-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathNCA.R
294 lines (263 loc) · 11.5 KB
/
NCA.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
library(tidyverse)
library(PKPDmisc)
library(plotly)
if(!require(readxl)) install.packages('readxl'); library(readxl)
if(!require(NonCompart)) install.packages('NonCompart'); library(NonCompart)
if(!require(textclean)) install.packages('textclean'); library(textclean)
raw <- read_excel('PKTK_dataset_201015.xlsx', sheet = 1)
Clean_raw <- raw %>%
mutate_at(vars(ID : II), function(x)ifelse(x == '.', NA, x) %>% as.numeric)
names(Clean_raw) <- names(Clean_raw) %>%
strip(lower.case = FALSE) %>%
word(1) %>%
toupper
Clean_raw <- Clean_raw %>%
mutate_at(vars(ID, SPECIES : FORMULATION), as.factor) %>%
mutate(SEX = factor(SEX, levels = c(1, 2), labels = c("M", "F")), ROUTE = factor(ROUTE, levels = c(1, 2), labels = c('PO', 'IV'))) %>%
filter(FORMULATION == 1)
## Analysis per species
Clean_animal <- Clean_raw %>%
split(.$SPECIES)
Clean_animal
names(Clean_animal) <- c('Mouse', 'Rat', 'Dog', 'Monkey')
Clean_animal <- map(1:4, function(i){
Clean_animal[[i]] %>% mutate(name = names(Clean_animal)[i])})
# Number of animals
Clean_animal %>%
map(function(x){x %>% filter(TIME == 0, MDV == 1) %>% group_by(DOSE, ROUTE) %>% summarise(n = n())})
# NCA & DOSE Linearity
data_NCA <- map(Clean_animal, function(set){
PO <- set %>%
filter(MDV == 0, CMT == 2, MULTIPLE == 1, ROUTE == 'PO') %>%
as.data.frame() %>%
mutate(DOSE = as.numeric(as.character(DOSE)))
if(length(PO$DV) == 0){
PO
} else {
DM <- PO %>% group_by(ID) %>% slice(1) %>% ungroup()
NCA_indiv <- tblNCA(PO, key = c("ID", "DOSE", "SEX", "MULTIPLE", "ROUTE", "FORMULATION"), colTime = "TIME", colConc = "DV", dose = DM$DOSE, adm = "Extravascular", dur = 0, concUnit = 'ng/mL', doseUnit = "mg", timeUnit = "h", down = "Linear", R2ADJ = 0.1) %>%
select(ID, DOSE, SEX, MULTIPLE, ROUTE, FORMULATION, CMAX, AUCLST, AUCIFO, TMAX, LAMZHL, CLFO, VZFO)
NCA_mean <- NCA_indiv %>%
group_by(DOSE, SEX) %>%
summarise_at(vars(CMAX : VZFO), mean, na.rm = T) %>%
mutate(AUCdose = AUCLST / DOSE) %>%
ungroup() %>%
rbind(NCA_indiv %>%
group_by(DOSE) %>%
summarise_at(vars(CMAX : VZFO), mean, na.rm = T) %>%
mutate(AUCdose = AUCLST / DOSE, SEX = as.factor("both")) %>%
ungroup() %>%
select(DOSE, SEX, everything())) %>%
arrange(DOSE)
NCA_mean %>%
ggplot(aes(x = DOSE, y = AUCdose)) +
geom_line() +
geom_point() +
theme_bw() +
facet_wrap(~SEX)
ggsave(paste0("Data_Exploration/", unique(set$name), "/NCA/",unique(set$name), "_PO_AUCdose.png"), device = "png")
write.csv(NCA_indiv, paste0("Data_Exploration/", unique(set$name), "/NCA/",unique(set$name), "_PO_NCAindiv.csv"))
write.csv(NCA_mean, paste0("Data_Exploration/", unique(set$name), "/NCA/",unique(set$name), "_PO_NCAmean.csv"))
}
IV <- set %>%
filter(MDV == 0, CMT == 2, MULTIPLE == 1, ROUTE == 'IV') %>%
as.data.frame() %>%
mutate(DOSE = as.numeric(as.character(DOSE)))
if(length(IV$DV) == 0){
IV
} else {
DM <- IV %>% group_by(ID) %>% slice(1) %>% ungroup()
NCA_indiv <- tblNCA(IV, key = c("ID", "DOSE", "SEX", "MULTIPLE", "ROUTE", "FORMULATION"), colTime = "TIME", colConc = "DV", dose = DM$DOSE, adm = "Bolus", dur = 0, concUnit = 'ng/mL', doseUnit = "mg", timeUnit = "h", down = "Linear", R2ADJ = 0.1) %>%
select(ID, DOSE, SEX, MULTIPLE, ROUTE, FORMULATION, CMAX, AUCLST, AUCIFO, TMAX, LAMZHL, CLO, VZO)
NCA_mean <- NCA_indiv %>%
group_by(DOSE, SEX) %>%
summarise_at(vars(CMAX : VZO), mean, na.rm = T) %>%
mutate(AUCdose = AUCLST / DOSE) %>%
ungroup() %>%
rbind(NCA_indiv %>%
group_by(DOSE) %>%
summarise_at(vars(CMAX : VZO), mean, na.rm = T) %>%
mutate(AUCdose = AUCLST / DOSE, SEX = as.factor("both")) %>%
ungroup() %>%
select(DOSE, SEX, everything())) %>%
arrange(DOSE)
NCA_mean %>%
ggplot(aes(x = DOSE, y = AUCdose)) +
geom_line() +
geom_point() +
theme_bw() +
facet_wrap(~SEX)
ggsave(paste0("Data_Exploration/", unique(set$name), "/NCA/",unique(set$name), "_IV_AUCdose.png"), device = "png")
write.csv(NCA_indiv, paste0("Data_Exploration/", unique(set$name), "/NCA/",unique(set$name), "_IV_NCAindiv.csv"))
write.csv(NCA_mean, paste0("Data_Exploration/", unique(set$name), "/NCA/",unique(set$name), "_IV_NCAmean.csv"))
}
})
# Individual figures
figure_dose <- map(Clean_animal, function(set){
PO <- set %>% filter(MDV == 0, CMT == 2, MULTIPLE == 1, ROUTE == 'PO')
IV <- set %>% filter(MDV == 0, CMT == 2, MULTIPLE == 1, ROUTE == 'IV')
LPO <- set %>% filter(MDV == 0, CMT == 3, MULTIPLE == 1, ROUTE == 'PO')
LIV <- set %>% filter(MDV == 0, CMT == 3, MULTIPLE == 1, ROUTE == 'IV')
if(length(PO$DV) == 0){
PO
} else {
p1 <- ggplot(data = PO, aes(x = TIME, y = DV, col = ID)) +
geom_line() +
geom_point(alpha = 0.5, size = 0.8) +
facet_grid(SEX ~ DOSE, scale = "free") +
theme_bw() +
theme(legend.position = "none")
p1
ggsave(paste0("Data_Exploration/", unique(set$name), "/Plasma/",unique(set$name), "_PO_freey.png"), device = "png", width = 8, height = 4)
p1 + scale_y_continuous(trans = 'log10')
ggsave(paste0("Data_Exploration/", unique(set$name), "/Plasma/",unique(set$name), "_PO_freey_log10.png"), device = "png", width = 8, height = 4)
}
if(length(IV$DV) == 0){
IV
} else {
p2 <- ggplot(data = IV, aes(x = TIME, y = DV, col = ID)) +
geom_line() +
geom_point(alpha = 0.5, size = 0.8) +
facet_grid(SEX ~ DOSE, scale = "free_y") +
theme_bw() +
theme(legend.position = "none")
p2
ggsave(paste0("Data_Exploration/", unique(set$name), "/Plasma/",unique(set$name), "_IV_freey.png"), device = "png")
p2 + scale_y_continuous(trans = 'log10')
ggsave(paste0("Data_Exploration/", unique(set$name), "/Plasma/",unique(set$name), "_IV_freey_log10.png"), device = "png")
}
if(length(LPO$DV) == 0){
LPO
} else {
p3 <- ggplot(data = LPO, aes(x = TIME, y = DV, col = ID)) +
geom_line() +
geom_point(alpha = 0.5, size = 0.8) +
facet_grid(SEX ~ DOSE, scale = "free") +
theme_bw() +
theme(legend.position = "none")
p3
ggsave(paste0("Data_Exploration/", unique(set$name), "/Liver/",unique(set$name), "_PO_liver.png"), device = "png")
p3 + scale_y_continuous(trans = 'log10')
ggsave(paste0("Data_Exploration/", unique(set$name), "/Liver/",unique(set$name), "_PO_liver_log10.png"), device = "png")
}
if(length(LIV$DV) == 0){
LIV
} else {
p4 <- ggplot(data = LIV, aes(x = TIME, y = DV, col = ID)) +
geom_line() +
geom_point(alpha = 0.5, size = 0.8) +
facet_grid(SEX ~ DOSE, scale = "free_y") +
theme_bw() +
theme(legend.position = "none")
p4
ggsave(paste0("Data_Exploration/", unique(set$name), "/Liver/",unique(set$name), "_IV_liver.png"), device = "png")
p4 + scale_y_continuous(trans = 'log10')
ggsave(paste0("Data_Exploration/", unique(set$name), "/Liver/",unique(set$name), "_IV_liver_log10.png"), device = "png")
}
})
Clean_animal %>%
map(function(set){
Mean_data <- set %>%
filter(MDV == 0) %>%
group_by(DOSE, TIME, CMT, MULTIPLE, ROUTE) %>%
summarise(mean = mean(DV, na.rm = T), sd = sd(DV, na.rm = T)) %>%
arrange(ROUTE, CMT, MULTIPLE, DOSE, TIME)
p1<- Mean_data %>%
filter(CMT == 2, MULTIPLE == 1) %>%
ggplot() +
geom_line(aes(x = TIME, y = mean)) +
geom_point(aes(x = TIME, y = mean)) +
geom_errorbar(aes(x = TIME, ymax = mean + sd, ymin = mean - sd), alpha = 0.5) +
theme_bw() +
facet_grid(ROUTE ~ DOSE, scale = "free_y")
p1
ggsave(paste0("Data_Exploration/", unique(set$name), "/Plasma/", unique(set$name), "_POIV_mean.png"), device = "png", width = 8, height = 4)
p1 + scale_y_continuous(trans = 'log10')
ggsave(paste0("Data_Exploration/", unique(set$name), "/Plasma/", unique(set$name), "_POIV_mean_log10.png"), device = "png", width = 8, height = 4)
p2 <- Mean_data %>%
filter(CMT == 3, MULTIPLE == 1) %>%
ggplot() +
geom_line(aes(x = TIME, y = mean)) +
geom_point(aes(x = TIME, y = mean)) +
geom_errorbar(aes(x = TIME, ymax = mean + sd, ymin = mean - sd), alpha = 0.5) +
theme_bw()
if(unique(set$name) %in% c("Dog", "Monkey")){
p3 <- p2 + ggtitle(unique(Mean_data[Mean_data$CMT == 3,]$ROUTE))
} else {
p3 <- p2 + facet_grid(ROUTE ~ DOSE)
}
p3
ggsave(paste0("Data_Exploration/", unique(set$name), "/Liver/", unique(set$name), "_POIV_mean_liver.png"), device = "png" )
p3 + scale_y_continuous(trans = 'log10')
ggsave(paste0("Data_Exploration/", unique(set$name), "/Liver/", unique(set$name), "_POIV_mean_liver_log10.png"), device = "png" )
})
figure_dose <- map(Clean_animal, function(set){
PO <- set %>% filter(MDV == 0, CMT == 2, MULTIPLE == 1, ROUTE == 'PO')
IV <- set %>% filter(MDV == 0, CMT == 2, MULTIPLE == 1, ROUTE == 'IV')
LPO <- set %>% filter(MDV == 0, CMT == 3, MULTIPLE == 1, ROUTE == 'PO')
LIV <- set %>% filter(MDV == 0, CMT == 3, MULTIPLE == 1, ROUTE == 'IV')
if(length(PO$DV) == 0){
PO
} else {
p1 <- ggplot(data = PO, aes(x = TIME, y = DV)) +
geom_line() +
geom_point() +
facet_grid(DOSE ~ SEX, scale = "free_y") +
theme_bw() +
theme(legend.position = "none")
p1
ggsave(paste0("Data_Exploration/", unique(set$name), "/Plasma/",unique(set$name), "_PO_freey.png"), device = "png")
p1 + scale_y_continuous(trans = 'log10')
ggsave(paste0("Data_Exploration/", unique(set$name), "/Plasma/",unique(set$name), "_PO_freey_log10.png"), device = "png")
}
if(length(IV$DV) == 0){
IV
} else {
p2 <- ggplot(data = IV, aes(x = TIME, y = DV, col = ID)) +
geom_line() +
geom_point() +
facet_grid(DOSE ~ SEX, scale = "free_y") +
theme_bw() +
theme(legend.position = "none")
p2
ggsave(paste0("Data_Exploration/", unique(set$name), "/Plasma/",unique(set$name), "_IV_freey.png"), device = "png")
p2 + scale_y_continuous(trans = 'log10')
ggsave(paste0("Data_Exploration/", unique(set$name), "/Plasma/",unique(set$name), "_IV_freey_log10.png"), device = "png")
}
if(length(LPO$DV) == 0){
LPO
} else {
p3 <- ggplot(data = LPO, aes(x = TIME, y = DV, col = ID)) +
geom_line() +
geom_point() +
facet_grid(DOSE ~ SEX) +
theme_bw() +
theme(legend.position = "none")
p3
ggsave(paste0("Data_Exploration/", unique(set$name), "/Liver/",unique(set$name), "_PO_liver.png"), device = "png")
p3 + scale_y_continuous(trans = 'log10')
ggsave(paste0("Data_Exploration/", unique(set$name), "/Liver/",unique(set$name), "_PO_liver_log10.png"), device = "png")
}
if(length(LIV$DV) == 0){
LIV
} else {
p4 <- ggplot(data = LIV, aes(x = TIME, y = DV, col = ID)) +
geom_line() +
geom_point() +
facet_grid(DOSE ~ SEX) +
theme_bw() +
theme(legend.position = "none")
p4
ggsave(paste0("Data_Exploration/", unique(set$name), "/Liver/",unique(set$name), "_IV_liver.png"), device = "png")
p4 + scale_y_continuous(trans = 'log10')
ggsave(paste0("Data_Exploration/", unique(set$name), "/Liver/",unique(set$name), "_IV_liver_log10.png"), device = "png")
}
})
## Plotly analysis
p1 <- Clean_animal[[1]] %>%
filter(MDV == 0, CMT == 3) %>%
ggplot(aes(x = TIME, y = DV, col = as.factor(ID))) +
geom_line() +
geom_point() +
facet_wrap(~ROUTE, scale = 'free_y')
ggplotly(p1)