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
|
#$Id$
# Generates fitted curves so the GUI can plot them
# Based on code in IRLSkinfit
# Modifications developed by Tessella for Syngenta: Copyright (C) 2011-2016 Syngenta
# Tessella Project Reference: 6245, 7247, 8361, 7414
#
# The CAKE R modules are free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
CakeDoPlot <- function(fit, xlim = range(fit$data$time), ...)
{
t.map.names <- names(fit$map)
metabolites <- grep("[A-Z]\\d", t.map.names, value = TRUE)
t.map.rest <- setdiff(t.map.names, metabolites)
# Generate the normal graphs.
final <- CakeSinglePlot(fit, xlim)
final_scaled <- final
if(length(metabolites) > 0){
for(i in 1:length(metabolites))
{
metabolite <- metabolites[i]
if (names(fit$map[[metabolite]])[1] != "SFO"){
# We do not support these curves for models other than SFO (for now; see CU-79).
next
}
fixed <- fit$fixed$value
names(fixed) <- rownames(fit$fixed)
parms.all <- c(fit$par, fixed)
ininames <- c(
rownames(subset(fit$start, type == "state")),
rownames(subset(fit$fixed, type == "state")))
odeini <- parms.all[ininames]
metabolite0Parameter <- metabolite
if (!(metabolite %in% names(odeini))){
metabolite0Parameter <- paste0(metabolite, "_0")
}
if (odeini[[metabolite0Parameter]] != 0){
# We do not support these curves where the initial concentration is non-zero (for now; see CU-79).
next
}
decay_var <- paste("k", metabolite, sep="_")
# calculate the new ffm (formation factor) and generate the two ffm scale charts
regex <- paste("f_(.+)_to", metabolite, sep="_")
decays = grep(regex, names(fit$par), value = TRUE)
if (length(decays) != 1){
# We do not support these curves where there is formation from more than 1 compartment (for now; see CU-79).
next
}
ffm_fitted <- sum(fit$par[decays])
normal <- final
ffm_scale <- NULL
numeric_DT50 <- as.numeric(fit$distimes[metabolite,][["DT50"]])
if (is.na(numeric_DT50)){
# We can't get anywhere without a numeric DT50.
next
}
# Generate the curve for DT50=1000d and ffm as fitted.
if (decay_var %in% names(fit$par)){
k_new <- fit$par[decay_var]*numeric_DT50/1000;
fit$par[decay_var]<- k_new
}
else{
# If decay_var was fixed, need to modify it there.
k_new <- fit$fixed[decay_var,]["value"]*numeric_DT50/1000;
fit$fixed[decay_var,]["value"] <- k_new[decay_var,]
}
dt50_1000_ffm_fitted <- CakeSinglePlot(fit, xlim)[metabolite]
naming <- c(names(final), paste(metabolite, "DT50_1000_FFM_FITTED", sep="_"))
normal <- c(final, dt50_1000_ffm_fitted)
names(normal) <- naming
final <- normal
# Generate the scaled FFM
if(ffm_fitted != 0)
{
ffm_scale <- 1 / ffm_fitted
final_scaled <- final[c("time", metabolite, paste(metabolite, "DT50_1000_FFM_FITTED", sep="_"))]
final_scaled[t.map.rest] <- NULL;
final_frame <- as.data.frame(final_scaled)
new_names <- c(paste(metabolite, "DT50_FITTED_FFM_1", sep="_"), paste(metabolite, "DT50_1000_FFM_1", sep="_"))
names(final_frame) <- c("time", new_names)
final_frame[new_names]<-final_frame[new_names]*ffm_scale;
cat("<PLOT MODEL START>\n")
write.table(final_frame, quote=FALSE)
cat("<PLOT MODEL END>\n")
}
}
}
cat("<PLOT MODEL START>\n")
write.table(final, quote=FALSE)
cat("<PLOT MODEL END>\n")
# View(final)
}
CakeSinglePlot <- function(fit, xlim = range(fit$data$time), scale_x = 1.0, ...)
{
solution = fit$solution
if ( is.null(solution) ) {
solution <- "deSolve"
}
atol = fit$atol
if ( is.null(atol) ) {
atol = 1.0e-6
}
fixed <- fit$fixed$value
names(fixed) <- rownames(fit$fixed)
parms.all <- c(fit$par, fixed)
ininames <- c(
rownames(subset(fit$start, type == "state")),
rownames(subset(fit$fixed, type == "state")))
odeini <- parms.all[ininames]
names(odeini) <- gsub("_0$", "", names(odeini))
odenames <- c(
rownames(subset(fit$start, type == "deparm")),
rownames(subset(fit$fixed, type == "deparm")))
odeparms <- parms.all[odenames]
odeini <- AdjustOdeInitialValues(odeini, fit, odeparms)
outtimes <- seq(0, xlim[2], length.out=CAKE.plots.resolution) * scale_x
# Solve the system
evalparse <- function(string)
{
eval(parse(text=string), as.list(c(odeparms, odeini)))
}
if (solution == "analytical") {
parent.type = names(fit$map[[1]])[1]
parent.name = names(fit$diffs)[[1]]
o <- switch(parent.type,
SFO = SFO.solution(outtimes,
evalparse(parent.name),
evalparse(paste("k", parent.name, sep="_"))),
FOMC = FOMC.solution(outtimes,
evalparse(parent.name),
evalparse("alpha"), evalparse("beta")),
DFOP = DFOP.solution(outtimes,
evalparse(parent.name),
evalparse(paste("k1", parent.name, sep="_")),
evalparse(paste("k2", parent.name, sep="_")),
evalparse(paste("g", parent.name, sep="_"))),
HS = HS.solution(outtimes,
evalparse(parent.name),
evalparse("k1"), evalparse("k2"),
evalparse("tb")),
IORE = IORE.solution(outtimes,
evalparse(parent.name),
evalparse(paste("k", parent.name, sep="_")),
evalparse("N"))
)
out <- cbind(outtimes, o)
dimnames(out) <- list(outtimes, c("time", parent.name))
}
if (solution == "eigen") {
coefmat.num <- matrix(sapply(as.vector(fit$coefmat), evalparse),
nrow = length(odeini))
e <- eigen(coefmat.num)
c <- solve(e$vectors, odeini)
f.out <- function(t) {
e$vectors %*% diag(exp(e$values * t), nrow=length(odeini)) %*% c
}
o <- matrix(mapply(f.out, outtimes),
nrow = length(odeini), ncol = length(outtimes))
dimnames(o) <- list(names(odeini), NULL)
out <- cbind(time = outtimes, t(o))
}
if (solution == "deSolve") {
out <- ode(
y = odeini,
times = outtimes,
func = fit$mkindiff,
parms = odeparms,
atol = atol
)
}
out_transformed <- PostProcessOdeOutput(out, fit, atol)
# Replace values that are incalculably small with 0.
for (compartment.name in names(fit$map)) {
if (length(out_transformed[, compartment.name][!is.nan(out_transformed[, compartment.name])]) > 0) {
out_transformed[, compartment.name][is.nan(out_transformed[, compartment.name])] <- 0
}
out_transformed[, compartment.name][out_transformed[, compartment.name] < 0] <- 0
}
return(out_transformed)
}
|
