span><span class="p">)</span><span class="o">+</span>
theme<span class="p">(</span> axis.title.x <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">14</span><span class="p">),</span> axis.text.x <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">12</span><span class="p">,</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">),</span>
axis.text.y <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">12</span><span class="p">,</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">),</span> axis.title.y <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">14</span><span class="p">))</span></code></pre></figure>
<p><img src="/assets/Rfig/explorePlots-1.svg" alt="plot of chunk explorePlots" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kp">colSums</span><span class="p">(</span>esMus<span class="p">)</span> <span class="o">%&gt;%</span>
melt<span class="p">()</span> <span class="o">%&gt;%</span>
ggplot<span class="p">(</span>aes<span class="p">(</span>x<span class="o">=</span>value<span class="p">))</span> <span class="o">+</span>
geom_histogram<span class="p">(</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;steelblue&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_classic<span class="p">()</span><span class="o">+</span>
xlab<span class="p">(</span><span class="s">&#39;Counts per cell)&#39;</span><span class="p">)</span> <span class="o">+</span>
ggtitle<span class="p">(</span><span class="s">&#39;Total counts per cell&#39;</span><span class="p">)</span><span class="o">+</span>
theme<span class="p">(</span> axis.title.x <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">14</span><span class="p">),</span> axis.text.x <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">12</span><span class="p">,</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">),</span>
axis.text.y <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">12</span><span class="p">,</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">),</span> axis.title.y <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">14</span><span class="p">))</span></code></pre></figure>
<p><img src="/assets/Rfig/explorePlots-2.svg" alt="plot of chunk explorePlots" /></p>
<p>As seen in the number of gene plots, there is a binomial distribution of number of genes per cell.
I will keep this in mind for further analysis.
The binomial distribtion is missing from the total counts per cell.</p>
<p>To use Seurat, I first have to create a Seurat object</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>esMusSeur <span class="o">&lt;-</span> CreateSeuratObject<span class="p">(</span>raw.data <span class="o">=</span> esMus<span class="p">,</span>
min.cells <span class="o">=</span> <span class="m">3</span><span class="p">,</span>
min.genes <span class="o">=</span> <span class="m">100</span><span class="p">,</span>
project <span class="o">=</span> <span class="s">&quot;ES_mouse&quot;</span><span class="p">)</span>
esMusSeur</code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## An object of class seurat in project ES_mouse
## 24022 genes across 2717 samples.</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>slotNames<span class="p">(</span>esMusSeur<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## [1] &quot;raw.data&quot; &quot;data&quot; &quot;scale.data&quot; &quot;var.genes&quot;
## [5] &quot;is.expr&quot; &quot;ident&quot; &quot;meta.data&quot; &quot;project.name&quot;
## [9] &quot;dr&quot; &quot;assay&quot; &quot;hvg.info&quot; &quot;imputed&quot;
## [13] &quot;cell.names&quot; &quot;cluster.tree&quot; &quot;snn&quot; &quot;calc.params&quot;
## [17] &quot;kmeans&quot; &quot;spatial&quot; &quot;misc&quot; &quot;version&quot;</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>esMusSeur <span class="o">&lt;-</span> NormalizeData<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span>
normalization.method <span class="o">=</span> <span class="s">&quot;LogNormalize&quot;</span><span class="p">,</span>
scale.factor <span class="o">=</span> <span class="m">1e4</span><span class="p">)</span>
<span class="kp">colSums</span><span class="p">(</span>esMusSeur<span class="o">@</span>data<span class="p">)</span> <span class="o">%&gt;%</span>
melt<span class="p">()</span> <span class="o">%&gt;%</span>
ggplot<span class="p">(</span>aes<span class="p">(</span>x<span class="o">=</span>value<span class="p">))</span> <span class="o">+</span>
geom_histogram<span class="p">(</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&quot;steelblue&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_classic<span class="p">()</span><span class="o">+</span>
xlab<span class="p">(</span><span class="s">&#39;Normalised counts per cell&#39;</span><span class="p">)</span> <span class="o">+</span>
ggtitle<span class="p">(</span><span class="s">&#39;Total counts per cell&#39;</span><span class="p">)</span><span class="o">+</span>
theme<span class="p">(</span> axis.title.x <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">14</span><span class="p">),</span> axis.text.x <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">12</span><span class="p">,</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">),</span>
axis.text.y <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">12</span><span class="p">,</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">),</span> axis.title.y <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">14</span><span class="p">))</span></code></pre></figure>
<p><img src="/assets/Rfig/createSeurat-1.svg" alt="plot of chunk createSeurat" />
As seen in the previous figure, after normalised the binomial distribution appears for the total counts.</p>
<p>Below I want to find the variable genes between the cells and plot the PCA.
Since this is my first single cell analysis, I will use the parameters from https://davetang.org/muse/2017/08/01/getting-started-seurat/</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>esMusSeur <span class="o">&lt;-</span> FindVariableGenes<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span>
mean.function <span class="o">=</span> ExpMean<span class="p">,</span>
dispersion.function <span class="o">=</span> LogVMR<span class="p">,</span>
x.low.cutoff <span class="o">=</span> <span class="m">0.0125</span><span class="p">,</span>
x.high.cutoff <span class="o">=</span> <span class="m">3</span><span class="p">,</span>
y.cutoff <span class="o">=</span> <span class="m">0.5</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/variability-1.svg" alt="plot of chunk variability" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kp">length</span><span class="p">(</span>esMusSeur<span class="o">@</span>var.genes<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## [1] 5707</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kp">head</span><span class="p">(</span>esMusSeur<span class="o">@</span>hvg.info<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## gene.mean gene.dispersion gene.dispersion.scaled
## Gm7102 0.5832131 5.417292 16.151102
## Dcdc2c 0.4870505 5.006380 18.885776
## Rn28s1 2.9695050 4.793843 2.929660
## Rn4.5s 2.7443443 4.607135 3.618150
## Rn45s 3.6240987 4.024695 2.001075
## Ccdc36 3.3616208 3.766651 1.659102</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>esMusSeur<span class="o">@</span>hvg.info <span class="o">%&gt;%</span>
ggplot<span class="p">(</span>aes<span class="p">(</span> x<span class="o">=</span> gene.mean<span class="p">,</span> y <span class="o">=</span> gene.dispersion.scaled<span class="p">))</span> <span class="o">+</span>
geom_point<span class="p">(</span>pch<span class="o">=</span><span class="m">21</span><span class="p">,</span> fill<span class="o">=</span><span class="s">&#39;steelblue&#39;</span><span class="p">)</span> <span class="o">+</span>
theme_classic<span class="p">()</span><span class="o">+</span>
xlab<span class="p">(</span><span class="s">&#39;Mean gene expression&#39;</span><span class="p">)</span> <span class="o">+</span>
ylab<span class="p">(</span><span class="s">&#39;scaled dispersion&#39;</span><span class="p">)</span> <span class="o">+</span>
ggtitle<span class="p">(</span><span class="s">&#39;Mean expression vs dispersion&#39;</span><span class="p">)</span><span class="o">+</span>
theme<span class="p">(</span> axis.title.x <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">14</span><span class="p">),</span> axis.text.x <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">12</span><span class="p">,</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">),</span>
axis.text.y <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">12</span><span class="p">,</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">),</span> axis.title.y <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">14</span><span class="p">))</span><span class="o">+</span>
geom_smooth<span class="p">()</span></code></pre></figure>
<p><img src="/assets/Rfig/variability-2.svg" alt="plot of chunk variability" /></p>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Regressing out: nUMI</code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Scaling data matrix</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>esMusSeur <span class="o">&lt;-</span> RunPCA<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span>
pc.genes <span class="o">=</span> esMusSeur<span class="o">@</span>var.genes<span class="p">,</span>
do.print <span class="o">=</span> <span class="kc">TRUE</span><span class="p">,</span>
pcs.print <span class="o">=</span> <span class="m">1</span><span class="o">:</span><span class="m">5</span><span class="p">,</span>
genes.print <span class="o">=</span> <span class="m">5</span><span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## [1] &quot;PC1&quot;
## [1] &quot;Krt8&quot; &quot;Krt18&quot; &quot;S100a6&quot; &quot;Gsn&quot; &quot;Lgals1&quot;
## [1] &quot;&quot;
## [1] &quot;Npm1&quot; &quot;Dppa5a&quot; &quot;Ptma&quot; &quot;Nlrp1a&quot; &quot;Rpl21&quot;
## [1] &quot;&quot;
## [1] &quot;&quot;
## [1] &quot;PC2&quot;
## [1] &quot;Lrrc58&quot; &quot;Tpm1&quot; &quot;Tagln&quot; &quot;Eno1b&quot; &quot;Eno1&quot;
## [1] &quot;&quot;
## [1] &quot;Tdh&quot; &quot;Mir690&quot; &quot;Esrrb&quot; &quot;Zfp42&quot; &quot;Slc29a1&quot;
## [1] &quot;&quot;
## [1] &quot;&quot;
## [1] &quot;PC3&quot;
## [1] &quot;Gm15772&quot; &quot;Mt2&quot; &quot;Esrrb&quot; &quot;Tdh&quot; &quot;Hmces&quot;
## [1] &quot;&quot;
## [1] &quot;Acsf2&quot; &quot;Ppm1k&quot; &quot;Gm10941&quot; &quot;Agl&quot; &quot;Mtfmt&quot;
## [1] &quot;&quot;
## [1] &quot;&quot;
## [1] &quot;PC4&quot;
## [1] &quot;Klf5&quot; &quot;Malat1&quot; &quot;Klf6&quot; &quot;Slc2a3&quot; &quot;Sparc&quot;
## [1] &quot;&quot;
## [1] &quot;Il12a&quot; &quot;Gm20740&quot; &quot;Olfr954&quot; &quot;Fam229b&quot; &quot;Tmem126b&quot;
## [1] &quot;&quot;
## [1] &quot;&quot;
## [1] &quot;PC5&quot;
## [1] &quot;Gapdh&quot; &quot;Gpx4&quot; &quot;Gm8709&quot; &quot;Dusp9&quot; &quot;Gsn&quot;
## [1] &quot;&quot;
## [1] &quot;Erdr1&quot; &quot;Hist1h2ao&quot; &quot;Apoe&quot; &quot;Hist1h2ap&quot; &quot;Flnb&quot;
## [1] &quot;&quot;
## [1] &quot;&quot;</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>PrintPCAParams<span class="p">(</span>esMusSeur<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Parameters used in latest PCA calculation run on: 2018-08-11 14:46:38
## =============================================================================
## PCs computed Genes used in calculation PCs Scaled by Variance Explained
## 20 5707 TRUE
## -----------------------------------------------------------------------------
## rev.pca
## FALSE
## -----------------------------------------------------------------------------
## Full gene list can be accessed using
## GetCalcParam(object = object, calculation = &quot;RunPCA&quot;, parameter = &quot;pc.genes&quot;)</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>VizPCA<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span> pcs.use <span class="o">=</span> <span class="m">1</span><span class="o">:</span><span class="m">2</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/PCA-1.svg" alt="plot of chunk PCA" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>PCAPlot<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span> dim.1 <span class="o">=</span> <span class="m">1</span><span class="p">,</span> dim.2 <span class="o">=</span> <span class="m">2</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/PCA-2.svg" alt="plot of chunk PCA" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>esMusSeur <span class="o">&lt;-</span> ProjectPCA<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span> do.print <span class="o">=</span> <span class="kc">FALSE</span><span class="p">)</span></code></pre></figure>
<p>By plotting the standard deviation for each principal compartment, I can see that the deviation stop at around the 12th compartment.</p>
<p>K-nearest neighbour finds the number of groups in the data set, which is used to colour the data in the tSNE-plot</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>PCElbowPlot<span class="p">(</span>esMusSeur<span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/subsetPCA-1.svg" alt="plot of chunk subsetPCA" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>esMusSeur <span class="o">&lt;-</span> FindClusters<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span>
reduction.type <span class="o">=</span> <span class="s">&quot;pca&quot;</span><span class="p">,</span>
dims.use <span class="o">=</span> <span class="m">1</span><span class="o">:</span><span class="m">10</span><span class="p">,</span>
resolution <span class="o">=</span> <span class="m">0.6</span><span class="p">,</span>
print.output <span class="o">=</span> <span class="m">0</span><span class="p">,</span>
save.SNN <span class="o">=</span> <span class="kc">TRUE</span><span class="p">)</span>
esMusSeur <span class="o">&lt;-</span> FindClusters<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span>
reduction.type <span class="o">=</span> <span class="s">&quot;pca&quot;</span><span class="p">,</span>
dims.use <span class="o">=</span> <span class="m">1</span><span class="o">:</span><span class="m">12</span><span class="p">,</span>
resolution <span class="o">=</span> <span class="m">0.6</span><span class="p">,</span>
print.output <span class="o">=</span> <span class="m">0</span><span class="p">,</span>
save.SNN <span class="o">=</span> <span class="kc">TRUE</span><span class="p">)</span>
esMusSeur <span class="o">&lt;-</span> RunTSNE<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span>
dims.use <span class="o">=</span> <span class="m">1</span><span class="o">:</span><span class="m">12</span><span class="p">,</span>
do.fast <span class="o">=</span> <span class="kc">TRUE</span><span class="p">)</span>
TSNEPlot<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span> do.label <span class="o">=</span> <span class="kc">TRUE</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/subsetPCA-2.svg" alt="plot of chunk subsetPCA" /></p>
<p>I then want to identify the top genes per cluster.</p>
<p>The top genes can then be plotted as violin plots, feature plots, which projects the expression on the tSNE-plot, or as heatmaps for the top 5 highest expressed genes per cluster.</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>topMarkers <span class="o">&lt;-</span> esMusSeur.markers <span class="o">%&gt;%</span>
group_by<span class="p">(</span>cluster<span class="p">)</span> <span class="o">%&gt;%</span>
top_n<span class="p">(</span><span class="m">1</span><span class="p">,</span> avg_logFC<span class="p">)</span>
VlnPlot<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span> features.plot <span class="o">=</span> topMarkers<span class="o">$</span>gene<span class="p">,</span> point.size.use <span class="o">=</span> <span class="m">0</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/topGenes-1.svg" alt="plot of chunk topGenes" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>FeaturePlot<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span>
features.plot <span class="o">=</span> topMarkers<span class="o">$</span>gene<span class="p">,</span>
cols.use <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;grey&quot;</span><span class="p">,</span> <span class="s">&quot;blue&quot;</span><span class="p">),</span>
reduction.use <span class="o">=</span> <span class="s">&quot;tsne&quot;</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/topGenes-2.svg" alt="plot of chunk topGenes" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>topMarkers5 <span class="o">&lt;-</span> esMusSeur.markers <span class="o">%&gt;%</span>
group_by<span class="p">(</span>cluster<span class="p">)</span> <span class="o">%&gt;%</span>
top_n<span class="p">(</span><span class="m">5</span><span class="p">,</span> avg_logFC<span class="p">)</span>
DoHeatmap<span class="p">(</span>object <span class="o">=</span> esMusSeur<span class="p">,</span>
genes.use <span class="o">=</span> topMarkers5<span class="o">$</span>gene<span class="p">,</span>
remove.key <span class="o">=</span> <span class="kc">TRUE</span><span class="p">,</span>
slim.col.label <span class="o">=</span> <span class="kc">TRUE</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/topGenes-3.svg" alt="plot of chunk topGenes" /></p>
<h3 id="there-you-go-my-first-single-cell-analysis-using-seurat-and-following-the-examples-from-dave-tang">There you go. My first single cell analysis using Seurat, and following the examples from Dave Tang</h3>
</description>
<pubDate>Sun, 25 Mar 2018 00:00:00 -0400</pubDate>
<link>http://jespermaag.github.io/blog/2018/SingleCellESmouse/</link>
<guid isPermaLink="true">http://jespermaag.github.io/blog/2018/SingleCellESmouse/</guid>
<category>jekyll</category>
<category>update</category>
</item>
<item>
<title>World bank health data</title>
<description>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kn">library</span><span class="p">(</span>data.table<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>tidyverse<span class="p">)</span></code></pre></figure>
<p>Data has been downloaded and is called data.csv.
https://www.kaggle.com/theworldbank/health-nutrition-and-population-statistics/data</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>data <span class="o">&lt;-</span> fread<span class="p">(</span><span class="s">&#39;~/Downloads/data.csv&#39;</span><span class="p">,</span> fill<span class="o">=</span><span class="bp">T</span><span class="p">,</span> stringsAsFactors<span class="o">=</span><span class="bp">F</span><span class="p">)</span>
<span class="kp">dim</span><span class="p">(</span>data<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## [1] 89010 61</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kp">head</span><span class="p">(</span>data<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Country Name Country Code
## 1: Arab World ARB
## 2: Arab World ARB
## 3: Arab World ARB
## 4: Arab World ARB
## 5: Arab World ARB
## 6: Arab World ARB
## Indicator Name
## 1: % of females ages 15-49 having comprehensive correct knowledge about HIV (2 prevent ways and reject 3 misconceptions)
## 2: % of males ages 15-49 having comprehensive correct knowledge about HIV (2 prevent ways and reject 3 misconceptions)
## 3: Adolescent fertility rate (births per 1,000 women ages 15-19)
## 4: Adults (ages 15+) and children (0-14 years) living with HIV
## 5: Adults (ages 15+) and children (ages 0-14) newly infected with HIV
## 6: Adults (ages 15+) living with HIV
## Indicator Code 1960 1961 1962
## 1: SH.HIV.KNOW.FE.ZS
## 2: SH.HIV.KNOW.MA.ZS
## 3: SP.ADO.TFRT 133.55501327769 134.159118941963 134.857912280869
## 4: SH.HIV.TOTL
## 5: SH.HIV.INCD.TL
## 6: SH.DYN.AIDS
## 1963 1964 1965 1966
## 1:
## 2:
## 3: 134.504575565342 134.105211273476 133.56962589645 132.675635192775
## 4:
## 5:
## 6:
## 1967 1968 1969 1970
## 1:
## 2:
## 3: 131.665502129354 129.190980115918 126.736756382819 124.382808900193
## 4:
## 5:
## 6:
## 1971 1972 1973 1974
## 1:
## 2:
## 3: 122.133431342027 120.020185557559 118.087531093609 116.132988067096
## 4:
## 5:
## 6:
## 1975 1976 1977 1978
## 1:
## 2:
## 3: 114.100918174437 111.980005447216 109.783821762662 106.033489239906
## 4:
## 5:
## 6:
## 1979 1980 1981 1982
## 1:
## 2:
## 3: 102.341720681455 98.7390023274647 95.2412508672802 91.7911923993221
## 4:
## 5:
## 6:
## 1983 1984 1985 1986
## 1:
## 2:
## 3: 88.0011769487606 84.2072557839419 80.3593225600132 76.4415956498419
## 4:
## 5:
## 6:
## 1987 1988 1989 1990
## 1:
## 2:
## 3: 72.5145803648751 71.1706639452677 69.8887679924858 69.0044133814268
## 4:
## 5:
## 6:
## 1991 1992 1993 1994
## 1:
## 2:
## 3: 67.7559924352118 66.9284506867798 64.9489678572737 62.9227777228154
## 4:
## 5:
## 6:
## 1995 1996 1997 1998
## 1:
## 2:
## 3: 60.7070695260477 58.5966308804751 56.4401276304142 55.5315395528949
## 4:
## 5:
## 6:
## 1999 2000 2001 2002
## 1:
## 2:
## 3: 54.6587808352011 53.8314102398679 52.9015276443892 51.9907926813042
## 4:
## 5:
## 6:
## 2003 2004 2005 2006
## 1:
## 2:
## 3: 51.5228563035101 51.1032496482833 50.7325902239383 50.3291352282938
## 4:
## 5:
## 6:
## 2007 2008 2009 2010
## 1:
## 2:
## 3: 49.9998514069402 49.8870459355469 49.7812066054555 49.6729747116906
## 4:
## 5:
## 6:
## 2011 2012 2013 2014
## 1:
## 2:
## 3: 49.5360469363113 49.3837446924523 48.7965576984378 48.1964180547578
## 4:
## 5:
## 6:
## 2015 V61
## 1: NA
## 2: NA
## 3: NA
## 4: NA
## 5: NA
## 6: NA</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kp">colnames</span><span class="p">(</span>data<span class="p">)</span> <span class="o">&lt;-</span> <span class="kp">gsub</span><span class="p">(</span><span class="s">&quot; &quot;</span><span class="p">,</span> <span class="s">&quot;_&quot;</span><span class="p">,</span> <span class="kp">colnames</span><span class="p">(</span>data<span class="p">))</span></code></pre></figure>
<p>Dataset consists of countries and larger regions.
First I want to investigate the differences in life expectancy per region.</p>
<p>We see that different regions have different life expectancy for both female and male.
The survival of men is always lower comapred to females.</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>regions <span class="o">&lt;-</span> data<span class="p">[</span>data<span class="o">$</span>Country_Name <span class="o">%in%</span> <span class="kp">unique</span><span class="p">(</span>data<span class="o">$</span>Country_Name<span class="p">)[</span><span class="m">1</span><span class="o">:</span><span class="m">41</span><span class="p">],]</span>
regions2014 <span class="o">&lt;-</span> regions<span class="p">[,</span><span class="kt">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">4</span><span class="p">,</span> <span class="m">59</span><span class="p">)]</span>
regions2014 <span class="o">%&gt;%</span>
filter<span class="p">(</span>Indicator_Name <span class="o">==</span> <span class="s">&quot;Life expectancy at birth, female (years)&quot;</span> <span class="o">|</span>
Indicator_Name <span class="o">==</span> <span class="s">&quot;Life expectancy at birth, male (years)&quot;</span> <span class="p">)</span> <span class="o">%&gt;%</span>
melt<span class="p">(</span>id<span class="o">=</span><span class="kt">c</span><span class="p">(</span><span class="m">1</span><span class="o">:</span><span class="m">4</span><span class="p">))</span> <span class="o">%&gt;%</span>
mutate<span class="p">(</span>Sex <span class="o">=</span> <span class="kp">gsub</span><span class="p">(</span><span class="s">&quot;Life expectancy at birth, |\\(years\\)&quot;</span><span class="p">,</span> <span class="s">&quot;&quot;</span><span class="p">,</span> Indicator_Name<span class="p">))</span> <span class="o">%&gt;%</span>
arrange<span class="p">(</span>value<span class="p">)</span> <span class="o">%&gt;%</span>
filter<span class="p">(</span><span class="o">!</span><span class="kp">grepl</span><span class="p">(</span><span class="s">&#39;income|&amp;|dividend|small|Small|area|UN|conflict|poor&#39;</span><span class="p">,</span> Country_Name<span class="p">))</span> <span class="o">%&gt;%</span>
mutate<span class="p">(</span>Country_Name <span class="o">=</span> <span class="kp">factor</span><span class="p">(</span>Country_Name<span class="p">,</span> levels<span class="o">=</span><span class="kp">unique</span><span class="p">(</span>Country_Name<span class="p">)))</span> <span class="o">%&gt;%</span>
ggplot<span class="p">(</span>aes<span class="p">(</span>x <span class="o">=</span> Country_Name<span class="p">,</span> y <span class="o">=</span> <span class="kp">as.numeric</span><span class="p">(</span>value<span class="p">),</span> fill<span class="o">=</span>Sex<span class="p">,</span> group<span class="o">=</span>Country_Name<span class="p">))</span> <span class="o">+</span>
coord_flip<span class="p">()</span><span class="o">+</span>
ylim<span class="p">(</span><span class="m">0</span><span class="p">,</span><span class="m">90</span><span class="p">)</span><span class="o">+</span>
<span class="c1">#geom_segment(aes(x = 0, x = Country_Name, xend = value), color = &quot;grey50&quot;) +</span>
geom_line<span class="p">()</span> <span class="o">+</span>
geom_point<span class="p">(</span>pch<span class="o">=</span><span class="m">21</span><span class="p">,</span> size<span class="o">=</span><span class="m">3</span><span class="p">)</span> <span class="o">+</span>
scale_fill_brewer<span class="p">(</span>palette<span class="o">=</span><span class="s">&quot;Set1&quot;</span><span class="p">)</span> <span class="o">+</span>
<span class="c1">#scale_fill_manual(values=c(&#39;deepskyblue&#39;,&#39;darkorange2&#39;)) +</span>
theme_classic<span class="p">()</span> <span class="o">+</span>
theme<span class="p">(</span> axis.title.x <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">14</span><span class="p">),</span> axis.text.x <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">12</span><span class="p">,</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">),</span>
axis.text.y <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">12</span><span class="p">,</span>colour<span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">),</span> axis.title.y <span class="o">=</span> element_text<span class="p">(</span>size<span class="o">=</span><span class="m">14</span><span class="p">))</span><span class="o">+</span>
ggtitle<span class="p">(</span><span class="s">&#39;Life expetancy per region&#39;</span><span class="p">)</span> <span class="o">+</span>
xlab<span class="p">(</span><span class="s">&#39;Region&#39;</span><span class="p">)</span> <span class="o">+</span>
ylab<span class="p">(</span><span class="s">&#39;Life expectancy from birth (years)&#39;</span><span class="p">)</span> </code></pre></figure>
<p><img src="/assets/Rfig/Female_lifeExpectancy-1.png" alt="plot of chunk Female_lifeExpectancy" /></p>
<h2 id="further-analysis-will-investigate-these-differneces">Further analysis will investigate these differneces</h2>
</description>
<pubDate>Sun, 18 Mar 2018 00:00:00 -0400</pubDate>
<link>http://jespermaag.github.io/blog/2018/WorldBankHealth/</link>
<guid isPermaLink="true">http://jespermaag.github.io/blog/2018/WorldBankHealth/</guid>
<category>jekyll</category>
<category>update</category>
</item>
<item>
<title>Bayesian regression of heart data</title>
<description><p>This post tries to use Bayesian linear regression to explore
causes of heart disease.</p>
<h2 id="bayesian-linear-regression">Bayesian linear regression</h2>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kn">library</span><span class="p">(</span><span class="s">&quot;corrplot&quot;</span><span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>ncvreg<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>ipred<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>dplyr<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>ggplot2<span class="p">)</span>
<span class="kn">library</span><span class="p">(</span>RColorBrewer<span class="p">)</span>
<span class="c1">#data(dystrophy)</span>
data<span class="p">(</span>heart<span class="p">)</span>
<span class="kp">dim</span><span class="p">(</span>heart<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## [1] 462 10</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>str<span class="p">(</span>heart<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## &#39;data.frame&#39;: 462 obs. of 10 variables:
## $ sbp : int 160 144 118 170 134 132 142 114 114 132 ...
## $ tobacco : num 12 0.01 0.08 7.5 13.6 6.2 4.05 4.08 0 0 ...
## $ ldl : num 5.73 4.41 3.48 6.41 3.5 6.47 3.38 4.59 3.83 5.8 ...
## $ adiposity: num 23.1 28.6 32.3 38 27.8 ...
## $ famhist : num 1 0 1 1 1 1 0 1 1 1 ...
## $ typea : int 49 55 52 51 60 62 59 62 49 69 ...
## $ obesity : num 25.3 28.9 29.1 32 26 ...
## $ alcohol : num 97.2 2.06 3.81 24.26 57.34 ...
## $ age : int 52 63 46 58 49 45 38 58 29 53 ...
## $ chd : int 1 1 0 1 1 0 0 1 0 1 ...</code></pre></figure>
<p>##Omit NA’s and plot variables to see if there’s correlations</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>dat <span class="o">=</span> heart
Cor <span class="o">&lt;-</span> cor<span class="p">(</span>dat<span class="p">)</span>
corrplot<span class="p">(</span>Cor<span class="p">,</span> type<span class="o">=</span><span class="s">&quot;upper&quot;</span><span class="p">,</span> method<span class="o">=</span><span class="s">&quot;ellipse&quot;</span><span class="p">,</span> tl.pos<span class="o">=</span><span class="s">&quot;d&quot;</span><span class="p">)</span>
corrplot<span class="p">(</span>Cor<span class="p">,</span> type<span class="o">=</span><span class="s">&quot;lower&quot;</span><span class="p">,</span> method<span class="o">=</span><span class="s">&quot;number&quot;</span><span class="p">,</span> col<span class="o">=</span><span class="s">&quot;black&quot;</span><span class="p">,</span>
add<span class="o">=</span><span class="kc">TRUE</span><span class="p">,</span> diag<span class="o">=</span><span class="kc">FALSE</span><span class="p">,</span> tl.pos<span class="o">=</span><span class="s">&quot;n&quot;</span><span class="p">,</span> cl.pos<span class="o">=</span><span class="s">&quot;n&quot;</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/corrplot-1.svg" alt="plot of chunk corrplot" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>dat <span class="o">%&gt;%</span>
<span class="kp">as.data.frame</span><span class="p">()</span> <span class="o">%&gt;%</span>
select<span class="p">(</span><span class="o">-</span><span class="kt">c</span><span class="p">(</span>famhist<span class="p">))</span> <span class="o">%&gt;%</span>
melt<span class="p">()</span> <span class="o">%&gt;%</span>
ggplot<span class="p">(</span>aes<span class="p">(</span>x<span class="o">=</span>value<span class="p">,</span> fill<span class="o">=</span>variable<span class="p">))</span><span class="o">+</span>
geom_histogram<span class="p">(</span>colour<span class="o">=</span><span class="s">&quot;black&quot;</span><span class="p">,</span> size<span class="o">=</span><span class="m">0.1</span><span class="p">)</span> <span class="o">+</span>
facet_wrap<span class="p">(</span><span class="o">~</span>variable<span class="p">,</span> ncol<span class="o">=</span><span class="m">3</span><span class="p">,</span> scale<span class="o">=</span><span class="s">&quot;free&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_classic<span class="p">()</span> <span class="o">+</span>
scale_fill_brewer<span class="p">(</span>palette<span class="o">=</span><span class="s">&quot;Set1&quot;</span><span class="p">)</span><span class="o">+</span>
theme<span class="p">(</span>legend.position<span class="o">=</span><span class="s">&quot;none&quot;</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/corrplot-2.svg" alt="plot of chunk corrplot" /></p>
<p>#Divide the data into training and test set</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>chdYes <span class="o">=</span> heart<span class="p">[</span>heart<span class="o">$</span>chd<span class="o">==</span><span class="m">1</span><span class="p">,]</span>
chdNo <span class="o">=</span> heart<span class="p">[</span>heart<span class="o">$</span>chd<span class="o">==</span><span class="m">0</span><span class="p">,]</span>
sampleYes <span class="o">=</span> <span class="kp">sample</span><span class="p">(</span><span class="kp">rownames</span><span class="p">(</span>chdYes<span class="p">),</span> <span class="kp">nrow</span><span class="p">(</span>chdYes<span class="p">)</span><span class="o">*</span><span class="m">0.8</span><span class="p">)</span>
sampleNo <span class="o">=</span> <span class="kp">sample</span><span class="p">(</span><span class="kp">rownames</span><span class="p">(</span>chdNo<span class="p">),</span> <span class="kp">nrow</span><span class="p">(</span>chdNo<span class="p">)</span><span class="o">*</span><span class="m">0.8</span><span class="p">)</span>
trainYes <span class="o">=</span> chdYes<span class="p">[</span>sampleYes<span class="p">,]</span>
trainNo <span class="o">=</span> chdNo<span class="p">[</span>sampleNo<span class="p">,]</span>
testYes <span class="o">=</span> chdYes<span class="p">[</span><span class="o">!</span><span class="kp">rownames</span><span class="p">(</span>chdYes<span class="p">)</span> <span class="o">%in%</span> sampleYes<span class="p">,]</span>
testNo <span class="o">=</span> chdNo<span class="p">[</span><span class="o">!</span><span class="kp">rownames</span><span class="p">(</span>chdNo<span class="p">)</span> <span class="o">%in%</span> sampleNo<span class="p">,]</span>
dat <span class="o">=</span> <span class="kp">rbind</span><span class="p">(</span>trainYes<span class="p">,</span> trainNo<span class="p">)</span>
test <span class="o">=</span> <span class="kp">rbind</span><span class="p">(</span>testYes<span class="p">,</span> testNo<span class="p">)</span></code></pre></figure>
<p>###Model the data with JAGS
As we have centered the data around 0, the distribution of Beta will follow a double exponential distriubtion.</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>X <span class="o">=</span> <span class="kp">scale</span><span class="p">(</span>dat<span class="p">[,</span><span class="m">-10</span><span class="p">],</span> center<span class="o">=</span><span class="kc">TRUE</span><span class="p">,</span> scale<span class="o">=</span><span class="kc">TRUE</span><span class="p">)</span>
X <span class="o">%&gt;%</span>
<span class="kp">as.data.frame</span><span class="p">()</span> <span class="o">%&gt;%</span>
select<span class="p">(</span><span class="o">-</span><span class="kt">c</span><span class="p">(</span>famhist<span class="p">))</span> <span class="o">%&gt;%</span>
melt<span class="p">()</span> <span class="o">%&gt;%</span>
ggplot<span class="p">(</span>aes<span class="p">(</span>x<span class="o">=</span>value<span class="p">,</span> fill<span class="o">=</span>variable<span class="p">))</span><span class="o">+</span>
geom_histogram<span class="p">(</span>colour<span class="o">=</span><span class="s">&quot;black&quot;</span><span class="p">,</span> size<span class="o">=</span><span class="m">0.1</span><span class="p">)</span> <span class="o">+</span>
facet_wrap<span class="p">(</span><span class="o">~</span>variable<span class="p">,</span> ncol<span class="o">=</span><span class="m">3</span><span class="p">,</span> scale<span class="o">=</span><span class="s">&quot;free&quot;</span><span class="p">)</span> <span class="o">+</span>
theme_classic<span class="p">()</span> <span class="o">+</span>
scale_fill_brewer<span class="p">(</span>palette<span class="o">=</span><span class="s">&quot;Set1&quot;</span><span class="p">)</span><span class="o">+</span>
theme<span class="p">(</span>legend.position<span class="o">=</span><span class="s">&quot;none&quot;</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/Model_data-1.svg" alt="plot of chunk Model_data" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>mod_glm <span class="o">=</span> <span class="kp">summary</span><span class="p">(</span>glm<span class="p">(</span>chd <span class="o">~</span> <span class="m">.</span><span class="p">,</span> data<span class="o">=</span>dat<span class="p">))</span></code></pre></figure>
<p>#JAGS model</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kn">library</span><span class="p">(</span><span class="s">&quot;rjags&quot;</span><span class="p">)</span>
mod1_string <span class="o">=</span> <span class="s">&quot; model {</span>
<span class="s"> for (i in 1:length(y)) {</span>
<span class="s"> y[i] ~ dbern(p[i])</span>
<span class="s"> #logit(p[i]) = int + b[1]*AGE[i] + b[2]*CK[i] + b[3]*H[i] + b[4]*PK[i] + b[5]*LD[i]</span>
<span class="s"> logit(p[i]) = int + b[1]*sbp[i] + b[2]+tobacco[i] + b[3]*ldl[i] + </span>
<span class="s"> b[4]*adiposity[i] + b[5]*famhist[i] +b[6]*typea[i] +b[7]*obesity[i] + b[8]*alcohol[i] + b[9]*age[i]</span>
<span class="s"> }</span>
<span class="s"> int ~ dnorm(0.0, 1.0/25.0)</span>
<span class="s"> for (j in 1:9) {</span>
<span class="s"> b[j] ~ ddexp(0.0, sqrt(2.0)) # has variance 1.0</span>
<span class="s"> #b[j] ~ dnorm(0.0, 2) # noninformative for logistic regression</span>
<span class="s"> #b[j] ~ dnorm(0.0, 1.0/4.0^2)</span>
<span class="s"> }</span>
<span class="s">} &quot;</span>
data_jags <span class="o">=</span> <span class="kt">list</span><span class="p">(</span>y<span class="o">=</span>dat<span class="p">[,</span><span class="m">10</span><span class="p">],</span> sbp<span class="o">=</span>X<span class="p">[,</span><span class="s">&quot;sbp&quot;</span><span class="p">],</span> tobacco<span class="o">=</span>X<span class="p">[,</span><span class="s">&quot;tobacco&quot;</span><span class="p">],</span> ldl<span class="o">=</span>X<span class="p">[,</span><span class="s">&quot;ldl&quot;</span><span class="p">],</span>
adiposity<span class="o">=</span>X<span class="p">[,</span><span class="s">&quot;adiposity&quot;</span><span class="p">],</span> famhist<span class="o">=</span>X<span class="p">[,</span><span class="s">&quot;famhist&quot;</span><span class="p">],</span>
typea<span class="o">=</span>X<span class="p">[,</span><span class="s">&quot;typea&quot;</span><span class="p">],</span> obesity<span class="o">=</span>X<span class="p">[,</span><span class="s">&quot;obesity&quot;</span><span class="p">],</span> alcohol<span class="o">=</span>X<span class="p">[,</span><span class="s">&quot;alcohol&quot;</span><span class="p">],</span> age<span class="o">=</span>X<span class="p">[,</span><span class="s">&quot;age&quot;</span><span class="p">])</span>
params <span class="o">=</span> <span class="kt">c</span><span class="p">(</span><span class="s">&quot;int&quot;</span><span class="p">,</span> <span class="s">&quot;b&quot;</span><span class="p">)</span></code></pre></figure>
<p>##Run JAGS</p>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Error: &lt;text&gt;:3:1: unexpected &#39;,&#39;
## 2: suppressMessages(update(mod1, 5e3))
## 3: ,
## ^</code></pre></figure>
<h2 id="convergence-diagnostics">convergence diagnostics</h2>
<p>1) No pattern in traceplot
2) After 5e3 updates (e.g. burn ins I still observe autocorrelation in b[2] and in int)
Increasing it to 5e4
3) DIC
Mean deviance: 503.9
penalty 8.646
Penalized deviance: 512.5</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>plot<span class="p">(</span>mod1_sim<span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/Plotting diagnostics-1.svg" alt="plot of chunk Plotting diagnostics" /><img src="/assets/Rfig/Plotting diagnostics-2.svg" alt="plot of chunk Plotting diagnostics" /><img src="/assets/Rfig/Plotting diagnostics-3.svg" alt="plot of chunk Plotting diagnostics" /><img src="/assets/Rfig/Plotting diagnostics-4.svg" alt="plot of chunk Plotting diagnostics" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>gelman.diag<span class="p">(</span>mod1_sim<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Potential scale reduction factors:
##
## Point est. Upper C.I.
## b[1] 1.00 1.00
## b[2] 1.05 1.11
## b[3] 1.00 1.00
## b[4] 1.00 1.01
## b[5] 1.00 1.00
## b[6] 1.00 1.00
## b[7] 1.00 1.00
## b[8] 1.00 1.00
## b[9] 1.00 1.00
## int 1.04 1.10
##
## Multivariate psrf
##
## 1.02</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>autocorr.diag<span class="p">(</span>mod1_sim<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## b[1] b[2] b[3] b[4] b[5]
## Lag 0 1.000000000 1.0000000 1.0000000000 1.000000000 1.0000000000
## Lag 1 0.287920926 0.9851922 0.3462278072 0.722909298 0.2658015636
## Lag 5 0.010262570 0.9378278 0.0008810413 0.205100190 0.0024193182
## Lag 10 0.007303032 0.8806068 0.0132742196 0.030675749 -0.0005156971
## Lag 50 0.005479209 0.5317571 -0.0033351594 0.005065785 -0.0052446834
## b[6] b[7] b[8] b[9] int
## Lag 0 1.000000000 1.000000000 1.000000000 1.000000000 1.0000000
## Lag 1 0.301678048 0.630132049 0.239460718 0.534791017 0.9852943
## Lag 5 0.004675731 0.172722918 -0.006375527 0.092841859 0.9369034
## Lag 10 0.004933605 0.036995541 -0.002855998 0.005109218 0.8799680
## Lag 50 -0.002915316 0.007974022 0.004067879 -0.006795221 0.5316811</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>autocorr.plot<span class="p">(</span>mod1_sim<span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/Plotting diagnostics-5.svg" alt="plot of chunk Plotting diagnostics" /><img src="/assets/Rfig/Plotting diagnostics-6.svg" alt="plot of chunk Plotting diagnostics" /><img src="/assets/Rfig/Plotting diagnostics-7.svg" alt="plot of chunk Plotting diagnostics" /><img src="/assets/Rfig/Plotting diagnostics-8.svg" alt="plot of chunk Plotting diagnostics" /><img src="/assets/Rfig/Plotting diagnostics-9.svg" alt="plot of chunk Plotting diagnostics" /></p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>effectiveSize<span class="p">(</span>mod1_sim<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## b[1] b[2] b[3] b[4] b[5] b[6]
## 8172.85337 96.52570 6961.24000 2350.24891 8535.10192 7542.87353
## b[7] b[8] b[9] int
## 2810.94261 8762.84597 3950.24210 98.74925</code></pre></figure>
<p>##Summary statistics
I observe that tobacco, adiposity and alcohol have
posterior probabilities centered around 0.
This means that they do not contribute much to heart disease
I remove these and compare between models</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>dic1</code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Mean deviance: 394.9
## penalty 8.171
## Penalized deviance: 403.1</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>posterior <span class="o">&lt;-</span> mod1_csim<span class="p">[,</span><span class="m">1</span><span class="o">:</span><span class="m">9</span><span class="p">]</span>
<span class="kp">colnames</span><span class="p">(</span>posterior<span class="p">)</span> <span class="o">&lt;-</span> <span class="kp">colnames</span><span class="p">(</span>X<span class="p">)</span>
posterior<span class="o">%&gt;%</span>
<span class="kp">as.data.frame</span><span class="p">()</span> <span class="o">%&gt;%</span>
select<span class="p">(</span><span class="o">-</span><span class="kt">c</span><span class="p">(</span>famhist<span class="p">))</span> <span class="o">%&gt;%</span>
melt<span class="p">()</span> <span class="o">%&gt;%</span>
ggplot<span class="p">(</span>aes<span class="p">(</span>x<span class="o">=</span>value<span class="p">,</span> fill<span class="o">=</span>variable<span class="p">))</span><span class="o">+</span>
geom_histogram<span class="p">(</span>colour<span class="o">=</span><span class="s">&quot;black&quot;</span><span class="p">,</span> size<span class="o">=</span><span class="m">0.1</span><span class="p">,</span> binwidth<span class="o">=</span><span class="m">0.05</span><span class="p">)</span> <span class="o">+</span>
facet_wrap<span class="p">(</span><span class="o">~</span>variable<span class="p">,</span> ncol<span class="o">=</span><span class="m">3</span><span class="p">)</span> <span class="o">+</span>
theme_classic<span class="p">()</span> <span class="o">+</span>
scale_fill_brewer<span class="p">(</span>palette<span class="o">=</span><span class="s">&quot;Set1&quot;</span><span class="p">)</span><span class="o">+</span>
theme<span class="p">(</span>legend.position<span class="o">=</span><span class="s">&quot;none&quot;</span><span class="p">)</span> <span class="o">+</span>
xlim<span class="p">(</span><span class="m">-3</span><span class="p">,</span> <span class="m">3</span><span class="p">)</span></code></pre></figure>
<p><img src="/assets/Rfig/unnamed-chunk-1-1.svg" alt="plot of chunk unnamed-chunk-1" /></p>
<p>##Ajust model to remove terms centered around 0
Here the autocorrelation disappers and the effective smaple size is larger for all variables</p>
<p><img src="/assets/Rfig/unnamed-chunk-2-1.svg" alt="plot of chunk unnamed-chunk-2" /><img src="/assets/Rfig/unnamed-chunk-2-2.svg" alt="plot of chunk unnamed-chunk-2" /><img src="/assets/Rfig/unnamed-chunk-2-3.svg" alt="plot of chunk unnamed-chunk-2" /><img src="/assets/Rfig/unnamed-chunk-2-4.svg" alt="plot of chunk unnamed-chunk-2" /><img src="/assets/Rfig/unnamed-chunk-2-5.svg" alt="plot of chunk unnamed-chunk-2" /></p>
<h1 id="comparing-the-models">Comparing the models</h1>
<p>DIC for model 1 is larger than for model 2. Therefore, model2 is better and I will use this</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>dic1</code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Mean deviance: 394.9
## penalty 8.171
## Penalized deviance: 403.1</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>dic2</code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Mean deviance: 388.3
## penalty 6.957
## Penalized deviance: 395.2</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span>dic1 <span class="o">-</span> dic2</code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>## Difference: 7.843772
## Sample standard error: 26.6649</code></pre></figure>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span></span><span class="kp">summary</span><span class="p">(</span>mod2_sim<span class="p">)</span></code></pre></figure>
<figure class="highlight"><pre><code class="language-text" data-lang="text"><span></span>##
## Iterations = 6001:11000
## Thinning interval = 1
## Number of chains = 3
## Sample size per chain = 5000
##
## 1. Empirical mean and standard deviation for each variable,