-
Notifications
You must be signed in to change notification settings - Fork 2
Expand file tree
/
Copy path.Rhistory
More file actions
512 lines (512 loc) · 26.9 KB
/
.Rhistory
File metadata and controls
512 lines (512 loc) · 26.9 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
OS_results <- c()
expression = data$TPM[gene,]
expMarker <- merge.data.frame(data$Clinical, data.frame(Sample=names(expression),Marker=expression),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$OS)),]
expMarker$OS <- as.numeric(expMarker$OS)
expMarker$OS_CNSR <- as.numeric(expMarker$OS_CNSR)
expMarker$Class <- ifelse(expMarker$Marker > mean(expMarker$Marker),"High","Low")
fit = survfit(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_os/",uuid,".png"),width = 600,height = 600, bg="white")
ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_os_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
OS_results = c(hr,p$pval)
names(OS_results) = c(paste0("OS_",names(hr)),"OS_p_value")
result = c(result,OS_results)
}
if(is.element("PFS",colnames(data$Clinical))){
PFS_results <- c()
expression = data$TPM[gene,]
expMarker <- merge.data.frame(data$Clinical, data.frame(Sample=names(expression),Marker=expression),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$PFS)),]
expMarker$PFS <- as.numeric(expMarker$PFS)
expMarker$PFS_CNSR <- as.numeric(expMarker$PFS_CNSR)
expMarker$Class <- ifelse(expMarker$Marker > mean(expMarker$Marker),"High","Low")
fit = survfit(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_pfs/",uuid,".png"),width = 600,height = 600, bg="white")
ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_pfs_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
PFS_results = c(hr,p$pval)
names(PFS_results) = c(paste0("PFS_",names(hr)),"PFS_p_value")
result = c(result,PFS_results)
}
return(result)
}else{
return("The input gene is not included in the dataset.")
}
}
kk(dataset = "Gide_2019",gene = "CD274",uuid="ae7648f9-dab6-4d20-a769-794450a845c2")
HR_95CI <- function(x){
x <- summary(x)
HR <-signif(x$coef[2], digits=2)
HR.confint.lower <- signif(x$conf.int[,"lower .95"], 2)
HR.confint.upper <- signif(x$conf.int[,"upper .95"],2)
res<- c(HR,HR.confint.lower,HR.confint.upper)
#names(res)<-c("Hazard Ratio","95% Upper CI","95% lower CI")
names(res)<-c("HR","HR_Upper","HR_Lower")
return(res)
}
kk = function(dataset,gene,uuid){
my_comparisons = list(c("No_Response","Response"))
file_path_dataset = paste0("./datasets/",dataset,".Rdata")
load(file_path_dataset)
data = get(dataset)
result = c()
if(is.element(gene,rownames(data$TPM))){
expression = data$TPM[gene,]
expMarker = merge(data$Samples,data.frame(Sample=names(expression),Marker=expression),by="Sample")
expMarker = expMarker[which(!is.na(expMarker$Resp_NoResp)),]
g = ggboxplot(expMarker,x="Resp_NoResp",y="Marker",color = "Resp_NoResp",palette = "npg",add = "jitter") +
stat_compare_means(comparisons = my_comparisons,label = "p.signif",method.args = list(alternative = "two.sided"))
ggsave(plot = g,filename = paste0("tmp/Results_wilcox_test/",uuid,".png"),width = 6,height = 6, bg="white")
res = wilcox.test(Marker ~ Resp_NoResp, data = expMarker,alternative = "two.sided")
auc = roc(Resp_NoResp ~ Marker, data = expMarker,auc = T)
g = ggroc(auc,color="#4D96FF",linetype = 1.2,size= 1) + theme_minimal() + annotate("text", x=0.9, y=0.97, label=paste0("AUC = ",round(auc$auc,digits = 2)),size=6, fontface="bold") + xlab("Specificity") + ylab("Sensitivity")
ggsave(plot = g,filename = paste0("tmp/Results_AUC/",uuid,".png"),width = 6,height = 6,bg = "white")
result=c(res$p.value,as.numeric(auc$auc))
names(result) = c("p_value","AUC")
if(is.element("OS",colnames(data$Clinical))){
OS_results <- c()
expression = data$TPM[gene,]
expMarker <- merge.data.frame(data$Clinical, data.frame(Sample=names(expression),Marker=expression),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$OS)),]
expMarker$OS <- as.numeric(expMarker$OS)
expMarker$OS_CNSR <- as.numeric(expMarker$OS_CNSR)
expMarker$Class <- ifelse(expMarker$Marker > mean(expMarker$Marker),"High","Low")
fit = survfit(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_os/",uuid,".png"),width = 600,height = 600, bg="white")
ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_os_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
OS_results = c(hr,p$pval)
names(OS_results) = c(paste0("OS_",names(hr)),"OS_p_value")
result = c(result,OS_results)
}
if(is.element("PFS",colnames(data$Clinical))){
PFS_results <- c()
expression = data$TPM[gene,]
expMarker <- merge.data.frame(data$Clinical, data.frame(Sample=names(expression),Marker=expression),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$PFS)),]
expMarker$PFS <- as.numeric(expMarker$PFS)
expMarker$PFS_CNSR <- as.numeric(expMarker$PFS_CNSR)
expMarker$Class <- ifelse(expMarker$Marker > mean(expMarker$Marker),"High","Low")
fit = survfit(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_pfs/",uuid,".png"),width = 600,height = 600, bg="white")
ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_pfs_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
PFS_results = c(hr,p$pval)
names(PFS_results) = c(paste0("PFS_",names(hr)),"PFS_p_value")
result = c(result,PFS_results)
}
return(result)
}else{
return("The input gene is not included in the dataset.")
}
}
kk(dataset = "Gide_2019",gene = "CD274",uuid="ae7648f9-dab6-4d20-a769-794450a845c2")
kk = function(dataset,gene,uuid){
my_comparisons = list(c("No_Response","Response"))
file_path_dataset = paste0("./datasets/",dataset,".Rdata")
load(file_path_dataset)
data = get(dataset)
result = c()
if(is.element(gene,rownames(data$TPM))){
expression = data$TPM[gene,]
expMarker = merge(data$Samples,data.frame(Sample=names(expression),Marker=expression),by="Sample")
expMarker = expMarker[which(!is.na(expMarker$Resp_NoResp)),]
g = ggboxplot(expMarker,x="Resp_NoResp",y="Marker",color = "Resp_NoResp",palette = "npg",add = "jitter") +
stat_compare_means(comparisons = my_comparisons,label = "p.signif",method.args = list(alternative = "two.sided"))
ggsave(plot = g,filename = paste0("tmp/Results_wilcox_test/",uuid,".png"),width = 6,height = 6, bg="white")
res = wilcox.test(Marker ~ Resp_NoResp, data = expMarker,alternative = "two.sided")
auc = roc(Resp_NoResp ~ Marker, data = expMarker,auc = T)
g = ggroc(auc,color="#4D96FF",linetype = 1.2,size= 1) + theme_minimal() + annotate("text", x=0.9, y=0.97, label=paste0("AUC = ",round(auc$auc,digits = 2)),size=6, fontface="bold") + xlab("Specificity") + ylab("Sensitivity")
ggsave(plot = g,filename = paste0("tmp/Results_AUC/",uuid,".png"),width = 6,height = 6,bg = "white")
result=c(res$p.value,as.numeric(auc$auc))
names(result) = c("p_value","AUC")
if(is.element("OS",colnames(data$Clinical))){
OS_results <- c()
expression = data$TPM[gene,]
expMarker <- merge.data.frame(data$Clinical, data.frame(Sample=names(expression),Marker=expression),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$OS)),]
expMarker$OS <- as.numeric(expMarker$OS)
expMarker$OS_CNSR <- as.numeric(expMarker$OS_CNSR)
expMarker$Class <- ifelse(expMarker$Marker > mean(expMarker$Marker),"High","Low")
fit = survfit(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_os/",uuid,".png"),width = 600,height = 600, bg="white")
ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_os_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
OS_results = c(hr,p$pval)
names(OS_results) = c(paste0("OS_",names(hr)),"OS_p_value")
result = c(result,OS_results)
}
if(is.element("PFS",colnames(data$Clinical))){
PFS_results <- c()
expression = data$TPM[gene,]
expMarker <- merge.data.frame(data$Clinical, data.frame(Sample=names(expression),Marker=expression),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$PFS)),]
expMarker$PFS <- as.numeric(expMarker$PFS)
expMarker$PFS_CNSR <- as.numeric(expMarker$PFS_CNSR)
expMarker$Class <- ifelse(expMarker$Marker > mean(expMarker$Marker),"High","Low")
fit = survfit(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_pfs/",uuid,".png"),width = 600,height = 600, bg="white")
ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_pfs_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
PFS_results = c(hr,p$pval)
names(PFS_results) = c(paste0("PFS_",names(hr)),"PFS_p_value")
result = c(result,PFS_results)
}
return(as.data.frame(result))
}else{
return("The input gene is not included in the dataset.")
}
}
kk(dataset = "Gide_2019",gene = "CD274",uuid="ae7648f9-dab6-4d20-a769-794450a845c2")
kk = function(dataset,gene,uuid){
my_comparisons = list(c("No_Response","Response"))
file_path_dataset = paste0("./datasets/",dataset,".Rdata")
load(file_path_dataset)
data = get(dataset)
result = c()
if(is.element(gene,rownames(data$TPM))){
expression = data$TPM[gene,]
expMarker = merge(data$Samples,data.frame(Sample=names(expression),Marker=expression),by="Sample")
expMarker = expMarker[which(!is.na(expMarker$Resp_NoResp)),]
g = ggboxplot(expMarker,x="Resp_NoResp",y="Marker",color = "Resp_NoResp",palette = "npg",add = "jitter") +
stat_compare_means(comparisons = my_comparisons,label = "p.signif",method.args = list(alternative = "two.sided"))
ggsave(plot = g,filename = paste0("tmp/Results_wilcox_test/",uuid,".png"),width = 6,height = 6, bg="white")
res = wilcox.test(Marker ~ Resp_NoResp, data = expMarker,alternative = "two.sided")
auc = roc(Resp_NoResp ~ Marker, data = expMarker,auc = T)
g = ggroc(auc,color="#4D96FF",linetype = 1.2,size= 1) + theme_minimal() + annotate("text", x=0.9, y=0.97, label=paste0("AUC = ",round(auc$auc,digits = 2)),size=6, fontface="bold") + xlab("Specificity") + ylab("Sensitivity")
ggsave(plot = g,filename = paste0("tmp/Results_AUC/",uuid,".png"),width = 6,height = 6,bg = "white")
result=c(res$p.value,as.numeric(auc$auc))
names(result) = c("p_value","AUC")
if(is.element("OS",colnames(data$Clinical))){
OS_results <- c()
expression = data$TPM[gene,]
expMarker <- merge.data.frame(data$Clinical, data.frame(Sample=names(expression),Marker=expression),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$OS)),]
expMarker$OS <- as.numeric(expMarker$OS)
expMarker$OS_CNSR <- as.numeric(expMarker$OS_CNSR)
expMarker$Class <- ifelse(expMarker$Marker > mean(expMarker$Marker),"High","Low")
fit = survfit(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_os/",uuid,".png"),width = 600,height = 600, bg="white")
ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_os_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
OS_results = c(hr,p$pval)
names(OS_results) = c(paste0("OS_",names(hr)),"OS_p_value")
result = c(result,OS_results)
}
if(is.element("PFS",colnames(data$Clinical))){
PFS_results <- c()
expression = data$TPM[gene,]
expMarker <- merge.data.frame(data$Clinical, data.frame(Sample=names(expression),Marker=expression),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$PFS)),]
expMarker$PFS <- as.numeric(expMarker$PFS)
expMarker$PFS_CNSR <- as.numeric(expMarker$PFS_CNSR)
expMarker$Class <- ifelse(expMarker$Marker > mean(expMarker$Marker),"High","Low")
fit = survfit(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_pfs/",uuid,".png"),width = 600,height = 600, bg="white")
ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_pfs_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
PFS_results = c(hr,p$pval)
names(PFS_results) = c(paste0("PFS_",names(hr)),"PFS_p_value")
result = c(result,PFS_results)
}
return(as.data.frame(t(result)))
}else{
return("The input gene is not included in the dataset.")
}
}
kk(dataset = "Gide_2019",gene = "CD274",uuid="ae7648f9-dab6-4d20-a769-794450a845c2")
kk(dataset = "Gide_2019",gene = "CD274",uuid="ae7648f9-dab6-4d20-a769-794450a845c2") -> dat
View(dat)
library(Rserve)
Rserve(port=6311,args="--no-save")
View(dat)
colnames(dat)
geneset = "CD3G,CD3E,CD8B,THEMIS,TRAT1,GRAP2,CD247,CD2,CD96,PRF1,CD6,IL7R,ITK,GPR18,EOMES,SIT1,NLRC3,CD244,KLRD1,SH2D1A,CCL5,XCL2,CST7,GFI1,KCNA3,PSTPIP1"
strsplit(geneset,split = ",",fixed = T)
unlist(strsplit(geneset,split = ",",fixed = T))
unlist(strsplit(geneset,split = ",",fixed = T)) -> a
signature = c("CD3G","CD3E","CD8B","THEMIS","TRAT1","GRAP2","CD247",
"CD2","CD96","PRF1","CD6","IL7R","ITK","GPR18","EOMES",
"SIT1","NLRC3","CD244","KLRD1","SH2D1A","CCL5","XCL2",
"CST7","GFI1","KCNA3","PSTPIP1")
geneset = "CD3G,CD3E,CD8B,THEMIS,TRAT1,GRAP2,CD247,CD2,CD96,PRF1,CD6,IL7R,ITK,GPR18,EOMES,SIT1,NLRC3,CD244,KLRD1,SH2D1A,CCL5,XCL2,CST7,GFI1,KCNA3,PSTPIP1"
signature = unlist(strsplit(geneset,split = ",",fixed = T))
signature = intersect(signature,rownames(data$TPM))
my_comparisons = list(c("No_Response","Response"))
file_path_dataset = paste0("./datasets/",dataset,".Rdata")
load(file_path_dataset)
data = get(dataset)
result = c()
geneset = "CD3G,CD3E,CD8B,THEMIS,TRAT1,GRAP2,CD247,CD2,CD96,PRF1,CD6,IL7R,ITK,GPR18,EOMES,SIT1,NLRC3,CD244,KLRD1,SH2D1A,CCL5,XCL2,CST7,GFI1,KCNA3,PSTPIP1"
signature = unlist(strsplit(geneset,split = ",",fixed = T))
signature = intersect(signature,rownames(data$TPM))
expression = data$TPM[signature,]
expMarker = merge(data$Samples,data.frame(Sample=names(expression),Signature=expression),by="Sample")
names(expression)
expression = data$TPM[signature,]
View(expression)
names(expression)
colnames(expression)
expMarker = merge(data$Samples,data.frame(Sample=colnames(expression),Signature=expression),by="Sample")
View(expression)
?apply
expression = data$TPM[signature,]
library("GSVA")
geneSet = list()
geneSet[["signature"]] = signature
gsva.es <- gsva(data$TPM, geneSet, method="ssgsea", verbose=T)
View(gsva.es)
View(expression)
expression = apply(expression, 2, sum)
expression
gsva.es
as.numeric(gsva.es)
expression = as.numeric(gsva.es)
names(expression) = colnames(gsva.es)
expression
expression = data$TPM[signature,]
expression = apply(expression, 2, mean)
expression
expression = data$TPM[signature,]
expression = apply(expression, 2, sum)
expression
expMarker = merge(data$Samples,data.frame(Sample=names(expression),Signature=as.numeric(expression)),by="Sample")
expMarker = expMarker[which(!is.na(expMarker$Resp_NoResp)),]
View(data)
View(data[["Clinical"]])
kk_set = function(dataset,geneset,uuid,type){
my_comparisons = list(c("No_Response","Response"))
file_path_dataset = paste0("./datasets/",dataset,".Rdata")
load(file_path_dataset)
data = get(dataset)
result = c()
geneset = "CD3G,CD3E,CD8B,THEMIS,TRAT1,GRAP2,CD247,CD2,CD96,PRF1,CD6,IL7R,ITK,GPR18,EOMES,SIT1,NLRC3,CD244,KLRD1,SH2D1A,CCL5,XCL2,CST7,GFI1,KCNA3,PSTPIP1"
signature = unlist(strsplit(geneset,split = ",",fixed = T))
signature = intersect(signature,rownames(data$TPM))
if(length(signature) >= 2){
expression = data$TPM[signature,]
if(type=="average"){
expression = apply(expression, 2, mean)
}else if(type=="sum"){
expression = apply(expression, 2, sum)
}else if(type=="ssGSEA"){
library("GSVA")
geneSet = list()
geneSet[["signature"]] = signature
gsva.es <- gsva(data$TPM, geneSet, method="ssgsea", verbose=T)
expression = as.numeric(gsva.es)
names(expression) = colnames(gsva.es)
}
expMarker = merge(data$Clinical,data.frame(Sample=names(expression),Signature=as.numeric(expression)),by="Sample")
expMarker = expMarker[which(!is.na(expMarker$Resp_NoResp)),]
g = ggboxplot(expMarker,x="Resp_NoResp",y="Signature",color = "Resp_NoResp",palette = "npg",add = "jitter") +
stat_compare_means(comparisons = my_comparisons,label = "p.signif",method.args = list(alternative = "two.sided"))
ggsave(plot = g,filename = paste0("tmp/Results_wilcox_test/",uuid,".png"),width = 6,height = 6, bg="white")
res = wilcox.test(Signature ~ Resp_NoResp, data = expMarker,alternative = "two.sided")
auc = roc(Resp_NoResp ~ Signature, data = expMarker,auc = T)
g = ggroc(auc,color="#4D96FF",linetype = 1.2,size= 1) + theme_minimal() + annotate("text", x=0.9, y=0.97, label=paste0("AUC = ",round(auc$auc,digits = 2)),size=6, fontface="bold") + xlab("Specificity") + ylab("Sensitivity")
ggsave(plot = g,filename = paste0("tmp/Results_AUC/",uuid,".png"),width = 6,height = 6,bg = "white")
result=c(res$p.value,as.numeric(auc$auc))
names(result) = c("p_value","AUC")
if(is.element("OS",colnames(data$Clinical))){
OS_results <- c()
expMarker = merge(data$Clinical,data.frame(Sample=names(expression),Signature=as.numeric(expression)),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$OS)),]
expMarker$OS <- as.numeric(expMarker$OS)
expMarker$OS_CNSR <- as.numeric(expMarker$OS_CNSR)
expMarker$Class <- ifelse(expMarker$Signature > mean(expMarker$Signature),"High","Low")
fit = survfit(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_os/",uuid,".png"),width = 600,height = 600, bg="white")
plot = ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
print(plot);dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_os_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
OS_results = c(hr,p$pval)
names(OS_results) = c(paste0("OS_",names(hr)),"OS_p_value")
result = c(result,OS_results)
}
if(is.element("PFS",colnames(data$Clinical))){
PFS_results <- c()
expMarker = merge(data$Clinical,data.frame(Sample=names(expression),Signature=as.numeric(expression)),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$PFS)),]
expMarker$PFS <- as.numeric(expMarker$PFS)
expMarker$PFS_CNSR <- as.numeric(expMarker$PFS_CNSR)
expMarker$Class <- ifelse(expMarker$Signature > mean(expMarker$Signature),"High","Low")
fit = survfit(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_pfs/",uuid,".png"),width = 600,height = 600, bg="white")
plot = ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
print(plot);dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_pfs_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
PFS_results = c(hr,p$pval)
names(PFS_results) = c(paste0("PFS_",names(hr)),"PFS_p_value")
result = c(result,PFS_results)
}
return(as.data.frame(t(result)))
}else{
return("The number of input genes contained in the dataset is less than 2.")
}
}
kk_set(dataset = "Gide_2019",geneset = "CD3G,CD3E,CD8B,THEMIS,TRAT1,GRAP2,CD247,CD2,CD96,PRF1,CD6,IL7R,ITK,GPR18,EOMES,SIT1,NLRC3,CD244,KLRD1,SH2D1A,CCL5,XCL2,CST7,GFI1,KCNA3,PSTPIP1",uuid = "test",type = "sum")
kk_set = function(dataset,geneset,uuid,type){
my_comparisons = list(c("No_Response","Response"))
file_path_dataset = paste0("./datasets/",dataset,".Rdata")
load(file_path_dataset)
data = get(dataset)
result = c()
# geneset = "CD3G,CD3E,CD8B,THEMIS,TRAT1,GRAP2,CD247,CD2,CD96,PRF1,CD6,IL7R,ITK,GPR18,EOMES,SIT1,NLRC3,CD244,KLRD1,SH2D1A,CCL5,XCL2,CST7,GFI1,KCNA3,PSTPIP1"
signature = unlist(strsplit(geneset,split = ",",fixed = T))
signature = intersect(signature,rownames(data$TPM))
if(length(signature) >= 2){
expression = data$TPM[signature,]
if(type=="average"){
expression = apply(expression, 2, mean)
}else if(type=="sum"){
expression = apply(expression, 2, sum)
}else if(type=="ssGSEA"){
library("GSVA")
geneSet = list()
geneSet[["signature"]] = signature
gsva.es <- gsva(data$TPM, geneSet, method="ssgsea", verbose=T)
expression = as.numeric(gsva.es)
names(expression) = colnames(gsva.es)
}
expMarker = merge(data$Clinical,data.frame(Sample=names(expression),Signature=as.numeric(expression)),by="Sample")
expMarker = expMarker[which(!is.na(expMarker$Resp_NoResp)),]
g = ggboxplot(expMarker,x="Resp_NoResp",y="Signature",color = "Resp_NoResp",palette = "npg",add = "jitter") +
stat_compare_means(comparisons = my_comparisons,label = "p.signif",method.args = list(alternative = "two.sided"))
ggsave(plot = g,filename = paste0("tmp/Results_wilcox_test/",uuid,".png"),width = 6,height = 6, bg="white")
res = wilcox.test(Signature ~ Resp_NoResp, data = expMarker,alternative = "two.sided")
auc = roc(Resp_NoResp ~ Signature, data = expMarker,auc = T)
g = ggroc(auc,color="#4D96FF",linetype = 1.2,size= 1) + theme_minimal() + annotate("text", x=0.9, y=0.97, label=paste0("AUC = ",round(auc$auc,digits = 2)),size=6, fontface="bold") + xlab("Specificity") + ylab("Sensitivity")
ggsave(plot = g,filename = paste0("tmp/Results_AUC/",uuid,".png"),width = 6,height = 6,bg = "white")
result=c(res$p.value,as.numeric(auc$auc))
names(result) = c("p_value","AUC")
if(is.element("OS",colnames(data$Clinical))){
OS_results <- c()
expMarker = merge(data$Clinical,data.frame(Sample=names(expression),Signature=as.numeric(expression)),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$OS)),]
expMarker$OS <- as.numeric(expMarker$OS)
expMarker$OS_CNSR <- as.numeric(expMarker$OS_CNSR)
expMarker$Class <- ifelse(expMarker$Signature > mean(expMarker$Signature),"High","Low")
fit = survfit(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_os/",uuid,".png"),width = 600,height = 600, bg="white")
plot = ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
print(plot);dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(OS,OS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_os_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
OS_results = c(hr,p$pval)
names(OS_results) = c(paste0("OS_",names(hr)),"OS_p_value")
result = c(result,OS_results)
}
if(is.element("PFS",colnames(data$Clinical))){
PFS_results <- c()
expMarker = merge(data$Clinical,data.frame(Sample=names(expression),Signature=as.numeric(expression)),by="Sample",all.y = T)
expMarker <- expMarker[which(!is.na(expMarker$PFS)),]
expMarker$PFS <- as.numeric(expMarker$PFS)
expMarker$PFS_CNSR <- as.numeric(expMarker$PFS_CNSR)
expMarker$Class <- ifelse(expMarker$Signature > mean(expMarker$Signature),"High","Low")
fit = survfit(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
png(paste0("tmp/Results_pfs/",uuid,".png"),width = 600,height = 600, bg="white")
plot = ggsurvplot(fit, data=expMarker,pval = TRUE,ggtheme = theme_minimal(),pval.method = TRUE,surv.median.line="hv",conf.int=T,risk.table=T)
print(plot);dev.off()
p = surv_pvalue(fit,data=expMarker)
res.cox <- coxph(Surv(PFS,PFS_CNSR) ~ Class,data=expMarker)
hr = HR_95CI(res.cox)
if(!is.element(Inf,hr) & !is.element(NA,hr)){
plot = ggforest(res.cox,data = expMarker,refLabel = 1,fontsize = 1, cpositions = c(0.00,0.06,0.4))
ggsave(plot=plot,filename = paste0("tmp/Results_pfs_forest/",uuid,".png"),width = 12,height = 6, bg="white")
}
PFS_results = c(hr,p$pval)
names(PFS_results) = c(paste0("PFS_",names(hr)),"PFS_p_value")
result = c(result,PFS_results)
}
return(as.data.frame(t(result)))
}else{
return("The number of input genes contained in the dataset is less than 2.")
}
}
kk_set(dataset = "Gide_2019",geneset = "CD3G,CD3E,CD8B,THEMIS,TRAT1,GRAP2,CD247,CD2,CD96,PRF1,CD6,IL7R,ITK,GPR18,EOMES,SIT1,NLRC3,CD244,KLRD1,SH2D1A,CCL5,XCL2,CST7,GFI1,KCNA3,PSTPIP1",uuid = "test",type = "sum")
kk_set(dataset = "Gide_2019",geneset = "CD3G,CD3E,CD8B,THEMIS,TRAT1,GRAP2,CD247,CD2,CD96,PRF1,CD6,IL7R,ITK,GPR18,EOMES,SIT1,NLRC3,CD244,KLRD1,SH2D1A,CCL5,XCL2,CST7,GFI1,KCNA3,PSTPIP1",uuid = "test",type = "average")
kk_set(dataset = "Gide_2019",geneset = "CD3G,CD3E,CD8B,THEMIS,TRAT1,GRAP2,CD247,CD2,CD96,PRF1,CD6,IL7R,ITK,GPR18,EOMES,SIT1,NLRC3,CD244,KLRD1,SH2D1A,CCL5,XCL2,CST7,GFI1,KCNA3,PSTPIP1",uuid = "test",type = "ssGSEA")
geneSet[["signature"]] = signature
gsva.es <- gsva(data$TPM, geneSet, method="ssgsea", verbose=T)
View(gsva.es)
load('/Users/kanghongen/Downloads/GWAS.IBD.ALL_R2.0.5_maf.0.005_Core56.Cell.Proportion_B.Cells.Memory_Binary.MedianLowHigh.RData')
load("/Users/kanghongen/Downloads/GWAS.IBD.ALL_R2.0.5_maf.0.005_Core56.Cell.Proportion_B.Cells.Memory_Binary.MedianLowHigh.RData")
View(result.maf.ls)
library(RSclient)
rsc1 <- RSconnect(port = 6311)
RSshutdown(rsc1)