-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathsurvival.R
executable file
·1309 lines (1081 loc) · 49.4 KB
/
survival.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
#Suppress all warning messages
options(warn=-1)
start_time <- suppressMessages(Sys.time())
#suppressMessages(library("optparse"))
option_list <- list(
optparse::make_option(c("-I", "--filein"), action="store",
type='character', dest = "exp_file", help="Input file name. Tab separated values (tsv) file containing genes/transcripts expression and survival paramenters"),
optparse::make_option(c("-O", "--outprefix"), action="store",
type='character', dest = "out", default = "reboot", help="Output file prefix. Default: reboot"),
optparse::make_option(c("-S", "--signature"), action="store",
type='character', dest = "sig_file", help="Tab separated values (tsv) file containing a set of genes/transcripts and corresponding cox coefficients"),
optparse::make_option(c("-M", "--multivariate"), action="store",
type='logical', dest = "type", default = FALSE, help="If clinical variables should be included, choose -M. This option is tied with -C option"),
optparse::make_option(c("-C", "--clinical"), action="store",
type='character', dest = "clin_file", default = "", help="Tab separated values (tsv) file containing binary categorical variables only. Required if -M option is chosen"),
optparse::make_option(c("-R", "--roc"), action="store",
type='logical', dest = "roc_curve", default = FALSE, help="If continuous variables should be categorized according to a ROC curve instead of median, choose -R"),
optparse::make_option(c("-V", "--variancefilter"), action="store",
type='numeric', dest = "var", default = "0.01", help="Minimum normalized variance (0-1) required for follow up time among samples (double). Default: 0.01"),
optparse::make_option(c("-F", "--force"), action="store",
type='logical', dest = "force", default = FALSE, help="To force overcome follow up variance filter and/or proportion filter for survival status (<20%), choose -F"))
opo <- optparse::OptionParser(option_list=option_list, add_help_option = T)
in_object <- optparse::parse_args(opo)
#Change variables
exp_file = in_object$exp_file
out = in_object$out
sig_file = in_object$sig_file
type = in_object$type
clin_file = in_object$clin_file
roc_curve = in_object$roc_curve
var = in_object$var
force = in_object$force
#Check if clinical data is provided in case type == TRUE
if(type){
if(clin_file == ""){
cat("Insert clinical variables file\n")
q(status=0)
}
}
####### Log file ##########
sink(file = paste(out, ".log", sep=''), append=T)
#Load libraries
# suppressMessages(library("survcomp"))
# suppressMessages(library("survival"))
# suppressMessages(library("survminer"))
# suppressMessages(library("OptimalCutpoints"))
# suppressMessages(library("survivalROC"))
# suppressMessages(library("forestmodel"))
# suppressMessages(library("sjstats"))
# suppressMessages(library("data.table"))
# suppressMessages(library("plyr"))
# suppressMessages(library("dplyr"))
cat("\n\n============================================================")
cat(" Apply Signature ")
cat("============================================================\n\n")
cat("Chosen parameters: ")
cat(paste(commandArgs(trailingOnly = T), collapse = " "))
cat("\n\n")
if(force){
cat("You have chosen the --force option which may lead to Cox model convergence issues. In this case, please consider using another survival endpoint.\n\n")
}
#Print log message
cat("Checking provided files...\n")
#Check if provided files exist
if(!file.exists(exp_file)){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: File ", exp_file, " does not exist. Please provide a valid file.\n",sep=""))
sink()
quit(save="no")
}
if(!file.exists(sig_file)){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: File ", sig_file, " does not exist. Please provide a valid file.\n",sep=""))
sink()
quit(save="no")
}
if(type & clin_file!="" & !file.exists(clin_file)){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: File ", clin_file, " does not exist. Please provide a valid file.\n",sep=""))
sink()
quit(save="no")
}
#Read provided files
signature = read.table(sig_file, header=T, check.names=F, stringsAsFactors=F)
data = read.table(exp_file, header=T, check.names=F, stringsAsFactors=F)
if(type & clin_file!=""){
clin = read.delim(clin_file, header=T, row.names=1)
}
#Check OS
if(class(data[,2])!="integer" | levels(as.factor(data[,2]))[1] != "0" | levels(as.factor(data[,2]))[2] != "1"){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: The second column of file ", exp_file, " does not contain endpoint status. Please check the manual for more information.\n",sep=""))
sink()
quit(save="no")
}
#Check OS.time
if(class(data[,3])!="integer" | min(data[,3])<0){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: The third column of file ", exp_file, " does not contain endpoint times. Please check the manual for more information.\n",sep=""))
sink()
quit(save="no")
}
#Check number of columns in signature file
if(ncol(signature)!=2){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: ", sig_file, " should have 2 columns. Please check the manual for more information.\n",sep=""))
sink()
quit(save="no")
}
#Check signature file format
if(!is.character(signature[,1])){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: Invalid format for column 1 of file ", sig_file, ". It should be a list of features (characters).\n",sep=""))
sink()
quit(save="no")
}
#Check signature format
if(!is.numeric(signature[,2])){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: Invalid format for column 2 of file ", sig_file, ". It should be a list of Cox coefficients (numeric values).\n",sep=""))
sink()
quit(save="no")
}
colnames(signature) = c("feature", "coefficient")
colnames(data)[1:3] = c("sample", "OS", "OS.time")
#Replace column names
signature$feature = gsub("-","_",signature$feature)
colnames(data) = gsub("-","_",colnames(data))
#Check if there is at least one valid feature in signature
signature = signature[!is.na(signature$coefficient) & signature$coefficient!=0,]
if(nrow(signature)==0){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: No valid features to be tested in the signature.\n",sep=""))
sink()
quit(save="no")
}
#Check if all genes in signature are present on data file
data = data[,grep(paste("^sample$","^OS$","^OS.time$",paste("^",signature$feature,"$",collapse="|",sep=""),sep="|"),colnames(data))]
if(ncol(data) != (nrow(signature)+3)){
if(ncol(data) > (nrow(signature)+3)){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: Duplicated features are not allowed. Please check the provided files.\n",sep=""))
sink()
quit(save="no")
}
if(ncol(data) < (nrow(signature)+3)){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: Not all features are present in both files. Please check the provided files.\n",sep=""))
sink()
quit(save="no")
}
}
#Check OS and OS.time
if(!force){
OSstatus <- data[,2]
percentage <- sum(OSstatus)/length(OSstatus)
if(percentage < 0.2 | percentage > 0.8){
cat("Survival status proportion:", percentage, " is probably not enough to the analysis. \nDeath or recidive are alternative options for the analysis", "\n\n")
cat("If you want to continue anyway, choose the flag F. \n\n")
q(status=0)
}
followup <- data[,3]
uplimit <- max(followup)
normalized <- followup/uplimit
fvar <- var(normalized)
if(fvar < var){
cat("Follow up variance: ", var, " has not passed the variance filter. \n\n")
cat("If you want to continue anyway, choose the flag F. \n\n")
q(status=0)
}
}
#Reorder colnames of data according to signature order
target = c("sample","OS","OS.time",signature$feature)
data = data[,match(target, colnames(data))]
#Create rownames
rownames(data) = data$sample
data = data[,2:ncol(data)]
#Create signature score for each sample
tmp = as.matrix(data[,3:ncol(data)])
if(nrow(signature)>1){ tmp = as.data.frame(tmp%*%diag(signature$coefficient)) }else{ tmp = as.data.frame(tmp*signature$coefficient) }
tmp$score <- apply(tmp, 1, function(x) sum(x))
tmp = tmp[,ncol(tmp),drop=F]
data = cbind(data[,1:2],tmp)
#Outputs data table used to generate the KM plot
write.table(data, paste(out,"_ScoreCont_Table.tsv",sep=""), row.names=F, col.names=T, quote=F, sep="\t")
#Edited "ggcoxzph" function to add the global Schoenfeld Test p-value
reboot_ggcoxzph <- function(fit, resid = T, se = T, df = 4, nsmo = 40, var, point.col = "red", point.size = 1,
point.shape = 19, point.alpha = 1, caption = NULL, ggtheme = survminer::theme_survminer(), ...){
x <- fit
if(!methods::is(x, "cox.zph"))
stop("Can't handle an object of class ", class(x))
xx <- x$x
yy <- x$y
d <- nrow(yy)
df <- max(df)
nvar <- ncol(yy)
pred.x <- seq(from = min(xx), to = max(xx), length = nsmo)
temp <- c(pred.x, xx)
lmat <- splines::ns(temp, df = df, intercept = T)
pmat <- lmat[1:nsmo, ]
xmat <- lmat[-(1:nsmo), ]
qmat <- qr(xmat)
if (qmat$rank < df)
stop("Spline fit is singular, try a smaller degrees of freedom")
if (se) {
bk <- backsolve(qmat$qr[1:df, 1:df], diag(df))
xtx <- bk %*% t(bk)
seval <- d * ((pmat %*% xtx) * pmat) %*% rep(1, df)
}
ylab <- paste("Beta(t) for", dimnames(yy)[[2]])
if (missing(var))
var <- 1:nvar
else {
if (is.character(var))
var <- match(var, dimnames(yy)[[2]])
if (any(is.na(var)) || max(var) > nvar || min(var) <
1)
stop("Invalid variable requested")
}
if (x$transform == "log") {
xx <- exp(xx)
pred.x <- exp(pred.x)
}
else if (x$transform != "identity") {
xtime <- as.numeric(dimnames(yy)[[1]])
indx <- !duplicated(xx)
apr1 <- stats::approx(xx[indx], xtime[indx], seq(min(xx), max(xx),
length = 17)[2 * (1:8)])
temp <- signif(apr1$y, 2)
apr2 <- stats::approx(xtime[indx], xx[indx], temp)
xaxisval <- apr2$y
xaxislab <- rep("", 8)
for (i in 1:8) xaxislab[i] <- format(temp[i])
}
plots <- list()
lapply(var, function(i) {
invisible(round(x$table[i, 3],4) -> pval)
invisible(round(x$table[nrow(x$table), 3],4) -> global)
ggplot2::ggplot() + ggplot2::labs(title = paste0('Global Schoenfeld Test p: ', global),
subtitle = paste0('Individual Schoenfeld Test p: ', pval)) +
ggtheme + ggplot2::theme(plot.title = ggplot2::element_text(hjust = .5, vjust = .5, face = "bold", margin = ggplot2::margin(0, 0, 10, 0)),
plot.subtitle = ggplot2::element_text(hjust = 0, vjust = .5, face = "plain", margin = ggplot2::margin(10, 0, 10, 0))) -> gplot
y <- yy[, i]
yhat <- as.vector(pmat %*% qr.coef(qmat, y))
if (resid)
yr <- range(yhat, y)
else yr <- range(yhat)
if (se) {
temp <- as.vector(2 * sqrt(x$var[i, i] * seval))
yup <- yhat + temp
ylow <- yhat - temp
yr <- range(yr, yup, ylow)
}
if (x$transform == "identity") {
gplot + ggplot2::geom_line(ggplot2::aes(x=pred.x, y=yhat)) +
ggplot2::xlab("Time") +
ggplot2::ylab(ylab[i]) +
ggplot2::ylim(yr) -> gplot
} else if (x$transform == "log") {
gplot + ggplot2::geom_line(ggplot2::aes(x=log(pred.x), y=yhat)) +
ggplot2::xlab("Time") +
ggplot2::ylab(ylab[i]) +
ggplot2::ylim(yr) -> gplot
} else {
gplot + ggplot2::geom_line(ggplot2::aes(x=pred.x, y=yhat)) +
ggplot2::xlab("Time") +
ggplot2::ylab(ylab[i]) +
ggplot2::scale_x_continuous(breaks = xaxisval,
labels = xaxislab) +
ggplot2::ylim(yr)-> gplot
}
if (resid)
gplot <- gplot + ggplot2::geom_point(ggplot2::aes(x = xx, y =y),
col = point.col, shape = point.shape, size = point.size, alpha = point.alpha)
if (se) {
gplot <- gplot + ggplot2::geom_line(ggplot2::aes(x=pred.x, y=yup), lty = "dashed") +
ggplot2::geom_line(ggplot2::aes( x = pred.x, y = ylow), lty = "dashed")
}
ggpubr::ggpar(gplot, ...)
}) -> plots
names(plots) <- var
class(plots) <- c("ggcoxzph", "ggsurv", "list")
if("GLOBAL" %in% rownames(x$table)) # case of multivariate Cox
global_p <- x$table["GLOBAL", 3]
else global_p <- NULL # Univariate Cox
attr(plots, "global_pval") <- global_p
attr(plots, "caption") <- caption
plots
}
#Function to test Cox Proportional Assumptions (Schoenfeld Test)
test_ph_assumptions <- function(model_object, covariates, is_multi)
{
if(!is_multi & is.null(model_object)){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: Cox model could not be fitted to score due to convergence issues. Please consider using another survival endpoint.\n",sep=""))
sink()
quit(save="no")
} else if(is_multi & is.null(model_object)){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: Multivariate Cox model could not be fitted due to convergence issues. Please check the provided clinical variables or consider using another survival endpoint.\n",sep=""))
sink()
quit(save="no")
}
test.ph <- survival::cox.zph(model_object)
if(is_multi){
tmp_covariates <- c()
covariates = c("score", covariates)
new_covariates = colnames(test.ph$y)
#Match string to get from 'new covariates' the original 'covariates' name
for(var in new_covariates)
{
for(var2 in covariates)
{
if(grepl(var2, var)){tmp_covariates <- append(tmp_covariates, var2)}
}
}
} else{
tmp_covariates <- c("score")
}
rownames(test.ph$table) <- c(tmp_covariates, "GLOBAL")
colnames(test.ph$y) <- tmp_covariates
if(is_multi) {
phplot <- reboot_ggcoxzph(fit = test.ph)
} else {
phplot <- survminer::ggcoxzph(fit = test.ph)
}
pdf(file = paste(out, "_ph_assumptions_plot.pdf", sep=""))
for (plot in phplot) {
print(plot, newpage = T)
}
garbage = dev.off()
tmp_df <- as.data.frame(test.ph$table)
global_p <- tmp_df[nrow(tmp_df),3]
pvalues <- tmp_df[1:(nrow(tmp_df)-1),3,drop=F]
variables = as.character(rownames(pvalues[pvalues[[1]]>0.05,,drop=F]))
dropped = as.character(rownames(pvalues[pvalues[[1]]<=0.05,,drop=F]))
if(length(dropped)>0){
cat(paste("\tWarning: Proportional Hazards Assumptions not met (Global p = ", round(x = global_p, digits = 4), "). The following variables will be disregarded: ", paste(dropped,collapse=", "), ".\n", "\tFor more information, check plot: '",out,"_ph_assumptions_plot.pdf'\n",sep=""))
} else{
cat(paste("Proportional Hazards Assumptions met (Global p = ", round(x = global_p, digits = 4), ").\n", sep = ""))
}
return(variables)
}
#Function to calculate ROC curve
cutoff_ROC <- function(dataset, auc_val, filename, plot)
{
if (auc_val < .5)
{
optimal.cutpoint <- OptimalCutpoints::optimal.cutpoints(X = "score",
status = "OS",
tag.healthy = 0, methods = "Youden",
data = dataset,
control = OptimalCutpoints::control.cutpoints(),
#ci.fit = TRUE,
direction = ">")
}
else
{
optimal.cutpoint <- OptimalCutpoints::optimal.cutpoints(X = "score",
status = "OS",
tag.healthy = 0, methods = "Youden",
data = dataset,
#ci.fit = TRUE,
control = OptimalCutpoints::control.cutpoints())
}
var_cutoff <- as.numeric(optimal.cutpoint$Youden$Global$optimal.cutoff$cutoff)
if (length(var_cutoff) >= 2)
{
var_cutoff <- var_cutoff[1]
}
else
{
var_cutoff <- var_cutoff
}
if (plot){
pdf(filename)
roc_curve_plot <- OptimalCutpoints::plot.optimal.cutpoints(x = optimal.cutpoint, legend = T, which = c(1), col = "blue", bg = "white")
garbage = dev.off()
}
return(var_cutoff)
}
#If some clinical file is provided
if(type & clin_file != ""){
#Check if all samples in exp_file are present on clin_file
clin = merge(data,clin,by=0)
if(nrow(data) != nrow(clin)){
if(nrow(data) > nrow(clin)){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: Not all samples from file ", exp_file," are provided in file ", clin_file,". Please check the provided files.\n",sep=""))
sink()
quit(save="no")
}
if(nrow(data) < nrow(clin)){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: Not all samples from file ", clin_file," are provided in file ", exp_file,". Please check the provided files.\n",sep=""))
sink()
quit(save="no")
}
}
#Change rownames of clinical data
rownames(clin) = clin$Row.names
clin = clin[,2:ncol(clin)]
#Print log message
cat("Done\n")
cat("\n")
#Transform score to categorical with ROC curve or with median value
if(roc_curve){
#This value can be changed. By default, it uses the median followup time
cutoff = median(x = clin$OS.time, na.rm = TRUE)
nobs <- nrow(clin)
roc = survivalROC::survivalROC(Stime = clin$OS.time, status = clin$OS, marker = clin$score, predict.time = cutoff, method = "NNE", span = 0.25*nobs^(-0.20))
score_cutoff = cutoff_ROC(dataset = clin, auc_val = as.numeric(roc$AUC), plot = F)
clin$score = ifelse(clin$score > score_cutoff, "high", "low")
} else {
clin$score = ifelse(clin$score > median(clin$score), "high", "low")
}
#Check if all columns have categorical variables with only 2 factors
for(i in 3:ncol(clin)){
if(nlevels(as.factor(clin[,i])) != 2){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: Variable \"", colnames(clin)[i], "\" in file ", clin_file," does not have 2 categories. Please check the provided file.\n",sep=""))
sink()
quit(save="no")
}
}
} else{
#Print log message
cat("Done\n\n")
}
#Print log message
cat("Creating signature score...\n")
cat("Done\n\n")
# Modify hazard.ratio (survcomp) to avoid "strata" function not found error
my_hazard.ratio <- function(x, surv.time, surv.event, weights, strat, alpha = 0.05,
method.test = c("logrank", "likelihood.ratio", "wald"), na.rm = FALSE,
...)
{
method.test <- match.arg(method.test)
if (!missing(weights)) {
if (length(weights) != length(x)) {
stop("bad length for parameter weights!")
}
}
else {
weights <- rep(1, length(x))
}
if (!missing(strat)) {
if (length(strat) != length(x)) {
stop("bad length for parameter strat!")
}
iix <- weights <= 0
if (any(iix)) {
warning("samples with weight<=0 are discarded")
}
weights[iix] <- NA
}
else {
strat <- rep(1, length(x))
}
cc.ix <- complete.cases(x, surv.time, surv.event, weights,
strat)
if (sum(cc.ix) < 3) {
data <- list(x = x, z = rep(NA, length(x)), surv.time = surv.time,
surv.event = surv.event, weights = weights, strat = strat)
return(list(hazard.ratio = NA, coef = NA, se = NA, lower = NA,
upper = NA, p.value = NA, n = sum(cc.ix), coxm = NA,
data = data))
}
if (any(!cc.ix) & !na.rm) {
stop("NA values are present!")
}
sx <- x[cc.ix]
oo <- order(sx, decreasing = FALSE)
sx <- sx[oo]
stime <- surv.time[cc.ix][oo]
sevent <- surv.event[cc.ix][oo]
sweights <- weights[cc.ix][oo]
sstrat <- strat[cc.ix][oo]
data <- list(x = x, surv.time = surv.time, surv.event = surv.event)
options(warn = 2)
# Removes "strata(sstrat)" when calling coxph due to contrasts bug...
rr <- try(survival::coxph(survival::Surv(stime, sevent) ~ sx, weights = sweights, ...))
#rr <- try(survival::coxph(survival::Surv(stime, sevent) ~ survival::strata(sstrat) + sx,
# weights = sweights, ...))
options(warn = 0)
if (class(rr) == "try-error") {
res <- list(hazard.ratio = NA, coef = NA, se = NA, lower = NA,
upper = NA, p.value = NA, n = sum(cc.ix), coxm = NA,
data = data)
}
else {
hrcoef <- rr$coefficients
hrse <- sqrt(drop(rr$var))
names(hrcoef) <- names(hrse) <- NULL
mystat <- NA
switch(method.test, logrank = {
mystat <- rr$score
}, likelihood.ratio = {
mystat <- 2 * (rr$loglik[2] - rr$loglik[1])
}, wald = {
mystat <- rr$wald.test
})
mypp <- pchisq(mystat, df = 1, lower.tail = FALSE)
res <- list(hazard.ratio = exp(hrcoef), coef = hrcoef,
se = hrse, lower = exp(hrcoef - qnorm(alpha/2, lower.tail = FALSE) *
hrse), upper = exp(hrcoef + qnorm(alpha/2, lower.tail = FALSE) *
hrse), p.value = mypp, n = rr$n, coxm = rr, data = data)
}
return(res)
}
#Create function to run log-rank test for score signatures
logrank.test <- function(dat,filename){
#Transform score to categorical with ROC curve or with median value
if(roc_curve){
#This value can be changed. By default, it uses the median followup time
cutoff = median(x = dat$OS.time, na.rm = TRUE)
nobs <- nrow(dat)
cat("Generating ROC curve...\n")
roc = survivalROC::survivalROC(Stime = dat$OS.time, status = dat$OS, marker = dat$score, predict.time = cutoff, method = "NNE", span = 0.25*nobs^(-0.20))
score_cutoff = cutoff_ROC(dataset = dat, auc_val = as.numeric(roc$AUC), filename = paste(out, "_ROC.pdf", sep=""), plot = T)
dat$score = ifelse(dat$score > score_cutoff, "high", "low")
cat("Done\n\n")
} else {
dat$score = ifelse(dat$score > median(dat$score), "high", "low")
}
#Test proportional hazards assumptions
cat("Testing proportional hazards assumption (signature score)...\n")
uni_model = survival::coxph(formula = formula(paste('survival::Surv(OS.time, OS) ~ score')) , data = dat)
test_ph_assumptions(model_object = uni_model, covariates = "NULL", is_multi = F)
cat("Done\n\n")
cat("Running log-rank test for signature score...\n")
tryCatch({
#Run log rank test
hr_model = my_hazard.ratio(x = dat$score, surv.time = dat$OS.time, surv.event = dat$OS,
alpha = .05, method.test = "logrank", na.rm = T)
#Extract result fields: p-value, hazard ratio(95% confidence interval) and coefficient
hazard.ratio = paste(round(hr_model$hazard.ratio,4), " (95% CI, ", round(hr_model$lower,4), " - ", round(hr_model$upper,4), ")", sep="")
coef = hr_model$coef
log.rank.pvalue = hr_model$p.value
#Get number of high/low expression samples
low.high.samples = paste(nrow(dat[dat$score=="low",]),nrow(dat[dat$score=="high",]),sep="/")
#Run survfit to get median survival of groups and 95% confidence interval
fit = survival::survfit(survival::Surv(OS.time, OS) ~ score, data=dat)
median.survival.low = paste(summary(fit)$table[2,7], " (95% CI, ", summary(fit)$table[2,8]," - ", summary(fit)$table[2,9], ")" ,sep="")
median.survival.high = paste(summary(fit)$table[1,7], " (95% CI, ", summary(fit)$table[1,8]," - ", summary(fit)$table[1,9], ")" ,sep="")
#Add information about prognosis
if(!is.na(log.rank.pvalue) & log.rank.pvalue<0.05 & coef<0){
prognosis = "better"
} else if(!is.na(log.rank.pvalue) & log.rank.pvalue<0.05 & coef>0){
prognosis = "worse"
} else{
prognosis = "----"
}
#Create dataframe with result to be used in multivariate analysis
result = data.frame(feature="score", coefficient=round(coef,4), hazard.ratio=hazard.ratio, log.rank.pvalue=round(log.rank.pvalue,4),
low.high.samples=low.high.samples, median.survival.low=median.survival.low, median.survival.high=median.survival.high, prognosis=prognosis)
#Write log-rank result to file
write.table(result, filename, row.names=F, col.names=T, quote=F, sep="\t")
return(result)
}, warning = function(w){}, error = function(e){})
}
#Run log-rank test for score signatures
res_logrank = logrank.test(data, paste(out, "_logrank.txt",sep=""))
#Check if logrank model is not null
if(is.null(res_logrank)){
####### Error file ##########
sink(file = paste(out, ".err", sep=''), append=T)
cat(paste("Error: Cox model could not be fitted to score due to convergence issues. Please consider using another survival endpoint.\n",sep=""))
sink()
quit(save="no")
}
#Print log messages
cat("Done\n\n")
cat("Generating Kaplan-Meier curve...\n")
#Create function to plot survival curve
logrank.plot <- function(dat,filename){
#Get cutoff (either median or ROC) score of data signature AND transform data to categorical
if(roc_curve){
#This value can be changed. By default, it uses the median followup time
cutoff = median(x = dat$OS.time, na.rm = TRUE)
nobs <- nrow(dat)
roc = survivalROC::survivalROC(Stime = dat$OS.time, status = dat$OS, marker = dat$score, predict.time = cutoff, method = "NNE", span = 0.25*nobs^(-0.20))
score_cutoff = cutoff_ROC(dataset = dat, auc_val = as.numeric(roc$AUC), plot = F)
sig_value = round(score_cutoff,2)
dat$score = ifelse(dat$score > score_cutoff, "high", "low")
} else {
sig_value = round(median(dat$score),2)
dat$score = ifelse(dat$score > median(dat$score), "high", "low")
}
#If there is at least two groups to be compared, create Kaplan-meier curve
if(nlevels(as.factor(dat$score))>1){
pdf(filename)
# Perhaps remove legend.labs...
pp = survminer::ggsurvplot(survival::survfit(survival::Surv(OS.time, OS) ~ score, data=dat),
risk.table=TRUE, tables.theme = survminer::theme_cleantable(), tables.y.text = F,
tables.height=0.2, pval=paste("p =",format(res_logrank[,4],scientific=T),sep=" "),
font.legend=16, font.x=22, font.y=22, font.tickslab=8, pval.size=6, pval.coord=c(0,0.05),
title= "", legend = c(0.7, 0.9), legend.title="", censor=T,
legend.labs = survival::strata(c(paste("score>",sig_value, sep=""),
paste("score<=",sig_value, sep=""))), data=dat) +
ggplot2::xlab("Survival time")
print(pp, newpage = FALSE)
garbage = dev.off()
} else{
cat(paste("Warning: Data could not be partitioned into low/high scores.\n",sep=""))
}
}
#Make survival plot (log-rank test for score signatures)
logrank.plot(data, paste(out, "_km_plot.pdf",sep=""))
#Print log messages
cat("Done\n\n")
#Create function to run univariate Cox-regression for each provided clinical parameter
univCox.test <- function(dat, covariates){
univ_formulas = sapply(covariates, function(x) as.formula(paste('survival::Surv(OS.time, OS)~', x)))
univ_models = lapply( univ_formulas, function(x){survival::coxph(x, data = dat)})
univ_results <- lapply(univ_models, function(x){
coef = as.data.frame(stats::coef(summary(x)))
ci = as.data.frame(summary(x)$conf.int)
res = merge(coef,ci,by=0)
res = res[,c(1:3,6,9:10)]
colnames(res) = c("variable", "coefficient", "hr", "Cox.pvalue", "lower.ci", "upper.ci")
res$hazard.ratio = paste(round(res$hr,4), " (95% CI, ", round(res$lower.ci,4), " - ", round(res$upper.ci,4), ")", sep="")
res$Cox.pvalue = round(res$Cox.pvalue,4)
res$prognosis = "----"
if(nrow(res[(!is.na(res$Cox.pvalue) & res$Cox.pvalue<0.2 & res$coefficient<0),])>0){res[!is.na(res$Cox.pvalue) & res$Cox.pvalue<0.2 & !is.na(res$coefficient) & res$coefficient<0,]$prognosis <- "better"}
if(nrow(res[(!is.na(res$Cox.pvalue) & res$Cox.pvalue<0.2 & res$coefficient>0),])>0){res[!is.na(res$Cox.pvalue) & res$Cox.pvalue<0.2 & !is.na(res$coefficient) & res$coefficient>0,]$prognosis <- "worse"}
res = res[,c(1,7,4,8)]
return(res)
})
univ_res = do.call("rbind", univ_results)
return(univ_res)
}
#Bootstrap Resampling
my_bootstrap_method <- function(raw_df, boot_df, boot_sample)
{
boot_sample <- boot_sample + 1
boot_vec <- boot_df$strap[[boot_sample]]$id
bootstrap_df <- as.data.frame(raw_df[boot_vec,])
#Check if all columns have categorical variables with only 2 factors
for(i in 3:ncol(bootstrap_df))
{
if(nlevels(as.factor(bootstrap_df[,i])) != 2)
{
bootstrap_df <- "repeat"
break
} else {
myLevels <- levels(as.factor(bootstrap_df[,i]))
level1 <- (nrow(bootstrap_df[bootstrap_df[[i]] == myLevels[[1]],]) / nrow(bootstrap_df))
if (level1 < .2 | level1 > .8)
{
bootstrap_df <- "repeat"
break
}
}
}
return(bootstrap_df)
}
#Make forest plot for multivariate analysis
generate_forest_plot <- function(model_object, filename)
{
pdf(filename)
forest_plot <- suppressWarnings(forestmodel::forest_model(model = model_object, exponentiate = T, factor_separate_line = F, recalculate_width = T, recalculate_height = T))
suppressWarnings(print(forest_plot, newpage = FALSE))
garbage = dev.off()
}
#Merge tmp tables generated in multivariate cox regression with bootstrap
merge_tables <- function(tables_list, covariates)
{
cox_tables <- list()
iterator <- 0
for (i in 1:length(tables_list))
{
#Remove rows (variables) if p-value > .05
table <- tables_list[[i]]
table <- table[!is.na(table$prognosis),]
iterator <- iterator + 1
tmp_df <- dplyr::count(table, variable)
cox_tables[[iterator]] <- tmp_df
}
tmp_table <- data.table::rbindlist(l = cox_tables, use.names = T, fill = T, idcol = "Unique ID")
colnames(tmp_table) <- c("Unique ID", "Co-Variables", "Frequency")
final_table <- dplyr::count(tmp_table, `Co-Variables`)
colnames(final_table) <- c("Co-Variables", "Frequency")
tmp_covariates <- c()
covariates = c("score", covariates)
new_covariates = final_table[[1]]
#Match string to get from 'new covariates' the original 'covariates' name
for(var in new_covariates)
{
for(var2 in covariates)
{
if(grepl(var2, var)){tmp_covariates <- append(tmp_covariates, var2)}
}
}
final_table[[1]] = tmp_covariates
return(final_table)
}
#Make histograms of frequency of variables in each bootstrap multivariate model
barplot_co_variables <- function(plot_df, filename, covariates)
{
tmp_covariates <- c()
covariates = c("score", covariates)
new_covariates = plot_df[[1]]
#Match string to get from 'new covariates' the original 'covariates' name
for(var in new_covariates)
{
for(var2 in covariates)
{
if(grepl(var2, var)){tmp_covariates <- append(tmp_covariates, var2)}
}
}
plot_df$var = tmp_covariates
pdf(filename)
final_plot <- ggplot2::ggplot(data = plot_df, ggplot2::aes(x = stats::reorder(plot_df[[3]], -plot_df[[2]]), y = plot_df[[2]])) +
ggplot2::geom_bar(stat = "identity", width = 0.5, color = "black", fill = "black") +
ggplot2::xlab("") + ggplot2::ylab("Frequency (%)") +
ggplot2::geom_segment(ggplot2::aes(x = .5, y = 25, xend = (nrow(plot_df) + .5), yend = 25), color = "red", linetype = "dashed", size = .5) +
ggplot2::theme_bw() +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 60, hjust = 1), axis.text = ggplot2::element_text(face = "plain", colour = "black"),
legend.text = ggplot2::element_text(colour = "black", face = "plain"), axis.ticks = element_line(colour = "black"), axis.line = element_line(colour = "black"),
panel.grid.major.x = element_blank(), text = ggplot2::element_text(face = "plain", colour = "black")) +
ggplot2::scale_y_continuous(breaks = seq(0, 100, by = 20))
print(final_plot, newpage = FALSE)
garbage = dev.off()
}
#Create function to run multivariate Cox regression for score + clinical parameters
multiCox.test <- function(dat, univ_result, logrank_result, uni_covariates, all_covariates){
roc_curve2 <<- F
filters <<- T
#Select only relevant clinical parameters for multivariable Cox regression (p<0.2) which passed the Schoenfeld test
covariates = rownames(univ_result[(!is.na(univ_result$Cox.pvalue) & univ_result$Cox.pvalue<0.2),])
covariates = intersect(uni_covariates,covariates)
if(!is.element("score",covariates)){covariates=c("score",covariates)}
tryCatch({
if (roc_curve){
#Remove row if "NA" in at least one column (variable)
counter <- ncol(dat)
tmp_table <- nrow(dat[stats::complete.cases(dat),])
sample_cutoff <- .7 * nrow(dat)
temporary_table <- dat
backup_dat <- dat
# Ensure table without 'NAs' has at least 70% of the samples from the original dataframe
while (tmp_table < sample_cutoff)
{
if (counter < 5){
cat("\tWarning: Minimum number of covariables (3) not achieved. Please check variables provided in clinical file. ")
cat("Performing multivariate regression without bootstrap resampling...\n\n")
break
} else {
remove_var_vec <- c()
remove_index <- 0
for (col in names(temporary_table))
{
tmp_var_size <- as.vector(is.na(temporary_table[[col]]))
tmp_var_size2 <- length(tmp_var_size[tmp_var_size == T])
remove_index <- remove_index + 1
remove_var_vec[remove_index] <- tmp_var_size2
}
remove_col <- which.max(remove_var_vec)
if (remove_col == length(remove_var_vec))
{
temporary_table <- temporary_table[,c(1:(remove_col-1))]
} else {
temporary_table <- temporary_table[,c(1:(remove_col-1),(remove_col+1):ncol(temporary_table))]
}
tmp_table <- nrow(temporary_table[stats::complete.cases(temporary_table),])
dat <- temporary_table[stats::complete.cases(temporary_table),]
counter <- counter - 1
}
}
n_cols_bef <- ncol(dat)
tmp_cols <- c()
# Ensure less abundant category of each variable in table is at least 20%
for (col in names(dat))
{
var <- table(dat[[col]])
var1 <- var[[1]]
var2 <- var[[2]]
if (var1 <= var2)
{
prop <- (var1 / (var1 + var2))
if (prop < .2)
{
next
} else
{
tmp_cols <- append(tmp_cols, col)
}
} else
{
prop <- (var2 / (var1 + var2))
if (prop < .2)
{
next
} else
{
tmp_cols <- append(tmp_cols, col)
}
}
}
dat <- dat[, tmp_cols]
n_cols_aft <- ncol(dat)
n_cols_diff <- n_cols_bef - n_cols_aft
counter <- counter - n_cols_diff
barPlot_counter <- counter
if (counter < 5){
filters <<- F
cat("\tWarning: Minimum number of covariables (3) not achieved. Please check variables provided in clinical file. ")
cat("Performing multivariate regression without bootstrap resampling...\n\n")
dat <- backup_dat
#Run multivariate Cox
res = survival::coxph(formula = formula(paste('survival::Surv(OS.time, OS)~', paste(covariates,collapse=" + "))) , data = dat)
#Extract results
coef = as.data.frame(stats::coef(summary(res)))
ci = as.data.frame(summary(res)$conf.int)
res = merge(coef,ci,by=0)
res = res[,c(1:3,6,9:10)]
colnames(res) = c("variable", "coefficient", "hr", "Cox.pvalue", "lower.ci", "upper.ci")
res$hazard.ratio = paste(round(res$hr,4), " (95% CI, ", round(res$lower.ci,4), " - ", round(res$upper.ci,4), ")", sep="")
res$Cox.pvalue = round(res$Cox.pvalue,4)
res$prognosis = NA
if(nrow(res[(!is.na(res$Cox.pvalue) & res$Cox.pvalue<0.05 & res$coefficient<0),])>0){res[!is.na(res$Cox.pvalue) & res$Cox.pvalue<0.05 & !is.na(res$coefficient) & res$coefficient<0,]$prognosis <- "better"}
if(nrow(res[(!is.na(res$Cox.pvalue) & res$Cox.pvalue<0.05 & res$coefficient>0),])>0){res[!is.na(res$Cox.pvalue) & res$Cox.pvalue<0.05 & !is.na(res$coefficient) & res$coefficient>0,]$prognosis <- "worse"}
res = res[,c(1,7,4,8)]
} else {