-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathTC-DESeq2-Analysis.R
executable file
·1406 lines (1166 loc) · 75.8 KB
/
TC-DESeq2-Analysis.R
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
## DESeq2 - Time course and knockdown analysis ##
library(readr)
library(dplyr)
library(DESeq2)
library(tibble)
library(tidyr)
library(ggplot2)
library(RColorBrewer)
library(genefilter)
library(pheatmap)
library(biomaRt)
## Count reads with featureCounts version 1.4.6-p3 ##
#featureCounts -a ./../RNA-seq-rep1/mm10fromENSEMBL/Mus_musculus.GRCm38.79.gtf -o 24022017-AllRNASeqData-CountMat.txt -F GTF -t exon -g gene_id -s 0 -Q 1 -T 12 --minReadOverlap 1 -M Mapped.Trim.H0Y3NBGXX_TC1-O-D3-2_14s006637-1-1_Sinkkonen_lane114s006637_1_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-A-D1-1_15s009483-1-1_Sinkkonen_lane115s009483_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-A-D15-3_15s009491-1-1_Sinkkonen_lane115s009491_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-A-D3-2_15s009485-1-1_Sinkkonen_lane115s009485_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-A-D5-3_15s009487-1-1_Sinkkonen_lane115s009487_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-A-D9-1_15s009489-1-1_Sinkkonen_lane115s009489_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-O-D1-3_15s009484-1-1_Sinkkonen_lane115s009484_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-O-D15-3_15s009492-1-1_Sinkkonen_lane115s009492_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-O-D3-3_15s009486-1-1_Sinkkonen_lane115s009486_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-O-D5-2_15s009488-1-1_Sinkkonen_lane115s009488_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-O-D9-3_15s009490-1-1_Sinkkonen_lane115s009490_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H5TCCBGXX_TC2-ST2-D0-3_15s009482-1-1_Sinkkonen_lane115s009482_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-A-D1-3_15s009494-1-1_Sinkkonen_lane115s009494_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-A-D15-3_15s009502-1-1_Sinkkonen_lane115s009502_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-A-D3-3_15s009496-1-1_Sinkkonen_lane115s009496_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-A-D5-2_15s009498-1-1_Sinkkonen_lane115s009498_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-A-D9-2_15s009500-1-1_Sinkkonen_lane115s009500_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-O-D1-1_15s009495-1-1_Sinkkonen_lane115s009495_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-O-D15-2_15s009503-1-1_Sinkkonen_lane115s009503_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-O-D3-2_15s009497-1-1_Sinkkonen_lane115s009497_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-O-D5-2_15s009499-1-1_Sinkkonen_lane115s009499_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-O-D9-1_15s009501-1-1_Sinkkonen_lane115s009501_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.H7LKGBGXX_TC3-ST2-D0-1_15s009493-1-1_Sinkkonen_lane115s009493_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.TC1_A_D15_2_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.TC1_A_D1_3_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.TC1_A_D3_2_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.TC1_A_D5_1_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.TC1_A_D9_2_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.TC1_O_D15_3_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.TC1_O_D1_2_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.TC1_O_D5_1_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.TC1_O_D9_1_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam Mapped.Trim.TC1_ST2_D0_1_sequence.txt.worRNAreads.txt.gzAligned.sortedByCoord.out.bam
## Load data ##
TCData = read_delim("Counts/24022017-AllRNASeqData-CountMat.txt", delim = "\t", progress = TRUE, skip = 1)
View(TCData)
## Rearrange data to have first the time course ##
spleNamesTC = read_delim("G:/RNA-Seq-NewAnalysis/Counts/SamplesNamesTC.txt", progress = TRUE, delim = "\t", col_names = FALSE)
TCData.Mat = column_to_rownames(TCData, var = "Geneid") %>%
select(-Chr, -Start, -End, -Strand, -Length)
colnames(TCData.Mat) = t(spleNamesTC)
# TCData.Mat = mutate(TCData.Mat, `ST2-D0-rep1_c` = `ST2-D0-rep1`, `ST2-D0-rep2_c` = `ST2-D0-rep2`, `ST2-D0-rep3_c` = `ST2-D0-rep3`)
TCData.Mat
TCData.Mat.Fin = select(TCData.Mat, ends_with("ST2-D0-rep1"), ends_with("ST2-D0-rep2"), ends_with("ST2-D0-rep3"), contains("A-D1-rep1"),
contains("A-D1-rep2"), contains("A-D1-rep3"), contains("A-D3-rep1"), contains("A-D3-rep2"), contains("A-D3-rep3"),
contains("A-D5-rep1"), contains("A-D5-rep2"), contains("A-D5-rep3"), contains("A-D9-rep1"), contains("A-D9-rep2"),
contains("A-D9-rep3"), contains("A-D15-rep1"), contains("A-D15-rep2"), contains("A-D15-rep3"),contains("O-D1-rep1"),
# matches("ST2-D0-rep1_c"), matches("ST2-D0-rep2_c"), matches("ST2-D0-rep3_c"),
contains("O-D1-rep2"), contains("O-D1-rep3"), contains("O-D3-rep1"), contains("O-D3-rep2"), contains("O-D3-rep3"),
contains("O-D5-rep1"), contains("O-D5-rep2"), contains("O-D5-rep3"), contains("O-D9-rep1"), contains("O-D9-rep2"),
contains("O-D9-rep3"), contains("O-D15-rep1"), contains("O-D15-rep2"), contains("O-D15-rep3"))
TCData.Mat.Fin
colnames(TCData.Mat.Fin)
## Cretae the metadata ##
mData = data.frame(row.names = colnames(TCData.Mat.Fin), Cell = factor(c(rep("ST2", 3), rep("Ad", 15), rep("Ob", 15))),
Days = factor(c(rep("0", 3), rep("1", 3), rep("3", 3), rep("5", 3), rep("9", 3), rep("15", 3),
rep("1", 3), rep("3", 3), rep("5", 3), rep("9", 3), rep("15", 3))),
replicate = factor(rep(1:3, 11)),
SampleID = factor(c(rep("ST2-D0", 3), rep("A-D1", 3), rep("A-D3", 3), rep("A-D5", 3), rep("A-D9", 3), rep("A-D15", 3),
rep("O-D1", 3), rep("O-D3", 3), rep("O-D5", 3), rep("O-D9", 3), rep("O-D15", 3))))
mData
## DESeq matrix ##
ddsTC = DESeq2::DESeqDataSetFromMatrix(TCData.Mat.Fin, mData, design = ~SampleID)
colData(ddsTC)
ddsTC$SampleID = relevel(ddsTC$SampleID, "ST2-D0")
colData(ddsTC)
## Remove rows that have only 0 or 1 read ##
ddsTC = ddsTC[rowSums(counts(ddsTC)) > 1,]
head(assay(ddsTC))
## R-Log transformation ##
rldTC = rlogTransformation(ddsTC, blind = FALSE)
colData(rldTC)
head(assay(rldTC))
## PCA plot for Figure 1B ##
# PCAPlot = plotPCA(rldTC, intgroup = c("Cell", "type"))
PCAPlot.HandMade = plotPCA(rldTC, intgroup = c("Cell", "Days", "replicate"), returnData = TRUE)
percentVar = round(100 * attr(PCAPlot.HandMade, "percentVar"))
PCAPlot.HandMade$Days = factor(PCAPlot.HandMade$Days, levels = c(0, 1, 3, 5, 9, 15))
PCAPlot.HandMade$Cell = factor(PCAPlot.HandMade$Cell, levels = c("ST2", "Ad", "Ob"))
PCA.Diff = ggplot(PCAPlot.HandMade, aes(x = PC1, y = PC2, shape = Cell, color = Days)) +
geom_point(size = 5.0) +
labs(x = paste0("PC1: ", percentVar[1], "% variance"), y = paste0("PC2: ", percentVar[2], "% variance"),
fill = "Time") +
theme_classic() +
scale_shape_manual(values = c(17, 16, 15)) +
coord_cartesian(ylim = c(-40, 30)) +
scale_color_brewer(palette = "Paired") +
theme(axis.text.x = element_text(face = "bold", size = 16, colour = "black"),
axis.text.y = element_text(face = "bold", size = 16, colour = "black"),
axis.title.x = element_text(face = "bold", size = 16),
axis.title.y = element_text(face = "bold", size = 16),
axis.line = element_line(colour = "black", size = 1, linetype = "solid"),
legend.title = element_text(size = 16, face = "bold"),
legend.text = element_text(size = 16, face = "bold"))
pdf("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/24032017-PCA.Diff.pdf")
PCA.Diff
dev.off()
## Pheatmap for Figure 1D ##
# Get the gene name instead of Ensembl ID
# Use the same reference as the one that has been used during the mapping (Mus_musculus.GRCm38.79.gtf)
listMarts(host = 'mar2015.archive.ensembl.org')
ensembl79 = useMart(host = 'mar2015.archive.ensembl.org', biomart = 'ENSEMBL_MART_ENSEMBL', dataset = 'mmusculus_gene_ensembl')
filters = listFilters(ensembl79)
head(filters)
attributes = listAttributes(ensembl79)
head(attributes)
topVarGenes = head(order(rowVars(assay(rldTC)),decreasing = TRUE), 100) ## Take top 100 variable genes
mat = as.data.frame(assay(rldTC)[topVarGenes, ])
mat = mat - rowMeans(mat) ## how much a gene deviates in a specific sample from the gene average across all samples
head(mat)
ensGId.mat = rownames(mat)
qry.mat = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id', values = ensGId.mat, mart = ensembl79)
head(qry.mat)
mat$ensembl_gene_id = ensGId.mat
NameMat.mat = dplyr::full_join(mat, qry.mat, by = "ensembl_gene_id")
head(NameMat.mat)
rownames(NameMat.mat) = NameMat.mat$external_gene_name
NameMat.mat = NameMat.mat[,-34:-35]
head(NameMat.mat, 50)
df = as.data.frame(colData(rldTC)["Days"])
df$Days = factor(df$Days, levels = c(0, 1, 3, 5, 9, 15))
Days = c("#A6CEE3" ,"#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99" ,"#E31A1C")
names(Days) = c("0", "1", "3", "5", "9", "15")
ann_colors = list(Days = Days)
pdf("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/24032017-PheatmapTop100VarGenes.pdf", width = 8.27, height = 11.69,
onefile = FALSE)
ph = pheatmap(NameMat.mat, annotation_col = df, fontsize_row = 9, cluster_cols = FALSE, scale = "row",
color = colorRampPalette(rev(brewer.pal(11, "RdBu")))(100),
annotation_colors = ann_colors, annotation_legend = TRUE, show_colnames = FALSE, cellwidth = 8,
cellheight = 8, gaps_col = c(3, 18, 33))
dev.off()
## DEG ##
ddsTC2 = DESeq(ddsTC)
resultsNames(ddsTC2)
TC.NormCountMat = counts(ddsTC2, normalized = TRUE)
View(TC.NormCountMat)
write.table(TC.NormCountMat, "31032017-TC-NormCountMat.txt", sep = "\t", row.names = TRUE, col.names = TRUE, quote = FALSE)
## Calculate FPKM for TEPIC-DREM pipeline ##
# Take the gene length from the featureCounts output
gene_Length = dplyr::select(TCData, Geneid, Length)
gene_Length
GeneEnsId = tbl_df(rownames(ddsTC)) %>%
dplyr::rename(Geneid = value)
GeneEnsId
# Combine the gene name from above with the gene length
geneID.Length = left_join(GeneEnsId, gene_Length, by = "Geneid")
# Calculate the FPKM
mcols(ddsTC)$basepairs = as.vector(geneID.Length$Length)
all(rownames(ddsTC) == geneID.Length$Geneid) ## make sure that these new columns line up row for row with the DESeqDataSet
TC.FPKM.mat = fpkm(ddsTC, robust = TRUE)
View(TC.FPKM.mat)
TC.FPKM.mat = rownames_to_column(as.data.frame(TC.FPKM.mat)) %>%
dplyr::rename(ensembl_gene_id = rowname)
head(TC.FPKM.mat)
write.table(TC.FPKM.mat, "18052017_TC_FPKM_Mat.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
# DEG in adipo day 1 vs ST2 day 0
FCAD1vsD0 = list("SampleIDA.D1", "SampleIDST2.D0")
resAd1vsD0 = results(ddsTC2, contrast = FCAD1vsD0)
# save(resAd1vsD0, file = "G:/RNASeq-Ahr-Gzf1-KD/CountMat/13032017-DEG-AD1vsD0.rda")
resAd1vsD0.Sig = as.data.frame(subset(resAd1vsD0, padj < 0.05)) %>%
rownames_to_column(var = "ensembl_gene_id")
ensID.Ad1vsD0 = select(resAd1vsD0.Sig, ensembl_gene_id)
qry.Ad1vsD0 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.Ad1vsD0, mart = ensembl79)
head(qry.Ad1vsD0)
resAd1vsD0.Sig.GS = full_join(resAd1vsD0.Sig, qry.Ad1vsD0, by = "ensembl_gene_id") %>%
select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange,lfcSE, stat, pvalue, padj)
head(resAd1vsD0.Sig.GS)
write.table(resAd1vsD0.Sig.GS, "./DEG/24032017-AD1vsD0.Sig.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
# DEG in adipo day 3 vs ST2 day 0
FCAD3vsD0 = list("SampleIDA.D3", "SampleIDST2.D0")
resAd3vsD0 = results(ddsTC2, contrast = FCAD3vsD0)
resAd3vsD0.Sig = as.data.frame(subset(resAd3vsD0, padj < 0.05)) %>%
rownames_to_column(var = "ensembl_gene_id")
ensID.Ad3vsD0 = select(resAd3vsD0.Sig, ensembl_gene_id)
qry.Ad3vsD0 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.Ad3vsD0, mart = ensembl79)
head(qry.Ad3vsD0)
resAd3vsD0.Sig.GS = full_join(resAd3vsD0.Sig, qry.Ad3vsD0, by = "ensembl_gene_id") %>%
select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange,lfcSE, stat, pvalue, padj)
head(resAd3vsD0.Sig.GS)
write.table(resAd3vsD0.Sig.GS, "./DEG/24032017-AD3vsD0.Sig.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
# DEG in adipo day 5 vs ST2 day 0
FCAD5vsD0 = list("SampleIDA.D5", "SampleIDST2.D0")
resAd5vsD0 = results(ddsTC2, contrast = FCAD5vsD0)
resAd5vsD0.Sig = as.data.frame(subset(resAd5vsD0, padj < 0.05)) %>%
rownames_to_column(var = "ensembl_gene_id")
ensID.Ad5vsD0 = select(resAd5vsD0.Sig, ensembl_gene_id)
qry.Ad5vsD0 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.Ad5vsD0, mart = ensembl79)
head(qry.Ad5vsD0)
resAd5vsD0.Sig.GS = full_join(resAd5vsD0.Sig, qry.Ad5vsD0, by = "ensembl_gene_id") %>%
select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange,lfcSE, stat, pvalue, padj)
head(resAd5vsD0.Sig.GS)
write.table(resAd5vsD0.Sig.GS, "./DEG/24032017-AD5vsD0.Sig.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
# DEG in adipo day 9 vs ST2 day 0
FCAD9vsD0 = list("SampleIDA.D9", "SampleIDST2.D0")
resAd9vsD0 = results(ddsTC2, contrast = FCAD9vsD0)
resAd9vsD0.Sig = as.data.frame(subset(resAd9vsD0, padj < 0.05)) %>%
rownames_to_column(var = "ensembl_gene_id")
ensID.Ad9vsD0 = select(resAd9vsD0.Sig, ensembl_gene_id)
qry.Ad9vsD0 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.Ad9vsD0, mart = ensembl79)
head(qry.Ad9vsD0)
resAd9vsD0.Sig.GS = full_join(resAd9vsD0.Sig, qry.Ad9vsD0, by = "ensembl_gene_id") %>%
select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange,lfcSE, stat, pvalue, padj)
head(resAd9vsD0.Sig.GS)
write.table(resAd9vsD0.Sig.GS, "./DEG/24032017-AD9vsD0.Sig.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
# DEG in adipo day 15 vs ST2 day 0
FCAD15vsD0 = list("SampleIDA.D15", "SampleIDST2.D0")
resAd15vsD0 = results(ddsTC2, contrast = FCAD15vsD0)
save(resAd15vsD0, file = "Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/DEG/14042017-DEG-AD15vsD0.rda")
resAd15vsD0.Sig = as.data.frame(subset(resAd15vsD0, padj < 0.05)) %>%
rownames_to_column(var = "ensembl_gene_id")
ensID.Ad15vsD0 = select(resAd15vsD0.Sig, ensembl_gene_id)
qry.Ad15vsD0 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.Ad15vsD0, mart = ensembl79)
head(qry.Ad15vsD0)
resAd15vsD0.Sig.GS = full_join(resAd15vsD0.Sig, qry.Ad15vsD0, by = "ensembl_gene_id") %>%
select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange,lfcSE, stat, pvalue, padj)
head(resAd15vsD0.Sig.GS)
write.table(resAd15vsD0.Sig.GS, "./DEG/24032017-AD15vsD0.Sig.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
# DEG in osteoday 1 vs ST2 day 0
FCOD1vsD0 = list("SampleIDO.D1", "SampleIDST2.D0")
resOd1vsD0 = results(ddsTC2, contrast = FCOD1vsD0)
# save(resOd1vsD0, file = "G:/RNASeq-Ahr-Gzf1-KD/CountMat/15032017-DEG-OD1vsD0.rda")
resOd1vsD0.Sig = as.data.frame(subset(resOd1vsD0, padj < 0.05)) %>%
rownames_to_column(var = "ensembl_gene_id")
ensID.Od1vsD0 = select(resOd1vsD0.Sig, ensembl_gene_id)
qry.Od1vsD0 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.Od1vsD0, mart = ensembl79)
head(qry.Od1vsD0)
resOd1vsD0.Sig.GS = full_join(resOd1vsD0.Sig, qry.Od1vsD0, by = "ensembl_gene_id") %>%
select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange,lfcSE, stat, pvalue, padj)
head(resOd1vsD0.Sig.GS)
write.table(resOd1vsD0.Sig.GS, "./DEG/24032017-OD1vsD0.Sig.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
# DEG in osteoday 3 vs ST2 day 0
FCOD3vsD0 = list("SampleIDO.D3", "SampleIDST2.D0")
resOd3vsD0 = results(ddsTC2, contrast = FCOD3vsD0)
resOd3vsD0.Sig = as.data.frame(subset(resOd3vsD0, padj < 0.05)) %>%
rownames_to_column(var = "ensembl_gene_id")
ensID.Od3vsD0 = select(resOd3vsD0.Sig, ensembl_gene_id)
qry.Od3vsD0 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.Od3vsD0, mart = ensembl79)
head(qry.Od3vsD0)
resOd3vsD0.Sig.GS = full_join(resOd3vsD0.Sig, qry.Od3vsD0, by = "ensembl_gene_id") %>%
select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange,lfcSE, stat, pvalue, padj)
head(resOd3vsD0.Sig.GS)
write.table(resOd3vsD0.Sig.GS, "./DEG/24032017-OD3vsD0.Sig.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
# DEG in osteoday 5 vs ST2 day 0
FCOD5vsD0 = list("SampleIDO.D5", "SampleIDST2.D0")
resOd5vsD0 = results(ddsTC2, contrast = FCOD5vsD0)
resOd5vsD0.Sig = as.data.frame(subset(resOd5vsD0, padj < 0.05)) %>%
rownames_to_column(var = "ensembl_gene_id")
ensID.Od5vsD0 = select(resOd5vsD0.Sig, ensembl_gene_id)
qry.Od5vsD0 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.Od5vsD0, mart = ensembl79)
head(qry.Od5vsD0)
resOd5vsD0.Sig.GS = full_join(resOd5vsD0.Sig, qry.Od5vsD0, by = "ensembl_gene_id") %>%
select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange,lfcSE, stat, pvalue, padj)
head(resOd5vsD0.Sig.GS)
write.table(resOd5vsD0.Sig.GS, "./DEG/24032017-OD5vsD0.Sig.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
# DEG in osteoday 9 vs ST2 day 0
FCOD9vsD0 = list("SampleIDO.D9", "SampleIDST2.D0")
resOd9vsD0 = results(ddsTC2, contrast = FCOD9vsD0)
resOd9vsD0.Sig = as.data.frame(subset(resOd9vsD0, padj < 0.05)) %>%
rownames_to_column(var = "ensembl_gene_id")
ensID.Od9vsD0 = select(resOd9vsD0.Sig, ensembl_gene_id)
qry.Od9vsD0 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.Od9vsD0, mart = ensembl79)
head(qry.Od9vsD0)
resOd9vsD0.Sig.GS = full_join(resOd9vsD0.Sig, qry.Od9vsD0, by = "ensembl_gene_id") %>%
select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange,lfcSE, stat, pvalue, padj)
head(resOd9vsD0.Sig.GS)
write.table(resOd9vsD0.Sig.GS, "./DEG/24032017-OD9vsD0.Sig.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
# DEG in osteoday 15 vs ST2 day 0
FCOD15vsD0 = list("SampleIDO.D15", "SampleIDST2.D0")
resOd15vsD0 = results(ddsTC2, contrast = FCOD15vsD0)
save(resOd15vsD0, file = "Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/DEG/14042017-DEG-OD15vsD0.rda")
resOd15vsD0.Sig = as.data.frame(subset(resOd15vsD0, padj < 0.05)) %>%
rownames_to_column(var = "ensembl_gene_id")
ensID.Od15vsD0 = select(resOd15vsD0.Sig, ensembl_gene_id)
qry.Od15vsD0 = getBM(attributes = c('external_gene_name', 'ensembl_gene_id'), filters = 'ensembl_gene_id',
values = ensID.Od15vsD0, mart = ensembl79)
head(qry.Od15vsD0)
resOd15vsD0.Sig.GS = full_join(resOd15vsD0.Sig, qry.Od15vsD0, by = "ensembl_gene_id") %>%
select(ensembl_gene_id, external_gene_name, baseMean, log2FoldChange,lfcSE, stat, pvalue, padj)
head(resOd15vsD0.Sig.GS)
write.table(resOd15vsD0.Sig.GS, "./DEG/24032017-OD15vsD0.Sig.txt", sep = "\t", row.names = FALSE, col.names = TRUE, quote = FALSE)
## Keep genes that are significicant (FDR < 5%) at any timepoints for adipo for Figure 1C ##
resAd1.any = as.data.frame(resAd1vsD0) %>%
rownames_to_column(var = "ensembl_gene_id") %>%
select(ensembl_gene_id, log2FoldChangeAD1 = log2FoldChange, padjAD1 = padj)
resAd3.any = as.data.frame(resAd3vsD0) %>%
rownames_to_column(var = "ensembl_gene_id") %>%
select(ensembl_gene_id, log2FoldChangeAD3 = log2FoldChange, padjAD3 = padj)
resAd5.any = as.data.frame(resAd5vsD0) %>%
rownames_to_column(var = "ensembl_gene_id") %>%
select(ensembl_gene_id, log2FoldChangeAD5 = log2FoldChange, padjAD5 = padj)
resAd9.any = as.data.frame(resAd9vsD0) %>%
rownames_to_column(var = "ensembl_gene_id") %>%
select(ensembl_gene_id, log2FoldChangeAD9 = log2FoldChange, padjAD9 = padj)
resAd15.any = as.data.frame(resAd15vsD0) %>%
rownames_to_column(var = "ensembl_gene_id") %>%
select(ensembl_gene_id, log2FoldChangeAD15 = log2FoldChange, padjAD15 = padj)
resOb1.any = as.data.frame(resOd1vsD0) %>%
rownames_to_column(var = "ensembl_gene_id") %>%
select(ensembl_gene_id, log2FoldChangeOB1 = log2FoldChange, padjOB1 = padj)
resOb3.any = as.data.frame(resOd3vsD0) %>%
rownames_to_column(var = "ensembl_gene_id") %>%
select(ensembl_gene_id, log2FoldChangeOB3 = log2FoldChange, padjOB3 = padj)
resOb5.any = as.data.frame(resOd5vsD0) %>%
rownames_to_column(var = "ensembl_gene_id") %>%
select(ensembl_gene_id, log2FoldChangeOB5 = log2FoldChange, padjOB5 = padj)
resOb9.any = as.data.frame(resOd9vsD0) %>%
rownames_to_column(var = "ensembl_gene_id") %>%
select(ensembl_gene_id, log2FoldChangeOB9 = log2FoldChange, padjOB9 = padj)
resOb15.any = as.data.frame(resOd15vsD0) %>%
rownames_to_column(var = "ensembl_gene_id") %>%
select(ensembl_gene_id, log2FoldChangeOB15 = log2FoldChange, padjOB15 = padj)
DEGAll.Adipo = left_join(resAd1.any, resAd3.any, by = "ensembl_gene_id") %>%
left_join(resAd5.any, by = "ensembl_gene_id") %>%
left_join(resAd9.any, by = "ensembl_gene_id") %>%
left_join(resAd15.any, by = "ensembl_gene_id")
head(DEGAll.Adipo)
DEGAll.Adipo.padj = select(DEGAll.Adipo, ensembl_gene_id, starts_with("padj"))
head(DEGAll.Adipo.padj)
DEGAll.Adipo.any = DEGAll.Adipo.padj[apply(DEGAll.Adipo.padj[, -1], MARGIN = 1, function(x) any(x < 0.05)), ] %>%
na.omit()
head(DEGAll.Adipo.any)
write_delim(DEGAll.Adipo.any, "./DEG/24032017-Ad-DEGatAnyTP-FDR0.05.txt", delim = "\t", col_names = TRUE)
## Apply a cut-off on the threshold ##
DEGAll.Adipo.FCThr = filter(DEGAll.Adipo, DEGAll.Adipo$ensembl_gene_id %in% DEGAll.Adipo.any$ensembl_gene_id) %>%
select(ensembl_gene_id,starts_with("log2"))
head(DEGAll.Adipo.FCThr)
DEGAll.Adipo.FCThr.abs1UP = DEGAll.Adipo.FCThr[apply(DEGAll.Adipo.FCThr[, -1], MARGIN = 1,
function(x) any(x >= 1)), ]
head(DEGAll.Adipo.FCThr.abs1UP)
write_delim(DEGAll.Adipo.FCThr.abs1UP, "./DEG/27032017-Ad-DEGatAnyTP-FDR0.05-UPFClin2.txt", delim = "\t", col_names = TRUE)
DEGAll.Adipo.FCThr.abs1DOWN = DEGAll.Adipo.FCThr[apply(DEGAll.Adipo.FCThr[, -1], MARGIN = 1,
function(x) any(x <= -1)), ]
head(DEGAll.Adipo.FCThr.abs1DOWN)
write_delim(DEGAll.Adipo.FCThr.abs1DOWN, "./DEG/27032017-Ad-DEGatAnyTP-FDR0.05-DOWNFClin2.txt", delim = "\t", col_names = TRUE)
## Keep genes that are significicant (FDR < 5%) at any timepoints for osteo for Figure 1C ##
DEGAll.Osteo = left_join(resOb1.any, resOb3.any, by = "ensembl_gene_id") %>%
left_join(resOb5.any, by = "ensembl_gene_id") %>%
left_join(resOb9.any, by = "ensembl_gene_id") %>%
left_join(resOb15.any, by = "ensembl_gene_id")
head(DEGAll.Osteo)
DEGAll.Osteo.padj = select(DEGAll.Osteo, ensembl_gene_id, starts_with("padj"))
head(DEGAll.Osteo.padj)
DEGAll.Osteo.any = DEGAll.Osteo.padj[apply(DEGAll.Osteo.padj[, -1], MARGIN = 1, function(x) any(x < 0.05)), ] %>%
na.omit()
head(DEGAll.Osteo.any)
write_delim(DEGAll.Osteo.any, "./DEG/24032017-Ob-DEGatAnyTP-FDR0.05.txt", delim = "\t", col_names = TRUE)
## Apply a cut-off on the threshold osteo
DEGAll.Osteo.FCThr = filter(DEGAll.Osteo, DEGAll.Osteo$ensembl_gene_id %in% DEGAll.Osteo.any$ensembl_gene_id) %>%
select(ensembl_gene_id,starts_with("log2"))
head(DEGAll.Osteo.FCThr)
DEGAll.Osteo.FCThr.abs1UP = DEGAll.Osteo.FCThr[apply(DEGAll.Osteo.FCThr[, -1], MARGIN = 1,
function(x) any(x >= 1)), ]
head(DEGAll.Osteo.FCThr.abs1UP)
write_delim(DEGAll.Osteo.FCThr.abs1UP, "./DEG/27032017-Ob-DEGatAnyTP-FDR0.05-UPFClin2.txt", delim = "\t", col_names = TRUE)
DEGAll.Osteo.FCThr.abs1DOWN = DEGAll.Osteo.FCThr[apply(DEGAll.Osteo.FCThr[, -1], MARGIN = 1,
function(x) any(x <= -1)), ]
write_delim(DEGAll.Osteo.FCThr.abs1DOWN, "./DEG/27032017-Ob-DEGatAnyTP-FDR0.05-DOWNFClin2.txt", delim = "\t", col_names = TRUE)
head(DEGAll.Osteo.FCThr.abs1DOWN)
## Plot time-series profile of Ahr, Glis1, Cxcl12, Notch1, Notch2, Notch3, Notch4
Ahr = "ENSMUSG00000019256"
Notch1 = "ENSMUSG00000026923"
Notch2 = "ENSMUSG00000027878"
Notch3 = "ENSMUSG00000038146"
Notch4 = "ENSMUSG00000015468"
Cxcl12 = "ENSMUSG00000061353"
Glis1 = "ENSMUSG00000034762"
# Retrieve the normalized counts for Ahr #
AhrCounts = plotCounts(ddsTC2, gene = Ahr, intgroup = c("SampleID", "Cell"), returnData = TRUE)
AhrCounts$SampleID = factor(AhrCounts$SampleID, levels = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D9", "A-D15",
"O-D1", "O-D3", "O-D5", "O-D9", "O-D15"))
# Retrieve the normalized counts for Glis1 #
Glis1Counts = plotCounts(ddsTC2, gene = Glis1, intgroup = c("SampleID", "Cell"), returnData = TRUE)
Glis1Counts$SampleID = factor(Glis1Counts$SampleID, levels = c("ST2-D0", "A-D1", "A-D3", "A-D5", "A-D9", "A-D15",
"O-D1", "O-D3", "O-D5", "O-D9", "O-D15"))
# Retrieve the normalized counts for Notch1 #
Notch1Counts = plotCounts(ddsTC2, gene = Notch1, intgroup = c("SampleID", "Cell"), returnData = TRUE)
Notch1Counts$SampleID = factor(Notch1Counts$SampleID, levels = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D9", "A-D15",
"O-D1", "O-D3", "O-D5", "O-D9", "O-D15"))
# Retrieve the normalized counts for Notch2 #
Notch2Counts = plotCounts(ddsTC2, gene = Notch2, intgroup = c("SampleID", "Cell"), returnData = TRUE)
Notch2Counts$SampleID = factor(Notch2Counts$SampleID, levels = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D9", "A-D15",
"O-D1", "O-D3", "O-D5", "O-D9", "O-D15"))
# Retrieve the normalized counts for Notch3 #
Notch3Counts = plotCounts(ddsTC2, gene = Notch3, intgroup = c("SampleID", "Cell"), returnData = TRUE)
Notch3Counts$SampleID = factor(Notch3Counts$SampleID, levels = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D9", "A-D15",
"O-D1", "O-D3", "O-D5", "O-D9", "O-D15"))
# Retrieve the normalized counts for Notch4 #
Notch4Counts = plotCounts(ddsTC2, gene = Notch4, intgroup = c("SampleID", "Cell"), returnData = TRUE)
Notch4Counts$SampleID = factor(Notch4Counts$SampleID, levels = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D9", "A-D15",
"O-D1", "O-D3", "O-D5", "O-D9", "O-D15"))
# Retrieve the normalized counts for Cxcl12 #
Cxcl12Counts = plotCounts(ddsTC2, gene = Cxcl12, intgroup = c("SampleID", "Cell"), returnData = TRUE)
Cxcl12Counts$SampleID = factor(Cxcl12Counts$SampleID, levels = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D9", "A-D15",
"O-D1", "O-D3", "O-D5", "O-D9", "O-D15"))
## Cxcl12 plot (mRNA and SE) only for Ad for Fig4 in the paper##
# library(scales)
# show_col(hue_pal()(8)) ## Apply the same color at the line and the data points
Cxcl12Counts.Ad = Cxcl12Counts[c(1:18),] ## Select only the count for adipo
rel = "Msc-D0"
Cxcl12Counts.Ad.rel = Cxcl12Counts.Ad %>%
mutate(countRel = count/count[SampleID == rel]) %>%
mutate(type = "Cxcl12 mRNA") ## Normalize the count to Day0 and adda column specifying that it is mRNA samples
Cxcl12SE = read_delim("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/05042017-SEMapAdipoMERGEDFinCounts.txt",
delim = "\t") ## Load the count for the meta SE
AdipoSENm = c("SE_ID", "Msc-D0", "A-D1", "A-D3", "A-D5", "A-D15")
colnames(Cxcl12SE) = AdipoSENm
Cxcl12SE.SE921 = filter(Cxcl12SE, SE_ID == "SE-921") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame()## Take only the counts for SE-921 which targets Cxcl12
head(Cxcl12SE.SE921)
Cxcl12SE.rel = Cxcl12SE.SE921 %>%
mutate(countRel.SE = `SE-921`/`SE-921`[1]) %>%
mutate(SampleID = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D15")) %>%
mutate(type.SE = "merged SE") %>%
as.data.frame()
# Load the Pearson correaltion for SE-921 and Cxcl12
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/10042017-PearsonCor-Cxcl12-SE921.RData")
Cxcl12_SE.921
Cxcl12_Plot_Ad = ggplot(Cxcl12Counts.Ad.rel, aes(x = SampleID, y = countRel, group = 1)) +
geom_point(size = 5, color = "#FF6666") +
geom_smooth(data = Cxcl12Counts.Ad.rel, aes(x = SampleID, y = countRel,
linetype = "Cxcl12 mRNA"),
method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
geom_point(data = Cxcl12SE.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#FF6666", size = 5, shape = 17) +
geom_smooth(data = Cxcl12SE.rel, aes(x = SampleID, y = countRel.SE, linetype = "merged SE"),
method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
labs(x = "Time [Days]", y = "Signal relative to undifferentiated ST2 cells",
title = "Adipocyte differentiation") +
theme_classic() +
theme(axis.line = element_line(colour = "black", size = 1, linetype = "solid"),
axis.text.x = element_text(face = "bold", size = 20, colour = "black"),
axis.text.y = element_text(face = "bold", size = 20, colour = "black"),
axis.title.x = element_text(face = "bold", size = 20),
axis.title.y = element_text(face = "bold", size = 20),
legend.position = "right", legend.text = element_text(size = 20, face = "bold"),
plot.title = element_text(face = "bold", size = 20, hjust = 0.8)) +
scale_linetype_manual(name = "", values = c("solid", "dotdash")) +
scale_x_discrete(breaks = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D9", "A-D15"),
labels = c("D0", "D1", "D3", "D5", "D9", "D15")) +
ylim(0, 1.5)
Cxcl12_Plot_Ad = Cxcl12_Plot_Ad +
geom_text(x = 5.5, y = 1.5, label = paste0("r = ", round(Cxcl12_SE.921, digits = 2)), color = "black", size = 10)
print(Cxcl12_Plot_Ad)
pdf("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/12042017-Cxcl12-Ad-Fig3B.pdf", width = 8, height = 8)
Cxcl12_Plot_Ad
dev.off()
## Cxcl12 plot (mRNA and SE) only for Ob for Fig3 in the paper##
## Apply the same color at the line and the data points
Cxcl12Counts.Ob = Cxcl12Counts[c(1:3, 19:33),] ## Select only the count for adipo
rel.Ob = "Msc-D0"
Cxcl12Counts.Ob.rel = Cxcl12Counts.Ob %>%
mutate(countRel = count/count[SampleID == rel.Ob]) %>%
mutate(type = "Cxcl12 mRNA") ## Normalize the count to Day0 and adda column specifying that it is mRNA samples
Cxcl12SE.Ob = read_delim("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/05042017-SEMapOsteoMERGEDFinCounts.txt",
delim = "\t") ## Load the count for the meta SE
OsteoSENm = c("SE_ID", "Msc-D0", "O-D1", "O-D3", "O-D5", "O-D9", "O-D15")
colnames(Cxcl12SE.Ob) = OsteoSENm
Cxcl12SE.Ob.SE921 = filter(Cxcl12SE.Ob, SE_ID == "SE-921") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame()## Take only the counts for SE-889 which targets Cxcl12
head(Cxcl12SE.Ob.SE921)
Cxcl12SE.Ob.rel = Cxcl12SE.Ob.SE921 %>%
mutate(countRel.SE = `SE-921`/`SE-921`[1]) %>%
mutate(SampleID = c("Msc-D0", "O-D1", "O-D3", "O-D5", "O-D9", "O-D15")) %>%
mutate(type.SE = "merged SE") %>%
as.data.frame()
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/10042017-PearsonCor-Cxcl12-SE921.Ob.RData")
Cxcl12_Ob_SE.921
Cxcl12_Plot_Ob = ggplot(Cxcl12Counts.Ob.rel, aes(x = SampleID, y = countRel, group = 1)) +
geom_point(size = 5, color = "#0066CC") +
geom_smooth(data = Cxcl12Counts.Ob.rel, aes(x = SampleID, y = countRel,
linetype = "Cxcl12 mRNA"),
method = "loess", se = FALSE, size = 1.5, color = "#0066CC") +
geom_point(data = Cxcl12SE.Ob.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#0066CC", size = 5, shape = 17) +
geom_smooth(data = Cxcl12SE.Ob.rel, aes(x = SampleID, y = countRel.SE, linetype = "merged SE"),
method = "loess", se = FALSE, size = 1.5, color = "#0066CC") +
labs(x = "Time [Days]", y = "Signal relative to undifferentiated ST2 cells",
title = "Osteoblast differentiation") +
theme_classic() +
theme(axis.line = element_line(colour = "black", size = 1, linetype = "solid"),
axis.text.x = element_text(face = "bold", size = 20, colour = "black"),
axis.text.y = element_text(face = "bold", size = 20, colour = "black"),
axis.title.x = element_text(face = "bold", size = 20),
axis.title.y = element_text(face = "bold", size = 20),
legend.position = "right", legend.text = element_text(size = 20, face = "bold"),
plot.title = element_text(face = "bold", size = 20, hjust = 0.8)) +
scale_linetype_manual(name = "", values = c("solid", "dotdash")) +
scale_x_discrete(breaks = c("Msc-D0", "O-D1", "O-D3", "O-D5", "O-D9", "O-D15"),
labels = c("D0", "D1", "D3", "D5", "D9", "D15")) +
ylim(0, 1.5)
Cxcl12_Plot_Ob = Cxcl12_Plot_Ob +
geom_text(x = 5.5, y = 1.5, label = paste0("r = ", round(Cxcl12_Ob_SE.921, digits = 2)), color = "black", size = 10)
print(Cxcl12_Plot_Ob)
pdf("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/12042017-Cxcl12-Ob-Fig3B.pdf", width = 8, height = 8)
Cxcl12_Plot_Ob
dev.off()
#################################### Ahr figure 4 - se and mRNA plot - Adipo ############################################
## Ahr plot (mRNA and SE) only for Ad for Fig4 in the paper##
AhrCounts.Ad = AhrCounts[c(1:18),] ## Select only the count for adipo
rel = "Msc-D0"
AhrCounts.Ad.rel = AhrCounts.Ad %>%
mutate(countRel = count/count[SampleID == rel]) %>%
mutate(type = "Ahr mRNA") ## Normalize the count to Day0 and adda column specifying that it is mRNA samples
AhrSE = read_delim("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/05042017-SEMapAdipoMERGEDFinCounts.txt",
delim = "\t") ## Load the count for the meta SE
AdipoSENm = c("SE_ID", "Msc-D0", "A-D1", "A-D3", "A-D5", "A-D15")
colnames(AhrSE) = AdipoSENm
AhrSE.SE283 = filter(AhrSE, SE_ID == "SE-283") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame() %>%
head() ## Take only the counts for SE-283 which targets Ahr
AhrSE.SE284 = filter(AhrSE, SE_ID == "SE-284") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame() %>%
head() ## Take only the counts for SE-284 which targets Ahr
AhrSE.SE285 = filter(AhrSE, SE_ID == "SE-285") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame() %>%
head() ## Take only the counts for SE-285 which targets Ahr
AhrSE.SE286 = filter(AhrSE, SE_ID == "SE-286") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame() %>%
head() ## Take only the counts for SE-286 which targets Ahr
## Load the Pearson correlation between Ahr mRNA and their SEs ##
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/11042017-PearsonCor-Ahr-SE283.RData")
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/11042017-PearsonCor-Ahr-SE284.RData")
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/11042017-PearsonCor-Ahr-SE285.RData")
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/11042017-PearsonCor-Ahr-SE286.RData")
Ahr_SE.283
Ahr_SE.284
Ahr_SE.285
Ahr_SE.286
AhrSE.283.rel = AhrSE.SE283 %>%
mutate(countRel.SE = `SE-283`/`SE-283`[1]) %>%
mutate(SampleID = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D15")) %>%
mutate(type.SE = paste("SE-283 | r =", round(Ahr_SE.283, digits = 2))) %>%
as.data.frame()
AhrSE.284.rel = AhrSE.SE284 %>%
mutate(countRel.SE = `SE-284`/`SE-284`[1]) %>%
mutate(SampleID = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D15")) %>%
mutate(type.SE = paste("SE-284 | r =", round(Ahr_SE.284, digits = 2))) %>%
as.data.frame()
AhrSE.285.rel = AhrSE.SE285 %>%
mutate(countRel.SE = `SE-285`/`SE-285`[1]) %>%
mutate(SampleID = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D15")) %>%
mutate(type.SE = paste("SE-285 | r =", round(Ahr_SE.285, digits = 2))) %>%
as.data.frame()
AhrSE.286.rel = AhrSE.SE286 %>%
mutate(countRel.SE = `SE-286`/`SE-286`[1]) %>%
mutate(SampleID = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D15")) %>%
mutate(type.SE = paste("SE-286 | r =", round(Ahr_SE.286, digits = 2))) %>%
as.data.frame()
## Plotting
Ahr_SE_Plot_Ad = ggplot(AhrCounts.Ad.rel, aes(x = SampleID, y = countRel, group = 1)) +
geom_point(size = 5, color = "#FF6666") +
geom_smooth(data = AhrCounts.Ad.rel, aes(x = SampleID, y = countRel,
linetype = "Ahr mRNA"),
method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
geom_point(data = AhrSE.283.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#FF6666", size = 5,
shape = 15) +
geom_point(data = AhrSE.284.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#FF6666", size = 5,
shape = 16) +
geom_point(data = AhrSE.285.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#FF6666", size = 5,
shape = 17) +
geom_point(data = AhrSE.286.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#FF6666", size = 5,
shape = 19) +
geom_smooth(data = AhrSE.283.rel, aes(x = SampleID, y = countRel.SE, linetype = "SE-283 | r = 0.99"),
method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
geom_smooth(data = AhrSE.284.rel, aes(x = SampleID, y = countRel.SE, linetype = "SE-284 | r = 0.98"),
method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
geom_smooth(data = AhrSE.285.rel, aes(x = SampleID, y = countRel.SE, linetype = "SE-285 | r = 0.98"),
method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
geom_smooth(data = AhrSE.286.rel, aes(x = SampleID, y = countRel.SE, linetype = "SE-286 | r = 0.99"),
method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
labs(x = "Time [Days]", y = "Signal relative to undifferentiated ST2 cells",
title = "Adipocyte differentiation") +
theme_classic() +
theme(axis.line = element_line(colour = "black", size = 1, linetype = "solid"),
axis.text.x = element_text(face = "bold", size = 20, colour = "black"),
axis.text.y = element_text(face = "bold", size = 20, colour = "black"),
axis.title.x = element_text(face = "bold", size = 20),
axis.title.y = element_text(face = "bold", size = 20),
legend.position = c(0.8, 0.8), legend.text = element_text(size = 20, face = "bold"),
plot.title = element_text(face = "bold", size = 20, hjust = 0.5)) +
scale_linetype_manual(name = "", values = c("solid", "dashed", "dotted", "dotdash", "longdash")) +
scale_x_discrete(breaks = c("Msc-D0", "A-D1", "A-D3", "A-D5", "A-D9", "A-D15"),
labels = c("D0", "D1", "D3", "D5", "D9", "D15")) +
ylim(0, 1.5)
print(Ahr_SE_Plot_Ad)
pdf("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/12042017-Ahr-Ad-SE-Fig4B.pdf", width = 8, height = 8)
Ahr_SE_Plot_Ad
dev.off()
#################################### Ahr figure 4 - se and mRNA plot - Osteo ############################################
## Ahr plot (mRNA and SE) only for Ad for Fig3 in the paper##
AhrCounts.Ob = AhrCounts[c(1:3, 19:33),] ## Select only the count for adipo
rel = "Msc-D0"
AhrCounts.Ob.rel = AhrCounts.Ob %>%
mutate(countRel = count/count[SampleID == rel]) %>%
mutate(type = "Ahr mRNA") ## Normalize the count to Day0 and adda column specifying that it is mRNA samples
AhrSE = read_delim("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/05042017-SEMapOsteoMERGEDFinCounts.txt",
delim = "\t") ## Load the count for the meta SE
OsteoSENm = c("SE_ID", "Msc-D0", "O-D1", "O-D3", "O-D5", "O-D9", "A-D15")
colnames(AhrSE) = OsteoSENm
AhrSE.Ob.SE283 = filter(AhrSE, SE_ID == "SE-283") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame() %>%
head() ## Take only the counts for SE-283 which targets Ahr
AhrSE.Ob.SE284 = filter(AhrSE, SE_ID == "SE-284") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame() %>%
head() ## Take only the counts for SE-284 which targets Ahr
AhrSE.Ob.SE285 = filter(AhrSE, SE_ID == "SE-285") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame() %>%
head() ## Take only the counts for SE-285 which targets Ahr
AhrSE.Ob.SE286 = filter(AhrSE, SE_ID == "SE-286") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame() %>%
head() ## Take only the counts for SE-286 which targets Ahr
## Load the Pearson correlation between Ahr mRNA and their SEs ##
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/11042017-PearsonCor-Ahr-Ob-SE283.RData")
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/11042017-PearsonCor-Ahr-Ob-SE284.RData")
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/11042017-PearsonCor-Ahr-Ob-SE285.RData")
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/11042017-PearsonCor-Ahr-Ob-SE286.RData")
Ahr_Ob_SE.283
Ahr_Ob_SE.284
Ahr_Ob_SE.285
Ahr_Ob_SE.286
AhrSE.Ob.283.rel = AhrSE.Ob.SE283 %>%
mutate(countRel.SE = `SE-283`/`SE-283`[1]) %>%
mutate(SampleID = c("Msc-D0", "O-D1", "O-D3", "O-D5", "O-D9","O-D15")) %>%
mutate(type.SE = paste("SE-283 | r =", round(Ahr_Ob_SE.283, digits = 2))) %>%
as.data.frame()
AhrSE.Ob.284.rel = AhrSE.Ob.SE284 %>%
mutate(countRel.SE = `SE-284`/`SE-284`[1]) %>%
mutate(SampleID = c("Msc-D0", "O-D1", "O-D3", "O-D5", "O-D9","O-D15")) %>%
mutate(type.SE = paste("SE-284 | r =", round(Ahr_Ob_SE.284, digits = 2))) %>%
as.data.frame()
AhrSE.Ob.285.rel = AhrSE.Ob.SE285 %>%
mutate(countRel.SE = `SE-285`/`SE-285`[1]) %>%
mutate(SampleID = c("Msc-D0", "O-D1", "O-D3", "O-D5", "O-D9","O-D15")) %>%
mutate(type.SE = paste("SE-285 | r =", round(Ahr_Ob_SE.285, digits = 2))) %>%
as.data.frame()
AhrSE.Ob.286.rel = AhrSE.Ob.SE286 %>%
mutate(countRel.SE = `SE-286`/`SE-286`[1]) %>%
mutate(SampleID = c("Msc-D0", "O-D1", "O-D3", "O-D5", "O-D9","O-D15")) %>%
mutate(type.SE = paste("SE-286 | r =", round(Ahr_Ob_SE.286, digits = 2))) %>%
as.data.frame()
## Plotting
Ahr_SE_Plot_Ob = ggplot(AhrCounts.Ob.rel, aes(x = SampleID, y = countRel, group = 1)) +
geom_point(size = 5, color = "#0066CC") +
geom_smooth(data = AhrCounts.Ob.rel, aes(x = SampleID, y = countRel,
linetype = "Ahr mRNA"),
method = "loess", se = FALSE, size = 1.5, color = "#0066CC") +
geom_point(data = AhrSE.Ob.283.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#0066CC", size = 5,
shape = 15) +
geom_point(data = AhrSE.Ob.284.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#0066CC", size = 5,
shape = 16) +
geom_point(data = AhrSE.Ob.285.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#0066CC", size = 5,
shape = 17) +
geom_point(data = AhrSE.Ob.286.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#0066CC", size = 5,
shape = 19) +
geom_smooth(data = AhrSE.Ob.283.rel, aes(x = SampleID, y = countRel.SE, linetype = "SE-283 | r = 0.98"),
method = "loess", se = FALSE, size = 1.5, color = "#0066CC") +
geom_smooth(data = AhrSE.Ob.284.rel, aes(x = SampleID, y = countRel.SE, linetype = "SE-284 | r = 0.96"),
method = "loess", se = FALSE, size = 1.5, color = "#0066CC") +
geom_smooth(data = AhrSE.Ob.285.rel, aes(x = SampleID, y = countRel.SE, linetype = "SE-285 | r = 0.98"),
method = "loess", se = FALSE, size = 1.5, color = "#0066CC") +
geom_smooth(data = AhrSE.Ob.286.rel, aes(x = SampleID, y = countRel.SE, linetype = "SE-286 | r = 0.95"),
method = "loess", se = FALSE, size = 1.5, color = "#0066CC") +
labs(x = "Time [Days]", y = "Signal relative to undifferentiated ST2 cells",
title = "Osteoblast differentiation") +
theme_classic() +
theme(axis.line = element_line(colour = "black", size = 1, linetype = "solid"),
axis.text.x = element_text(face = "bold", size = 20, colour = "black"),
axis.text.y = element_text(face = "bold", size = 20, colour = "black"),
axis.title.x = element_text(face = "bold", size = 20),
axis.title.y = element_text(face = "bold", size = 20),
legend.position = c(0.8, 0.8), legend.text = element_text(size = 20, face = "bold"),
plot.title = element_text(face = "bold", size = 20, hjust = 0.5)) +
scale_linetype_manual(name = "", values = c("solid", "dashed", "dotted", "dotdash", "longdash")) +
scale_x_discrete(breaks = c("Msc-D0", "O-D1", "O-D3", "O-D5", "O-D9", "O-D15"),
labels = c("D0", "D1", "D3", "D5", "D9", "D15")) +
ylim(0, 1.5)
print(Ahr_SE_Plot_Ob)
pdf("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/12042017-Ahr-Ob-SE-Fig4B.pdf", width = 8, height = 8)
Ahr_SE_Plot_Ob
dev.off()
#################################### Glis1 figure 4 - se and mRNA plot - Adipo ############################################
## Glis1 plot (mRNA and SE) only for Ad for Fig4 in the paper##
Glis1Counts.Ad = Glis1Counts[c(1:18),] ## Select only the count for adipo
rel = "ST2-D0"
Glis1Counts.Ad.rel = Glis1Counts.Ad %>%
mutate(countRel = count/count[SampleID == rel]) %>%
mutate(type = "Glis1 mRNA") ## Normalize the count to Day0 and adda column specifying that it is mRNA samples
Glis1SE = read_delim("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/05042017-SEMapAdipoMERGEDFinCounts.txt",
delim = "\t") ## Load the count for the meta SE
AdipoSENm = c("SE_ID", "Msc-D0", "A-D1", "A-D3", "A-D5", "A-D15")
colnames(Glis1SE) = AdipoSENm
Glis1SE.SE831 = filter(Glis1SE, SE_ID == "SE-831") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame() %>%
head() ## Take only the counts for SE-831 which targets Glis1
## Load the Pearson correlation between Glis1 mRNA and its SE ##
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/02022018-PearsonCor-Glis1-SE831.RData")
Glis1_SE.831
Glis1SE.831.rel = Glis1SE.SE831 %>%
mutate(countRel.SE = `SE-831`/`SE-831`[1]) %>%
mutate(SampleID = c("ST2-D0", "A-D1", "A-D3", "A-D5", "A-D15")) %>%
mutate(type.SE = paste("SE-831 | r =", round(Glis1_SE.831, digits = 2))) %>%
as.data.frame()
## Plot together Glis1 mRNA (normalized count) and its SE
Glis1_SE_Plot_Ad = ggplot(Glis1Counts.Ad.rel, aes(x = SampleID, y = countRel, group = 1)) +
geom_point(size = 5, color = "#FF6666") +
geom_smooth(data = Glis1Counts.Ad.rel, aes(x = SampleID, y = countRel,
linetype = "Glis1 mRNA"),
method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
geom_point(data = Glis1SE.831.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#FF6666", size = 5,
shape = 15) +
geom_smooth(data = Glis1SE.831.rel, aes(x = SampleID, y = countRel.SE, linetype = "SE-831 | r = 0.99"),
method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
labs(x = "Time [Days]", y = "Signal relative to undifferentiated ST2 cells",
title = "Adipocyte differentiation") +
theme_classic() +
theme(axis.line = element_line(colour = "black", size = 1, linetype = "solid"),
axis.text.x = element_text(face = "bold", size = 30, colour = "black"),
axis.text.y = element_text(face = "bold", size = 30, colour = "black"),
axis.title.x = element_text(face = "bold", size = 30),
axis.title.y = element_text(face = "bold", size = 30),
legend.position = c(0.8, 0.8), legend.text = element_text(size = 30, face = "bold"),
plot.title = element_text(face = "bold", size = 30, hjust = 0.5)) +
scale_linetype_manual(name = "", values = c("solid", "dotted")) +
scale_x_discrete(breaks = c("ST2-D0", "A-D1", "A-D3", "A-D5", "A-D9", "A-D15"),
labels = c("D0", "D1", "D3", "D5", "D9", "D15")) +
ylim(0, 1.5)
print(Glis1_SE_Plot_Ad)
pdf("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/Figures/Figure6/15032018-Glis1-Ad-SE-Fig6C-biggerFont.pdf", width = 10, height = 10)
Glis1_SE_Plot_Ad
dev.off()
#################################### Glis1 figure 4 - se and mRNA plot - Osteo ############################################
## Glis1 plot (mRNA and SE) only for Ob for Fig4 in the paper##
Glis1Counts.Ob = Glis1Counts[c(1:3, 19:33),] ## Select only the count for osteo
rel = "ST2-D0"
Glis1Counts.Ob.rel = Glis1Counts.Ob %>%
mutate(countRel = count/count[SampleID == rel]) %>%
mutate(type = "Glis1 mRNA") ## Normalize the count to Day0 and adda column specifying that it is mRNA samples
Glis1SE = read_delim("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/05042017-SEMapOsteoMERGEDFinCounts.txt",
delim = "\t") ## Load the count for the meta SE
OsteoSENm = c("SE_ID", "Msc-D0", "O-D1", "O-D3", "O-D5", "O-D9", "A-D15")
colnames(Glis1SE) = OsteoSENm
Glis1SE.Ob.SE831 = filter(Glis1SE, SE_ID == "SE-831") %>%
column_to_rownames("SE_ID") %>%
t() %>%
as_data_frame() %>%
head() ## Take only the counts for SE-831 which targets Glis1
## Load the Pearson correlation between Ahr mRNA and their SEs ##
load("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/STEM/02022018-PearsonCor-Glis1-Ob-SE831.RData")
Glis1_SE.831_Ob
Glis1SE.Ob.831.rel = Glis1SE.Ob.SE831 %>%
mutate(countRel.SE = `SE-831`/`SE-831`[1]) %>%
mutate(SampleID = c("ST2-D0", "O-D1", "O-D3", "O-D5", "O-D9","O-D15")) %>%
mutate(type.SE = paste("SE-831 | r =", round(Glis1_SE.831_Ob, digits = 2))) %>%
as.data.frame()
## Plotting
Glis1_SE_Plot_Ob = ggplot(Glis1Counts.Ob.rel, aes(x = SampleID, y = countRel, group = 1)) +
geom_point(size = 5, color = "#0066CC") +
geom_smooth(data = Glis1Counts.Ob.rel, aes(x = SampleID, y = countRel,
linetype = "Glis1 mRNA"),
method = "loess", se = FALSE, size = 1.5, color = "#0066CC") +
geom_point(data = Glis1SE.Ob.831.rel, aes(x = SampleID, y = countRel.SE, group = 1), color = "#0066CC", size = 5,
shape = 15) +
geom_smooth(data = Glis1SE.Ob.831.rel, aes(x = SampleID, y = countRel.SE, linetype = "SE-831 | r = 0.95"),
method = "loess", se = FALSE, size = 1.5, color = "#0066CC") +
labs(x = "Time [Days]", y = "Signal relative to undifferentiated ST2 cells",
title = "Osteoblast differentiation") +
theme_classic() +
theme(axis.line = element_line(colour = "black", size = 1, linetype = "solid"),
axis.text.x = element_text(face = "bold", size = 30, colour = "black"),
axis.text.y = element_text(face = "bold", size = 30, colour = "black"),
axis.title.x = element_text(face = "bold", size = 30),
axis.title.y = element_text(face = "bold", size = 30),
legend.position = c(0.8, 0.8), legend.text = element_text(size = 30, face = "bold"),
plot.title = element_text(face = "bold", size = 30, hjust = 0.5)) +
scale_linetype_manual(name = "", values = c("solid", "dotted")) +
scale_x_discrete(breaks = c("ST2-D0", "O-D1", "O-D3", "O-D5", "O-D9", "O-D15"),
labels = c("D0", "D1", "D3", "D5", "D9", "D15")) +
ylim(0, 1.5)
print(Glis1_SE_Plot_Ob)
pdf("Y:/Deborah.GERARD/Gerard et al. - Manuscript 1/Figures/Figure6/15032018-Glis1-Ob-SE-Fig6C-biggerFont.pdf", width = 10, height = 10)
Glis1_SE_Plot_Ob
dev.off()
## Plot the Ahr and Notch (1, 2, 3, 4) expression across TC for adipo adipo and osteo
# Notch3 - Adipo
Notch3Counts.Ad = Notch3Counts[c(1:18),] ## Select only the count for adipo
AhrCounts.Ad
rel = "Msc-D0"
Notch3Counts.Ad.rel = Notch3Counts.Ad %>%
mutate(countRel = count/count[SampleID == rel]) %>%
mutate(type = "Notch3 mRNA") ## Normalize the count to Day0 and adda column specifying that it is mRNA samples
AhrCounts.Ad.rel
Notch3_Plot_Ad = ggplot(Notch3Counts.Ad.rel, aes(x = SampleID, y = countRel, group = 1)) +
geom_point(size = 5, color = "#FF6666") +
geom_smooth(data = Notch3Counts.Ad.rel, aes(x = SampleID, y = countRel,
linetype = "Notch3 mRNA"),
method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
# geom_point(data = Notch3Counts.Ad.rel, aes(x = SampleID, y = countRel, group = 1), color = "#FF6666", size = 5, shape = 17) +
# geom_smooth(data = Notch3Counts.Ad.rel, aes(x = SampleID, y = countRel, linetype = "Notch3 mRNA"),
# method = "loess", se = FALSE, size = 1.5, color = "#FF6666") +
labs(x = "Time [Days]", y = "Signal relative to undifferentiated ST2 cells",
title = "Adipocyte differentiation") +
theme_classic() +
theme(axis.line = element_line(colour = "black", size = 1, linetype = "solid"),
axis.text.x = element_text(face = "bold", size = 20, colour = "black"),
axis.text.y = element_text(face = "bold", size = 20, colour = "black"),