-
Notifications
You must be signed in to change notification settings - Fork 61
/
actionability_functions_msi_tmb_manuscript_R.r
1058 lines (937 loc) · 47.4 KB
/
actionability_functions_msi_tmb_manuscript_R.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
#!/usr/bin/env Rscript
### Annotate IMPACT files using oncokb-annotator ###
### Chakravarty D, Gao J, Phillips SM, et al. OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol. 2017;2017:PO.17.00011. doi:10.1200/PO.17.00011 ###
### Actionability Functions ###
# Collection of functions use to clean, process, and analysis actionability data
### Input parameters
# cna_df: OncoKB annotated IMPACT CNA data
# mut_df: OncoKB annotated IMPACT mutation data
# fus_df: OncoKB annotated IMPACT fusion data
# clin_df: OncoKB annotated IMPACT clinical sample data
# data_freeze: Sample data, must include *SAMPLE_ID*, group_col, and consent_col
# group_col: Column name for the groups (cancer types)
# consent_col: Columns name for 12-245 Part C consent status (YES/NO/NA)
# path_df: Pathway data, must include gene and correpsonding pathway columns (in that order)
# tsg_list: List of tumor suppresor genes (no header)
# fusion_list: List of genes to isolate from fusion partners (ie. NTRK1-LMNA fusion becomes NTRK1 fusion)
# prop_level_df: Output from action_levels_barplot_fun actionability_levels_barplot_table.txt
# alt_final_df: Output from action_alterations_barplot_fun actionability_master_alterations_table.txt
# alt_min: Minimum alteration percentage required in one cancer type to visualize alteration on main plot (default 1)
# status: Include only somatic mutations, only germline mutations, or both (options: somatic, germline, both)
# gene_order: List of genes for gene order, genes not included will be ordered by pathway following this list (no header)
# only_highest_level: TRUE/FALSE, If true only visualize the highest level of evidence genes in main plot
# msi_tmb_status: TRUE/FALSE, If true include Level 1 MSI/TMB status in actionability barplot, removes MSI/TMB in all other plots
# msi_tmb_df: MSI/TMB annotated file (atypical alterations), visualizes MSI/TMB level 1 for actionability barplot,
# removes all samples in file for all other plots
###
# Load libraries
if (!require('tidyverse')) install.packages('tidyverse'); library(tidyverse)
if (!require('cowplot')) install.packages('cowplot'); library(cowplot)
if (!require('reshape2')) install.packages('reshape2'); library(reshape2)
# Collapse oncogenic alterations
collapse_oncogenic <- function(data_frame, sample_column, alteration_type){
data_frame[, ] <- lapply(data_frame[, ], as.character)
#data_frame_samp <- data_frame %>% dplyr::filter(oncogenic == "Oncogenic") #### TESTING
data_frame_samp <- data_frame[grepl("Oncogenic", data_frame$oncogenic),]
colnames(data_frame_samp)[which(names(data_frame_samp) == sample_column)] <- "SAMPLE_ID"
data_frame_samp <- aggregate(oncogenic ~ SAMPLE_ID, data = data_frame_samp, toString, na.omit = TRUE)
colnames(data_frame_samp)[2] <- paste0(alteration_type, "_oncogenic")
return(data_frame_samp)
}
# Create frequency data frame by group for subgroup
freq_dataframe <- function(data_frame, split_group, percentage_group){
# Split group is the column to group by
# Percentage group is the column to calculate the percentage for, by group
df <- data_frame %>%
dplyr::select(percentage_group, split_group) %>%
group_by_(split_group, percentage_group, .drop = F) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(freq = n / sum(n)) %>%
ungroup()
return(df)
}
# Create actionability level barplot
action_levels_barplot_fun <- function(cna_df, mut_df, fus_df, clin_df, data_freeze,
status = c("somatic", "germline", "both"),
group_col,
consent_col,
msi_tmb_status,
msi_tmb_df){
# Read in data
cna_df <- read.delim(cna_df)
fus_df <- read.delim(fus_df)
mut_df <- read.delim(mut_df)
clin_df <- read.delim(clin_df)
data_freeze <- read.delim(data_freeze)
data_freeze$SAMPLE_ID <- as.character(data_freeze$SAMPLE_ID)
######
# Optional MSI/TMB addition
if (msi_tmb_status == TRUE){
msi_tmb_df <- read.delim(msi_tmb_path)
msi_tmb_df <- msi_tmb_df %>%
dplyr::select(SAMPLE_ID) %>%
mutate_if(is.factor, as.character) %>%
mutate(Highest_level = "LEVEL_1_MSI-H_TMB-H") %>%
distinct()
} else {
msi_tmb_df <- data.frame(SAMPLE_ID = character(), Highest_level = character())
}
#####
# Clean & filter clinical data
# Add group column
clin_df <- clin_df %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID),
HIGHEST_LEVEL = as.character(HIGHEST_LEVEL)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID) %>%
left_join(data_freeze[,c("SAMPLE_ID", group_col, consent_col)], by = c("SAMPLE_ID")) %>%
mutate(HIGHEST_LEVEL = ifelse(SAMPLE_ID %in% msi_tmb_df$SAMPLE_ID, "LEVEL_1_MSI-H_TMB-H", HIGHEST_LEVEL))
group_col_dup <- paste0(group_col, ".y")
colnames(clin_df)[which(names(clin_df) == group_col_dup)] <- group_col
colnames(clin_df)[which(names(clin_df) == consent_col)] <- "consent"
# Clean, filter, rename genomic data
# Fix column names if upper
# Filter for columns of interest
col_list <- c("SAMPLE_ID", "oncogenic", "LEVEL_1", "LEVEL_2", "LEVEL_3A", "LEVEL_3B", "LEVEL_4", "Highest_level")
# Fusions
fus_df <- fus_df %>%
dplyr::rename_all(recode,
Tumor_Sample_Barcode = "SAMPLE_ID",
HIGHEST_LEVEL = "Highest_level",
ONCOGENIC = "oncogenic") %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID),
Highest_level = as.character(Highest_level)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID) %>%
mutate(Highest_level = ifelse(SAMPLE_ID %in% msi_tmb_df$SAMPLE_ID, "LEVEL_1_MSI-H_TMB-H", Highest_level)) %>%
dplyr::select(col_list)
# CNA
cna_df <- cna_df %>%
dplyr::rename_all(recode,
Tumor_Sample_Barcode = "SAMPLE_ID",
HIGHEST_LEVEL = "Highest_level",
ONCOGENIC = "oncogenic") %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID),
Highest_level = as.character(Highest_level)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID) %>%
mutate(Highest_level = ifelse(SAMPLE_ID %in% msi_tmb_df$SAMPLE_ID, "LEVEL_1_MSI-H_TMB-H", Highest_level)) %>%
dplyr::select(col_list)
# Mutations
mut_df <- mut_df %>%
dplyr::rename_all(recode,
Tumor_Sample_Barcode = "SAMPLE_ID",
HIGHEST_LEVEL = "Highest_level",
ONCOGENIC = "oncogenic") %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID),
Highest_level = as.character(Highest_level)) %>%
mutate(Highest_level = ifelse(SAMPLE_ID %in% msi_tmb_df$SAMPLE_ID, "LEVEL_1_MSI-H_TMB-H", Highest_level)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID)
# Filter for status
if (status == "somatic") {
mut_somatic_df <- filter(mut_df, Mutation_Status != "GERMLINE" | is.na(Mutation_Status) == T)
mut_germ_df <- filter(mut_df, Mutation_Status == "GERMLINE")
mut_df <- mut_somatic_df[col_list]
} else if (status == "germline") {
clin_df <- filter(clin_df, consent == "YES")
mut_germ_df <- filter(mut_df, Mutation_Status == "GERMLINE")
mut_germ_df <- mut_germ_df[mut_germ_df$SAMPLE_ID %in% clin_df$SAMPLE_ID,]
mut_df <- mut_germ_df[col_list]
} else {
mut_df <- mut_df[col_list]
clin_germ_df <- filter(clin_df, consent == "YES" | is.na(consent) == T)
}
# Create master levels data frame for somatic
master_df <- rbind(cna_df, fus_df)
master_df <- rbind(master_df, mut_df)
master_df <- master_df %>%
dplyr::select(SAMPLE_ID, Highest_level) %>%
filter(Highest_level != "") %>%
mutate_if(is.factor, as.character) %>%
group_by(SAMPLE_ID) %>%
dplyr::arrange(Highest_level) %>%
dplyr::slice(1) %>%
ungroup()
# Collapse oncogenic alterations
cna_df <- collapse_oncogenic(cna_df, "SAMPLE_ID", "cna")
fus_df <- collapse_oncogenic(fus_df, "SAMPLE_ID", "fus")
mut_df <- collapse_oncogenic(mut_df, "SAMPLE_ID", "mut")
# Filter if germline
if (status == "germline") {
clin_df <- left_join(clin_df, mut_germ_df[,c("SAMPLE_ID", "Highest_level")])
clin_df <- clin_df %>% mutate_if(is.factor, as.character)
clin_df$HIGHEST_LEVEL <- ifelse(clin_df$SAMPLE_ID %in% mut_germ_df$SAMPLE_ID, clin_df$Highest_level, "NO_LEVEL")
# Get list of sample with oncogenic alteration
onco_samp_list <- mut_df$SAMPLE_ID
} else if (status == "somatic") {
clin_df <- left_join(clin_df, master_df, by = "SAMPLE_ID")
clin_df <- clin_df %>% mutate_if(is.factor, as.character)
clin_df$HIGHEST_LEVEL <- ifelse(clin_df$SAMPLE_ID %in% mut_germ_df$SAMPLE_ID,
clin_df$Highest_level, clin_df$HIGHEST_LEVEL)
# Merge to make master oncogenic list of samples
all_df <- full_join(cna_df, fus_df, by = "SAMPLE_ID")
all_df <- full_join(all_df, mut_df, by = "SAMPLE_ID")
# Get list of sample with oncogenic alteration
onco_samp_list <- all_df$SAMPLE_ID
} else {
# Merge to make master oncogenic list of samples
all_df <- full_join(cna_df, fus_df, by = "SAMPLE_ID")
all_df <- full_join(all_df, mut_df, by = "SAMPLE_ID")
# Get list of sample with oncogenic alteration
onco_samp_list <- all_df$SAMPLE_ID
}
# Fill in the highest level blanks:
clin_df$HIGHEST_LEVEL <- as.character(clin_df$HIGHEST_LEVEL)
clin_df$HIGHEST_LEVEL[clin_df$HIGHEST_LEVEL == "" | is.na(clin_df$HIGHEST_LEVEL) == T] <- "NO_LEVEL"
clin_df$HIGHEST_LEVEL[(clin_df$SAMPLE_ID %in% onco_samp_list) & (clin_df$HIGHEST_LEVEL == "NO_LEVEL") ] <- "ONCOGENIC"
# For highest level of actionability, calculate the percentage of each level by subtype
prop_level_df <- freq_dataframe(clin_df, group_col, "HIGHEST_LEVEL")
# Set level order
level_order <- c("LEVEL_1_MSI-H_TMB-H","LEVEL_1", "LEVEL_2", "LEVEL_3A", "LEVEL_3B", "LEVEL_4", "ONCOGENIC", "NO_LEVEL")
prop_level_df$HIGHEST_LEVEL <- factor(prop_level_df$HIGHEST_LEVEL, levels = level_order)
# Add counts for labels
# Check the number of oncotree codes and their frequency
data_freeze <- data_freeze[data_freeze$SAMPLE_ID %in% clin_df$SAMPLE_ID,]
clin_oncotree_freq <- as.data.frame(table(data_freeze[,group_col]))
clin_oncotree_freq <- clin_oncotree_freq[order(clin_oncotree_freq$Freq, decreasing = T),]
colnames(clin_oncotree_freq)[1] <- group_col
prop_level_df <- left_join(prop_level_df, clin_oncotree_freq, by = group_col)
if (status == "both") {
data_freeze_2 <- data_freeze[data_freeze$SAMPLE_ID %in% clin_germ_df$SAMPLE_ID,]
clin_oncotree_freq_germ <- as.data.frame(table(data_freeze_2[,group_col]))
colnames(clin_oncotree_freq_germ)[1] <- group_col
prop_level_df <- left_join(prop_level_df, clin_oncotree_freq_germ, by = group_col)
prop_level_df$label <- apply(prop_level_df[ ,c(group_col, "Freq.x")], 1, paste0, collapse = " n=" )
prop_level_df$label <- apply(prop_level_df[ ,c("label", "Freq.y")], 1, paste0, collapse = ":" )
} else {
prop_level_df$label <- apply(prop_level_df[ ,c(group_col, "Freq")], 1, paste0, collapse = " n=" )
}
# # Arrange by frequency of actionability
# prop_level_df <- prop_level_df %>%
# arrange(HIGHEST_LEVEL, desc(freq))
# Arrange by frequency of combined top 4 levels of actionability
prop_level_df_order <- prop_level_df %>%
filter(HIGHEST_LEVEL %in% c("LEVEL_1_MSI-H_TMB-H", "LEVEL_1", "LEVEL_2", "LEVEL_3A")) %>%
group_by(CANCER_TYPE) %>%
dplyr::mutate(sum_freq = sum(freq)) %>%
right_join(prop_level_df) %>%
dplyr::arrange(desc(sum_freq), HIGHEST_LEVEL, desc(freq)) %>%
dplyr::rename(total_count = Freq) %>%
mutate(CANCER_TYPE = factor(CANCER_TYPE, levels = unique(CANCER_TYPE)))
# Save
write.table(prop_level_df_order, "./actionability_levels_barplot_table.txt", sep = "\t", row.names = F, quote = F)
# Set orders
cancer_order <- unique(prop_level_df_order$label)
# Plot breakdown of levels of evidence as a percentage by sarcoma subtype
percent_bar_plot <- ggplot(prop_level_df_order, aes(y = freq, x = label, fill = HIGHEST_LEVEL)) +
geom_col(position = position_stack(reverse = TRUE)) +
theme(axis.text.x = element_text(angle = 45, hjust = 0, size = 6),
axis.text.y = element_text(size = 6),
axis.ticks.x = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=1),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.title = element_text(size = 7),
legend.text = element_text(size = 6),
legend.key.size = unit(0.4, "cm"),
axis.title.x = element_blank(),
plot.margin = unit(c(0.05, 0.05, 0.1, 0.05), "cm"),
legend.justification="left",
legend.margin=margin(0,0,0,0),
legend.box.margin=margin(-10,0,-10,-5)) +
scale_y_continuous(limits=c(0, 1.00), expand = c(0, 0)) +
scale_fill_manual(values = c("#88E281","#33A02C", "#1F78B4", "#984EA3", "#BE98CE", "#a8a8a8", "#ffdab9", "gray90"),
limits = c("LEVEL_1_MSI-H_TMB-H","LEVEL_1", "LEVEL_2", "LEVEL_3A", "LEVEL_3B", "LEVEL_4", "ONCOGENIC", "NO_LEVEL"),
labels = c("LEVEL 1 MSI/TMB-H","LEVEL 1", "LEVEL 2", "LEVEL 3A", "LEVEL 3B", "LEVEL 4", "ONCOGENIC", "NO LEVEL")) +
scale_x_discrete(position = "top",
limits = cancer_order) +
ylab("Frequency") +
labs(fill = "Highest Level of Evidence")
return(percent_bar_plot)
}
# Create actionability alteration barplot
action_alterations_barplot_fun <- function(cna_df, mut_df, fus_df, clin_df, data_freeze,
status = c("somatic", "germline", "both"),
group_col, consent_col,
prop_level_df = "./actionability_levels_barplot_table.txt",
only_highest_level = F,
msi_tmb_status,
msi_tmb_df){
# Read in data
cna_df <- read.delim(cna_df)
fus_df <- read.delim(fus_df)
mut_df <- read.delim(mut_df)
clin_df <- read.delim(clin_df)
data_freeze <- read.delim(data_freeze)
prop_level_df <- read.delim(prop_level_df)
# Set order
cancer_order_other <- as.character(unique(prop_level_df[,c(group_col)]))
######
# Optional MSI/TMB addition
if (msi_tmb_status == TRUE){
msi_tmb_df <- read.delim(msi_tmb_path)
msi_tmb_df <- msi_tmb_df %>%
dplyr::select(SAMPLE_ID) %>%
mutate_if(is.factor, as.character) %>%
mutate(Highest_level = "LEVEL_1_MSI-H_TMB-H") %>%
distinct()
data_freeze <- filter(data_freeze, !SAMPLE_ID %in% msi_tmb_df$SAMPLE_ID)
}
#####
# Clean & filter clinical data
# Add group column
clin_df <- clin_df %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID) %>%
left_join(data_freeze[,c("SAMPLE_ID", group_col, consent_col)], by = c("SAMPLE_ID"))
group_col_dup <- paste0(group_col, ".y")
colnames(clin_df)[which(names(clin_df) == group_col_dup)] <- group_col
colnames(clin_df)[which(names(clin_df) == consent_col)] <- "consent"
# Clean, filter, rename genomic data
# Fix column names if upper
fus_df <- fus_df %>%
dplyr::rename_all(recode,
Tumor_Sample_Barcode = "SAMPLE_ID",
HIGHEST_LEVEL = "Highest_level",
ONCOGENIC = "oncogenic") %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID) %>%
mutate(Fusion = gsub(" fusion", "", Fusion)) %>%
mutate(Fusion = gsub(" - Archer", "", Fusion)) %>%
dplyr::select(SAMPLE_ID, oncogenic, Highest_level, Fusion) %>%
rowwise() %>%
mutate(Fusion = ifelse(grepl("intragenic", Fusion), Fusion,
paste(sort(unlist(strsplit(Fusion, "-", fixed = TRUE))), collapse = "-"))) %>%
ungroup() %>%
distinct() %>%
dplyr::select(-Fusion) %>%
mutate(ALTERATION = "Fusion") %>%
filter(grepl("Oncogenic", oncogenic) == T, is.na(Highest_level) == F & Highest_level != "") %>%
dplyr::select(SAMPLE_ID, ALTERATION, oncogenic, Highest_level)
# CNA
cna_df <- cna_df %>%
dplyr::rename_all(recode,
Tumor_Sample_Barcode = "SAMPLE_ID",
HIGHEST_LEVEL = "Highest_level",
ONCOGENIC = "oncogenic") %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID) %>%
dplyr::select(SAMPLE_ID, ALTERATION, oncogenic, Highest_level) %>%
filter(grepl("Oncogenic", oncogenic) == T, is.na(Highest_level) == F & Highest_level != "") %>%
dplyr::select(SAMPLE_ID, ALTERATION, oncogenic, Highest_level)
# Mutations
# Filter for status
if (status == "somatic") {
mut_df <- filter(mut_df, Mutation_Status != "GERMLINE" | is.na(Mutation_Status) == T)
} else if (status == "germline") {
clin_df <- filter(clin_df, consent == "YES")
mut_df <- filter(mut_df, Mutation_Status == "GERMLINE")
mut_df <- mut_df[mut_df$SAMPLE_ID %in% clin_df$SAMPLE_ID,]
}
# Clean & Filter
mut_df <- mut_df %>%
dplyr::rename_all(recode,
Tumor_Sample_Barcode = "SAMPLE_ID",
HIGHEST_LEVEL = "Highest_level",
ONCOGENIC = "oncogenic") %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID) %>%
dplyr::select(SAMPLE_ID, oncogenic, Highest_level) %>%
filter(grepl("Oncogenic", oncogenic) == T, is.na(Highest_level) == F & Highest_level != "") %>%
mutate(ALTERATION = "Mutation") %>%
dplyr::select(SAMPLE_ID, ALTERATION, oncogenic, Highest_level)
# rbind to create master alterations data frame
# Filter for status for mutation data frame
if (status == "somatic" | status == "both") {
alt_final <- rbind(cna_df, fus_df)
alt_final <- rbind(alt_final, mut_df)
} else if (status == "germline") {
alt_final <- mut_df
}
alt_final <- left_join(alt_final, data_freeze[,c("SAMPLE_ID", group_col)], by = "SAMPLE_ID")
# Save
write.table(alt_final, "actionability_master_alterations_table.txt", sep = "\t", row.names = F, quote = F)
########## optional select only the highest level ##########
if (only_highest_level == T){
alt_final <- alt_final %>%
left_join(dplyr::select(clin_df, SAMPLE_ID, HIGHEST_LEVEL), by = "SAMPLE_ID") %>%
mutate_if(is.factor, as.character) %>%
filter(HIGHEST_LEVEL == Highest_level)
}
###########
# Save
write.table(alt_final, "actionability_master_alterations_highest_level_table.txt", sep = "\t", row.names = F, quote = F)
# Calculate the percentage of each alteration by subtype
prop_alteration_df <- as.data.frame(freq_dataframe(alt_final, group_col, "ALTERATION"))
prop_alteration_df$freq[is.na(prop_alteration_df$freq)] <- 0
prop_alteration_df$ALTERATION <- factor(prop_alteration_df$ALTERATION,
levels = c("Amplification", "Deletion", "Fusion", "Mutation"))
prop_alteration_df$group <- factor(prop_alteration_df[,group_col],
levels = cancer_order_other)
# Save
write.table(prop_alteration_df, "actionability_alterations_barplot_table.txt", sep = "\t", row.names = F, quote = F)
# Plot for ACTIONABLE ALTERATIONS
alt_freq_bar_plot <- ggplot(prop_alteration_df, aes(y = freq, x = group, fill = ALTERATION)) +
geom_col(position = position_stack(reverse = FALSE)) +
ylab("Frequency") +
labs(fill = "Actionable Alteration") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.y = element_text(size = 6),
panel.border = element_rect(colour = "black", fill=NA, size=1),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 6),
legend.key.size = unit(0.4, "cm"),
axis.title.x = element_blank(),
plot.margin = unit(c(0.05, 0.05, 0.05, 0.05), "cm"),
legend.justification="left",
legend.margin=margin(0,0,0,0),
legend.box.margin=margin(-10,0,-10,-5)) +
scale_y_continuous(limits=c(0, 1.00), expand = c(0, 0)) +
ylab("Frequency") +
scale_fill_manual(values = c("#A11111", "#02488E", "#660066", "#037903"),
limits = c("Amplification", "Deletion", "Fusion", "Mutation"),
labels = c("Amplification", "Deletion", "Fusion", "Mutation")) +
scale_x_discrete(limits = cancer_order_other)
alt_freq_bar_plot
return(alt_freq_bar_plot)
}
# Create actionability count barplot
action_count_barplot_fun <- function(clin_df, data_freeze, group_col,
prop_level_df = "./actionability_levels_barplot_table.txt",
status = c("somatic", "germline", "both"),
consent_col,
alt_final_df = "./actionability_master_alterations_table.txt",
msi_tmb_status,
msi_tmb_df){
# Read in files
prop_level_df <- read.delim(prop_level_df)
alt_final <- read.delim(alt_final_df)
clin_df <- read.delim(clin_df)
data_freeze <- read.delim(data_freeze)
# Filter for samples in data freeze and clean consent column
data_freeze$SAMPLE_ID <- as.character(data_freeze$SAMPLE_ID)
######
# Optional MSI/TMB addition
if (msi_tmb_status == TRUE){
msi_tmb_df <- read.delim(msi_tmb_path)
msi_tmb_df <- msi_tmb_df %>%
dplyr::select(SAMPLE_ID) %>%
mutate_if(is.factor, as.character) %>%
mutate(Highest_level = "LEVEL_1_MSI-H_TMB-H") %>%
distinct()
data_freeze <- filter(data_freeze, !SAMPLE_ID %in% msi_tmb_df$SAMPLE_ID)
}
#####
# Clean
clin_df <- clin_df[as.character(clin_df$SAMPLE_ID) %in% data_freeze$SAMPLE_ID,]
colnames(data_freeze)[which(names(data_freeze) == consent_col)] <- "consent"
# Set order
cancer_order_other <- as.character(unique(prop_level_df[,c(group_col)]))
# Create data frame that counts the number of actionable oncogenic alterations
alt_final$alt_count <- 1
alt_final <- dplyr::select(alt_final, SAMPLE_ID, alt_count)
# Filter for status
if (status == "germline") {
clin_df <- clin_df[clin_df$SAMPLE_ID %in% as.character(filter(data_freeze, consent == "YES")$SAMPLE_ID),]
}
# Add in samples that don't have an actionable alteration
alt_final_none <- as.data.frame(clin_df[,c("SAMPLE_ID")])
colnames(alt_final_none)[1] <- "SAMPLE_ID"
alt_final_none$alt_count <- 0
alt_final <- rbind(alt_final, alt_final_none)
alt_final <- aggregate(alt_count ~ SAMPLE_ID, alt_final, sum)
# Add cancer subtypes to clinical data frame and create labels
alt_final <- left_join(alt_final, data_freeze[,c("SAMPLE_ID", group_col)], by = "SAMPLE_ID")
alt_final$label <- ifelse(alt_final$alt_count >= 3, "3+", alt_final$alt_count)
# Calculate the percentage of each count by subtype
prop_alt_count_df <- as.data.frame(freq_dataframe(alt_final, group_col, "label"))
prop_alt_count_df$freq[is.na(prop_alt_count_df$freq)] <- 0
# Set order
prop_alt_count_df$label <- factor(prop_alt_count_df$label, levels = c("0", "1", "2", "3+"))
prop_alt_count_df$group <- factor(prop_alt_count_df[,group_col],
levels = cancer_order_other)
# Save
write.table(prop_alt_count_df, "actionability_count_table.txt", sep = "\t", row.names = F, quote = F)
# Number of alterations plot
alt_per_num_prop_plot <- ggplot(prop_alt_count_df, aes(y = freq, x = group, fill = label)) +
geom_col(position = position_stack(reverse = FALSE)) +
ylab("Frequency") +
labs(fill = "# of Actionable Alterations") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.y = element_text(size = 6),
panel.border = element_rect(colour = "black", fill=NA, size=1),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 6),
legend.key.size = unit(0.4, "cm"),
axis.title.x = element_blank(),
plot.margin = unit(c(0.05, 0.05, 0.05, 0.05), "cm"),
legend.justification="left",
legend.margin=margin(0,0,0,0),
legend.box.margin=margin(-10,0,-10,-5)) +
scale_y_continuous(limits=c(0, 1.00), expand = c(0, 0)) +
scale_fill_manual(values = c("#F7E690", "#F7AA14", "#E17202" ,"#701C5A"),
limits = c("0", "1", "2", "3+"),
labels = c("0", "1", "2", "3+")) +
scale_x_discrete(limits = cancer_order_other)
alt_per_num_prop_plot
return(alt_per_num_prop_plot)
}
# Create actionability alterations main plot
action_main_fun <- function(cna_df, mut_df, fus_df, clin_df, data_freeze,
path_df,
tsg_list, fusion_list,
prop_level_df = "./actionability_levels_barplot_table.txt",
group_col,
consent_col,
alt_min = 1,
status = c("somatic", "germline", "both"),
gene_order,
only_highest_level = F,
msi_tmb_status,
msi_tmb_df,
include_oncogenic = F){
# Read in data
cna_df <- read.delim(cna_df)
fus_df <- read.delim(fus_df)
mut_df <- read.delim(mut_df)
clin_df <- read.delim(clin_df)
data_freeze <- read.delim(data_freeze)
tsg_df <- read.delim(tsg_list, header = F)
prop_level_df <- read.delim(prop_level_df)
# Set order
cancer_order_other <- as.character(unique(prop_level_df[,c(group_col)]))
# Clean and filter data
# Data freeze
colnames(data_freeze)[which(names(data_freeze) == group_col)] <- "cancer_type"
colnames(data_freeze)[which(names(data_freeze) == consent_col)] <- "consent"
data_freeze <- data_freeze %>%
mutate_if(is.factor, as.character)
# Optional MSI/TMB addition
if (msi_tmb_status == TRUE){
msi_tmb_df <- read.delim(msi_tmb_path)
msi_tmb_df <- msi_tmb_df %>%
dplyr::select(SAMPLE_ID) %>%
mutate_if(is.factor, as.character) %>%
mutate(Highest_level = "LEVEL_1_MSI-H_TMB-H") %>%
distinct()
data_freeze <- filter(data_freeze, !SAMPLE_ID %in% msi_tmb_df$SAMPLE_ID)
}
# Clinical
clin_df <- clin_df %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID)
# CNA
cna_df <- cna_df %>%
dplyr::rename_all(recode,
Tumor_Sample_Barcode = "SAMPLE_ID",
HIGHEST_LEVEL = "Highest_level",
ONCOGENIC = "oncogenic") %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID),
Highest_level = as.character(Highest_level)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID,
grepl("Oncogenic", oncogenic))
# Fusions
fus_df <- fus_df %>%
dplyr::rename_all(recode,
Tumor_Sample_Barcode = "SAMPLE_ID",
HIGHEST_LEVEL = "Highest_level",
ONCOGENIC = "oncogenic") %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID),
Highest_level = as.character(Highest_level)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID,
grepl("Oncogenic", oncogenic))
# Mutations
mut_df <- mut_df %>%
dplyr::rename_all(recode,
Tumor_Sample_Barcode = "SAMPLE_ID",
HIGHEST_LEVEL = "Highest_level",
ONCOGENIC = "oncogenic") %>%
mutate(SAMPLE_ID = as.character(SAMPLE_ID),
Highest_level = as.character(Highest_level)) %>%
filter(SAMPLE_ID %in% data_freeze$SAMPLE_ID,
grepl("Oncogenic", oncogenic))
# Set tumor suppresor list
tumor_suppressor_list <- as.character(tsg_df$V1)
# Make count data frame - consider somatic/germline/both
if (status == "germline") {
data_freeze <- filter(data_freeze, consent == "YES")
clin_oncotree_freq <- as.data.frame(table(data_freeze$cancer_type))
colnames(clin_oncotree_freq)[] <- c("cancer_type", "total_count")
mut_df <- mut_df[mut_df$SAMPLE_ID %in% data_freeze$SAMPLE_ID,]
} else if (status == "somatic") {
clin_oncotree_freq <- as.data.frame(table(data_freeze$cancer_type))
colnames(clin_oncotree_freq)[] <- c("cancer_type", "total_count")
} else {
clin_oncotree_freq <- as.data.frame(table(data_freeze$cancer_type))
data_freeze_1 <- filter(data_freeze, consent == "YES")
data_freeze_1$SAMPLE_ID <- as.character(data_freeze_1$SAMPLE_ID)
clin_oncotree_freq_1 <- as.data.frame(table(data_freeze_1$cancer_type))
clin_oncotree_freq <- left_join(clin_oncotree_freq, clin_oncotree_freq_1, by = "Var1")
colnames(clin_oncotree_freq)[] <- c("cancer_type", "total_count", "germ_count")
# Remove samples that have germline alterations but ARE NOT Part C consented
remove_list <- intersect(filter(mut_df, Mutation_Status == "GERMLINE")$SAMPLE_ID,
filter(data_freeze, consent == "NO")$SAMPLE_ID)
mut_df <- mut_df[!(mut_df$SAMPLE_ID %in% remove_list),]
}
# Create CNA data frame, combine with pathways and tumor suppresor list
cna_df <- cna_df %>%
inner_join(dplyr::select(data_freeze, SAMPLE_ID, cancer_type), by = "SAMPLE_ID") %>%
dplyr::select(SAMPLE_ID, HUGO_SYMBOL, ALTERATION, LEVEL_1, LEVEL_2, LEVEL_3A,
LEVEL_3B, LEVEL_4, Highest_level, oncogenic, cancer_type) %>%
distinct() %>%
filter(is.na(Highest_level) == F) %>%
mutate(ALTERATION = substring(ALTERATION, 1, 3)) %>%
dplyr::select(SAMPLE_ID, HUGO_SYMBOL, ALTERATION, Highest_level, oncogenic, cancer_type) %>%
dplyr::rename(sample_id = SAMPLE_ID,
gene_symbol = HUGO_SYMBOL,
alteration = ALTERATION,
highest_level = Highest_level) %>%
mutate(onco_type = ifelse(gene_symbol %in% tumor_suppressor_list, "tumor_suppresor", NA))
# Create fusion data frame
# Combine fusions where the hugo gene symbol is counted twice (impact and archer)
fus_df <- fus_df %>%
inner_join(dplyr::select(data_freeze, SAMPLE_ID, cancer_type), by = "SAMPLE_ID") %>%
dplyr::select(SAMPLE_ID, Hugo_Symbol, Fusion, LEVEL_1, LEVEL_2, LEVEL_3A,
LEVEL_3B, LEVEL_4, Highest_level, oncogenic, cancer_type) %>%
mutate_if(is.factor, as.character) %>%
mutate(Fusion = gsub(" fusion", "", Fusion)) %>%
mutate(Fusion = gsub(" - Archer", "", Fusion)) %>%
rowwise() %>%
mutate(Fusion = ifelse(grepl("intragenic", Fusion), Fusion,
paste(sort(unlist(strsplit(Fusion, "-", fixed = TRUE))), collapse = "-"))) %>%
ungroup() %>%
distinct()
# If fusion list is provided, select the gene partner of interest based on the list
if (missing(fusion_list) == FALSE) {
# Read in fusion list
fusion_list <- read.delim(fusion_list, header = F)
fusion_list <- as.character(fusion_list$V1)
fusion_list_collapse <- paste0("\\b", paste(fusion_list , collapse="\\b|\\b"), "\\b")
# Filter for fusion list or full fusion name
fus_df <- fus_df %>%
mutate_if(is.factor, as.character) %>%
mutate(Fusion = ifelse(Hugo_Symbol %in% fusion_list, Hugo_Symbol,
ifelse(grepl(fusion_list_collapse, Fusion) == F, Fusion, "REMOVE"))) %>%
filter(Fusion != "REMOVE") %>%
mutate(Fusion = gsub("-intragenic", "", Fusion))
}
# Clean, add tumor suppresor columns
fus_df <- fus_df %>%
filter(Highest_level != "") %>%
mutate(Alteration = "Fus") %>%
dplyr::select(SAMPLE_ID, Fusion, Alteration, Highest_level, oncogenic, cancer_type) %>%
dplyr::rename(sample_id = SAMPLE_ID,
gene_symbol = Fusion,
alteration = Alteration,
highest_level = Highest_level) %>%
mutate(onco_type = ifelse(gene_symbol %in% tumor_suppressor_list, "tumor_suppresor", NA)) %>%
distinct()
# Collapse NTRK fusions
# Other fusions can be added to this list moving forward
fus_df <- fus_df %>%
mutate_if(is.factor, as.character) %>%
mutate(gene_symbol = ifelse(gene_symbol %in% c("NTRK1", "NTRK2", "NTRK3"), "NTRK1/2/3", gene_symbol)) %>%
distinct()
# Filter for mutation status
if (status == "somatic") {
mut_df <- filter(mut_df, Mutation_Status != "GERMLINE" | is.na(Mutation_Status) == T)
} else if (status == "germline") {
mut_df <- filter(mut_df, Mutation_Status == "GERMLINE")
}
# Mutation
mut_df <- mut_df %>%
inner_join(dplyr::select(data_freeze, SAMPLE_ID, cancer_type), by = "SAMPLE_ID") %>%
dplyr::select(SAMPLE_ID, Hugo_Symbol, Variant_Type, LEVEL_1, LEVEL_2, LEVEL_3A,
LEVEL_3B, LEVEL_4, Highest_level, oncogenic, cancer_type, HGVSp_Short, Mutation_Status)
# Add in oncogenic here if included
if (include_oncogenic == T) {
mut_df <- mut_df %>%
mutate(ONCOGENIC = "ONCOGENIC")
}
mut_df <- melt(mut_df, id.vars = c("SAMPLE_ID", "Hugo_Symbol", "Variant_Type", "Highest_level",
"oncogenic", "cancer_type", "HGVSp_Short", "Mutation_Status"))
# Aggregate by everything but strip for the highest level
# This is just in case there is a gene alteration that has more than one level
# Add pathways and tumor suppressor column
# Remove duplicates if they are in the same pathway (use order of input df)
mut_df <- mut_df %>%
mutate_if(is.factor, as.character) %>%
filter(value != "") %>%
dplyr::select(-value, -Highest_level) %>%
dplyr::rename(highest_level = variable) %>%
filter(is.na(highest_level) == F) %>%
mutate(highest_level == as.character(highest_level),
Mutation_Status = ifelse(is.na(Mutation_Status) == T, "", Mutation_Status)) %>%
group_by(SAMPLE_ID, Hugo_Symbol, Variant_Type, oncogenic, cancer_type, HGVSp_Short, Mutation_Status) %>%
dplyr::summarise(highest_level = toString(highest_level)) %>%
ungroup() %>%
mutate(highest_level = gsub(",.*", "", highest_level),
alteration = "Mut") %>%
mutate(onco_type = ifelse(Hugo_Symbol %in% tumor_suppressor_list, "tumor_suppresor", NA)) %>%
###
### work in progress
mutate(Hugo_Symbol = ifelse(Hugo_Symbol == "BRAF" & HGVSp_Short == "p.V600E", "BRAF_V600E", Hugo_Symbol)) %>%
mutate(Hugo_Symbol = ifelse(Hugo_Symbol == "BRAF" & HGVSp_Short != "p.V600E", "BRAF_Other", Hugo_Symbol)) %>%
###
###
distinct() %>%
rename(gene_symbol = Hugo_Symbol) %>%
mutate(gene_symbol = as.character(gene_symbol)) %>%
dplyr::select(SAMPLE_ID, gene_symbol, alteration, highest_level, oncogenic, cancer_type, Mutation_Status, onco_type) %>%
dplyr::rename(sample_id = SAMPLE_ID) %>%
mutate(onco_type = ifelse(Mutation_Status == "GERMLINE", "germline", onco_type),
gene_symbol = ifelse(Mutation_Status == "GERMLINE", paste0(gene_symbol, "*"), gene_symbol)) %>%
dplyr::select(-Mutation_Status) %>%
group_by(sample_id, gene_symbol, alteration, highest_level, oncogenic, cancer_type, onco_type) %>%
dplyr::slice(1) %>%
ungroup()
# Filter for status
# Combine CNA, FUS, and MUT - create final df
if (status == "somatic" | status == "both") {
gene_final_df <- rbind(cna_df, fus_df)
gene_final_df <- rbind(gene_final_df, mut_df)
} else if (status == "germline") {
gene_final_df <- mut_df
}
# Optional include oncogenic alterations in plot
if (include_oncogenic == T){
gene_final_df <- gene_final_df %>%
mutate(highest_level = ifelse((is.na(highest_level) == T | highest_level == "") &
grepl("Oncogenic", oncogenic) == T, "ONCOGENIC", highest_level))
}
# Combine all tumor suppressor alterations (del, mut, fus)
# If the alteration is on a tumor suppresor, ignore alteration label
# Clean up gene symbol, remove everything after the comma
# Remove mutation label to clean up y axis
gene_final_df <- gene_final_df %>%
filter(is.na(highest_level) == F & highest_level != "") %>%
mutate_if(is.factor, as.character) %>%
mutate(onco_type = ifelse(is.na(onco_type) == T, "oncogene", onco_type)) %>%
group_by(sample_id, gene_symbol, highest_level, cancer_type, onco_type) %>%
dplyr::summarise(alteration = toString(alteration)) %>%
ungroup() %>%
mutate(alteration = as.character(alteration)) %>%
mutate(alteration = ifelse(onco_type == "tumor_suppresor", "Del", alteration)) %>%
mutate(alteration = gsub(",.*", "", alteration)) %>%
mutate(gene_symbol_label = gsub(" Mut", "", paste0(gene_symbol, " ", alteration)))
# Optional select only the highest level
if (only_highest_level == T){
colnames(clin_df)[which(names(clin_df) == "SAMPLE_ID")] <- "sample_id"
gene_final_df <- gene_final_df %>%
left_join(dplyr::select(clin_df, sample_id, HIGHEST_LEVEL), by = "sample_id") %>%
mutate_if(is.factor, as.character) %>%
filter(HIGHEST_LEVEL == highest_level)
}
# Manual alterations
###
### work in progress
gene_final_df <- gene_final_df %>%
mutate_if(is.factor, as.character) %>%
mutate(gene_symbol = ifelse(gene_symbol %in% c("BRCA1", "BRCA2"), "BRCA1/2", gene_symbol),
gene_symbol_label = ifelse(gene_symbol == "BRCA1/2", "BRCA1/2 Del", gene_symbol_label)) %>%
mutate(gene_symbol = ifelse(gene_symbol %in% c("CHEK1", "CHEK2"), "CHEK1/2", gene_symbol),
gene_symbol_label = ifelse(gene_symbol == "CHEK1/2", "CHEK1/2 Del", gene_symbol_label)) %>%
mutate(gene_symbol = ifelse(gene_symbol %in% c("TSC1", "TSC2"), "TSC1/2", gene_symbol),
gene_symbol_label = ifelse(gene_symbol == "TSC1/2", "TSC1/2 Del", gene_symbol_label)) %>%
distinct()
###
###
# Calculate the percentage of each count by subtype
# Only select the highest level
prop_main_plot_df <- gene_final_df %>%
group_by(cancer_type, gene_symbol_label, highest_level) %>%
dplyr::summarise(n = n()) %>%
ungroup() %>%
left_join(clin_oncotree_freq, by = "cancer_type") %>%
dplyr::mutate(freq = n /total_count) %>%
group_by(cancer_type, gene_symbol_label) %>%
arrange(highest_level) %>%
dplyr::slice(1) %>%
ungroup() %>%
dplyr::mutate(percentage = 100*freq,
label_text = round(percentage, 0),
label_text = ifelse(percentage > 0 & percentage < 1, " ", label_text))
# Optional add pathway if provided, if not use it to set gene list
if (missing(path_df) == T) {
path_df <- gene_final_df %>%
left_join(prop_main_plot_df) %>%
dplyr::select(gene_symbol_label, highest_level, cancer_type, percentage) %>%
distinct() %>%
group_by(gene_symbol_label, highest_level) %>%
mutate(count = n()) %>%
ungroup() %>%
arrange(highest_level, desc(count), desc(percentage), gene_symbol_label) %>%
group_by(gene_symbol_label) %>%
dplyr::slice(1) %>%
ungroup() %>%
arrange(highest_level, desc(count), desc(percentage), gene_symbol_label) %>%
mutate(pathway = row_number()) %>%
dplyr::select(gene_symbol_label, pathway)
gene_final_df <- gene_final_df %>% left_join(path_df)
} else {
path_df <- read.delim(path_df)
colnames(path_df)[] <- c("gene_symbol", "pathway")
gene_final_df <- gene_final_df %>% left_join(path_df)
}
# Add germline label if figure includes both somatic and germline
if (status == "both") {
prop_main_plot_df$freq <- ifelse(grepl("\\*",prop_main_plot_df$gene_symbol_label) == TRUE,
prop_main_plot_df$n/prop_main_plot_df$germ_count,
prop_main_plot_df$freq)
}
# Add pathways
prop_main_plot_df <- prop_main_plot_df %>%
left_join(dplyr::select(gene_final_df, gene_symbol_label, gene_symbol), by = "gene_symbol_label") %>%
left_join(dplyr::select(gene_final_df, gene_symbol, cancer_type, highest_level, pathway, onco_type),
by = c("gene_symbol", "cancer_type", "highest_level")) %>%
group_by(gene_symbol, gene_symbol_label, cancer_type, pathway, onco_type, n, total_count, percentage, freq, label_text) %>%
dplyr::summarise(highest_level = toString(highest_level)) %>%
ungroup() %>%
mutate(highest_level_label = gsub(",.*", "", highest_level)) %>%
dplyr::select(-highest_level) %>%
dplyr::arrange(pathway, gene_symbol, highest_level_label, cancer_type)
# Only keep rows where at least one subtype meets the percetage threshold (alt_min)
prop_main_plot_df_filter <- prop_main_plot_df %>%
dplyr::select(gene_symbol_label, percentage) %>%
group_by(gene_symbol_label) %>%
filter(percentage == max(percentage)) %>%
filter(percentage < alt_min)
prop_main_plot_df <- prop_main_plot_df %>%
filter(!gene_symbol_label %in%prop_main_plot_df_filter$gene_symbol_label) %>%
mutate(cancer_type = factor(cancer_type, levels = cancer_order_other))
# Set gene order manually
if (missing(gene_order) == F) {
gene_order <- read.delim(gene_order, header = F)
gene_order <- as.data.frame(gene_order[rep(seq_len(nrow(gene_order)), each = 2), ])
colnames(gene_order)[] <- c("gene_symbol")
gene_order <- gene_order %>%
mutate_if(is.factor, as.character) %>%
mutate(order = seq(1:nrow(gene_order)),
gene_symbol = ifelse(order %% 2 == 0, paste0(gene_symbol, "*"), gene_symbol))
prop_main_plot_df <- prop_main_plot_df %>%
left_join(gene_order, by = "gene_symbol") %>%
dplyr::arrange(order)
}
# Get text color order
text_tsg_col <- prop_main_plot_df %>%
dplyr::select(gene_symbol_label, onco_type) %>%
distinct() %>%
dplyr::arrange(onco_type) %>%
group_by(gene_symbol_label) %>%
dplyr::summarise(onco_type = toString(onco_type)) %>%
ungroup() %>%
mutate(col = ifelse(onco_type != "tumor_suppresor", ifelse(onco_type == "oncogene", "#7E1116", "#4F0043"), "#191A57")) %>%
group_by(gene_symbol_label, col) %>%
slice(1) %>%
ungroup() %>%
mutate(gene_symbol_label = factor(gene_symbol_label, levels = unique(prop_main_plot_df$gene_symbol_label))) %>%
dplyr::arrange(gene_symbol_label)
# Write out data frame
write.table(prop_main_plot_df, "actionability_main_plot_data.txt", sep = "\t", quote = F, row.names = F)
# Create main plot
action_tile_plot_all <- ggplot(data = prop_main_plot_df, aes(x = cancer_type, y = gene_symbol_label)) +
geom_tile(aes(fill = highest_level_label)) +
geom_text(aes(label = label_text), colour = "white", size = 2) +
theme(panel.grid.major = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(size = 6), # colour = rev(text_tsg_col$col)),
panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA, size=0.5),
axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(size = 8),
plot.margin = unit(c(0.05, 0.05, 0.05, 0.05), "cm"),
legend.title = element_text(size = 8),
legend.text = element_text(size = 6),
legend.justification="left",
legend.margin=margin(0,0,0,0),
legend.box.margin=margin(-10,0,-10,-5)) +
geom_vline(xintercept=seq(1.5, length(levels(prop_main_plot_df$cancer_type))-0.5, 1),
lwd=0.25, colour="gray80") +
geom_hline(yintercept=seq(1.5, length(unique(prop_main_plot_df$gene_symbol_label))-0.5, 1),
lwd=0.25, colour="gray80") +
scale_fill_manual(values = c("#88E281","#33A02C", "#1F78B4", "#984EA3", "#BE98CE", "#a8a8a8", "#ffdab9", "gray90"),
limits = c("LEVEL_1_MSI-H_TMB-H","LEVEL_1", "LEVEL_2", "LEVEL_3A", "LEVEL_3B", "LEVEL_4", "ONCOGENIC", "NO_LEVEL"),
labels = c("LEVEL 1 MSI/TMB-H","LEVEL 1", "LEVEL 2", "LEVEL 3A", "LEVEL 3B", "LEVEL 4", "ONCOGENIC", "NO LEVEL")) +
scale_y_discrete(limits = rev(unique(prop_main_plot_df$gene_symbol_label)),
labels = gsub("_", " ", rev(unique(prop_main_plot_df$gene_symbol_label))),
expand = c(0,0)) +
scale_x_discrete(limits = cancer_order_other) +
labs(fill = "Highest Level\nof Evidence") +
guides(fill = guide_legend(override.aes = list(size = 1)))
return(action_tile_plot_all)
}
# Create actionability TMB-H & MSI-H main plot add-on
action_main_msi_tmb_fun <- function(clin_df,
data_freeze,
group_col,
prop_level_df = "./actionability_levels_barplot_table.txt",
msi_tmb_df){
# Read in data
data_freeze <- read.delim(data_freeze)
clin_df <- read.delim(clin_df)
msi_tmb_df <- read.delim(msi_tmb_df)
prop_level_df <- read.delim(prop_level_df)
# Set order
cancer_order_other <- as.character(unique(prop_level_df[,c(group_col)]))
# Get MSI/TMB frequency