-
Notifications
You must be signed in to change notification settings - Fork 0
/
table_script.r
358 lines (312 loc) · 15.8 KB
/
table_script.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
# Remove any previous objects in the workspace
rm(list=ls(all=TRUE))
graphics.off()
source("functions_NCAvNLME_2016.r")
library(plyr)
library(ggplot2)
library(reshape2)
# Choose the directory you wish to compile results from
master.dir <- "E:/hscpw-df1/Data1/Jim Hughes/2016"
setwd(master.dir)
file.name <- "collated_bioq_table.csv"
dir.r16 <- "01_MDV"
df.r16 <- read.csv(paste(dir.r16, file.name, sep="/"))
dir.r78 <- "04_SS"
df.r78 <- read.csv(paste(dir.r78, file.name, sep="/"))
dir.r910 <- "06_genKA"
df.r910 <- read.csv(paste(dir.r910, file.name, sep="/"))
dir.r1114 <- "07_noBOV"
df.r1114 <- read.csv(paste(dir.r1114, file.name, sep="/"))
df.all <- rbind(df.r16, df.r78, df.r910, df.r1114)
df.sub1 <- df.all[1:29]
df.sub2 <- df.all[30:43]
shk.file.name <- "collatedterm_shrinkage_data.csv"
shk.r16 <- read.csv(paste(dir.r16, shk.file.name, sep="/"))
shk.r78 <- read.csv(paste(dir.r78, shk.file.name, sep="/"))
shk.r910 <- read.csv(paste(dir.r910, shk.file.name, sep="/"))
shk.r1114 <- read.csv(paste(dir.r1114, shk.file.name, sep="/"))
names(shk.r1114)[9:10] <- c("BOVV21", "BOVV22")
shk.all <- rbind(shk.r16, shk.r78, shk.r910, shk.r1114)
nsim <- 500
nbioq <- df.all$IPREDBE*nsim/100
nbioq.m1 <- df.all$M1IPREDBE*df.all$M1NSIM/100
nbioq.m3 <- df.all$M3IPREDBE*df.all$M3NSIM/100
nnonb <- nsim - nbioq
nnonb.m1 <- df.all$M1NSIM - nbioq.m1
nnonb.m3 <- df.all$M3NSIM - nbioq.m3
cnbioq <- df.all$IPREDCM*nsim/100
cnbioq.m1 <- df.all$M1IPREDCM*df.all$M1NSIM/100
cnbioq.m3 <- df.all$M3IPREDCM*df.all$M3NSIM/100
cnnonb <- nsim - cnbioq
cnnonb.m1 <- df.all$M1NSIM - cnbioq.m1
cnnonb.m3 <- df.all$M3NSIM - cnbioq.m3
ncaTP <- nbioq - df.all$NCAFT2*nsim/100
ncaTN <- nnonb - df.all$NCAFT1*nsim/100
cncaTP <- cnbioq - df.all$NCACT2*nsim/100
cncaTN <- cnnonb - df.all$NCACT1*nsim/100
m1f1TP <- nbioq.m1 - df.all$M1F1FT2*df.all$M1NSIM/100
m1f1TN <- nnonb.m1 - df.all$M1F1FT1*df.all$M1NSIM/100
cm1TP <- cnbioq.m1 - df.all$M1CT2*df.all$M1NSIM/100
cm1TN <- cnnonb.m1 - df.all$M1CT1*df.all$M1NSIM/100
m1phTP <- nbioq.m1 - df.all$M1PHFT2*df.all$M1NSIM/100
m1phTN <- nnonb.m1 - df.all$M1PHFT1*df.all$M1NSIM/100
m3f1TP <- nbioq.m3 - df.all$M3F1FT2*df.all$M3NSIM/100
m3f1TN <- nnonb.m3 - df.all$M3F1FT1*df.all$M3NSIM/100
cm3TP <- cnbioq.m3 - df.all$M3CT2*df.all$M3NSIM/100
cm3TN <- cnnonb.m3 - df.all$M3CT1*df.all$M3NSIM/100
m3phTP <- nbioq.m3 - df.all$M3PHFT2*df.all$M3NSIM/100
m3phTN <- nnonb.m3 - df.all$M3PHFT1*df.all$M3NSIM/100
df.sub1$NCAFSENS <- ncaTP/nbioq*100
df.sub1$M1F1FSENS <- m1f1TP/nbioq.m1*100
df.sub1$M1PHFSENS <- m1phTP/nbioq.m1*100
df.sub1$M3F1FSENS <- m3f1TP/nbioq.m3*100
df.sub1$M3PHFSENS <- m3phTP/nbioq.m3*100
df.sub1$NCAFSPEC <- ncaTN/nnonb*100
df.sub1$M1F1FSPEC <- m1f1TN/nnonb.m1*100
df.sub1$M1PHFSPEC <- m1phTN/nnonb.m1*100
df.sub1$M3F1FSPEC <- m3f1TN/nnonb.m3*100
df.sub1$M3PHFSPEC <- m3phTN/nnonb.m3*100
df.sub1$NCAFACC <- (ncaTP+ncaTN)/nsim*100
df.sub1$M1F1FACC <- (m1f1TP+m1f1TN)/df.all$M1NSIM*100
df.sub1$M1PHFACC <- (m1phTP+m1phTN)/df.all$M1NSIM*100
df.sub1$M3F1FACC <- (m3f1TP+m3f1TN)/df.all$M3NSIM*100
df.sub1$M3PHFACC <- (m3phTP+m3phTN)/df.all$M3NSIM*100
df.sub1$NCACSENS <- cncaTP/nbioq*100
df.sub1$M1CSENS <- cm1TP/nbioq.m1*100
df.sub1$M3CSENS <- cm3TP/nbioq.m3*100
df.sub1$NCACSPEC <- cncaTN/nnonb*100
df.sub1$M1CSPEC <- cm1TN/nnonb.m1*100
df.sub1$M3CSPEC <- cm3TN/nnonb.m3*100
df.sub1$NCACACC <- (cncaTP+cncaTN)/nsim*100
df.sub1$M1CACC <- (cm1TP+cm1TN)/df.all$M1NSIM*100
df.sub1$M3CACC <- (cm3TP+cm3TN)/df.all$M3NSIM*100
df.final <- data.frame(df.sub1, df.sub2)
df.final.all <- df.final[df.final$TERMSTAT == "All", ]
df.final.suc <- df.final[df.final$TERMSTAT == "Only Success", ]
write.csv(df.final.suc,"results_successonly.csv",row.names=F)
write.csv(df.final.suc,"results_all.csv",row.names=F)
# PLOT SET 1
#Determine median, upper lower bounds of the difference between using
#all runs and only using successfully minimised runs
df.diff <- data.frame(
M1F1.F.SPEC = df.final.suc$M1F1FSPEC - df.final.all$M1F1FSPEC,
M1PH.F.SPEC = df.final.suc$M1PHFSPEC - df.final.all$M1PHFSPEC,
M3F1.F.SPEC = df.final.suc$M3F1FSPEC - df.final.all$M3F1FSPEC,
M3PH.F.SPEC = df.final.suc$M3PHFSPEC - df.final.all$M3PHFSPEC,
M1F1.F.SENS = df.final.suc$M1F1FSENS - df.final.all$M1F1FSENS,
M1PH.F.SENS = df.final.suc$M1PHFSENS - df.final.all$M1PHFSENS,
M3F1.F.SENS = df.final.suc$M3F1FSENS - df.final.all$M3F1FSENS,
M3PH.F.SENS = df.final.suc$M3PHFSENS - df.final.all$M3PHFSENS,
M1F1.F.ACC = df.final.suc$M1F1FACC - df.final.all$M1F1FACC,
M1PH.F.ACC = df.final.suc$M1PHFACC - df.final.all$M1PHFACC,
M3F1.F.ACC = df.final.suc$M3F1FACC - df.final.all$M3F1FACC,
M3PH.F.ACC = df.final.suc$M3PHFACC - df.final.all$M3PHFACC,
M1.C.SPEC = df.final.suc$M1CSPEC - df.final.all$M1CSPEC,
M3.C.SPEC = df.final.suc$M3CSPEC - df.final.all$M3CSPEC,
M1.C.SENS = df.final.suc$M1CSENS - df.final.all$M1CSENS,
M3.C.SENS = df.final.suc$M3CSENS - df.final.all$M3CSENS,
M1.C.ACC = df.final.suc$M1CACC - df.final.all$M1CACC,
M3.C.ACC = df.final.suc$M3CACC - df.final.all$M3CACC)
#colwise(summary)(df.diff)
df.final.all.M1F1FSPEC <- df.final.all$M1F1FSPEC
df.final.all.M1F1FSPEC[df.final.all.M1F1FSPEC == 0] <- 0.0000001
df.ratio <- data.frame(
M1F1.F.SPEC = df.final.suc$M1F1FSPEC/df.final.all.M1F1FSPEC,
M1PH.F.SPEC = df.final.suc$M1PHFSPEC/df.final.all$M1PHFSPEC,
M3F1.F.SPEC = df.final.suc$M3F1FSPEC/df.final.all$M3F1FSPEC,
M3PH.F.SPEC = df.final.suc$M3PHFSPEC/df.final.all$M3PHFSPEC,
M1F1.F.SENS = df.final.suc$M1F1FSENS/df.final.all$M1F1FSENS,
M1PH.F.SENS = df.final.suc$M1PHFSENS/df.final.all$M1PHFSENS,
M3F1.F.SENS = df.final.suc$M3F1FSENS/df.final.all$M3F1FSENS,
M3PH.F.SENS = df.final.suc$M3PHFSENS/df.final.all$M3PHFSENS,
M1F1.F.ACC = df.final.suc$M1F1FACC/df.final.all$M1F1FACC,
M1PH.F.ACC = df.final.suc$M1PHFACC/df.final.all$M1PHFACC,
M3F1.F.ACC = df.final.suc$M3F1FACC/df.final.all$M3F1FACC,
M3PH.F.ACC = df.final.suc$M3PHFACC/df.final.all$M3PHFACC,
M1.C.SPEC = df.final.suc$M1CSPEC/df.final.all$M1CSPEC,
M3.C.SPEC = df.final.suc$M3CSPEC/df.final.all$M3CSPEC,
M1.C.SENS = df.final.suc$M1CSENS/df.final.all$M1CSENS,
M3.C.SENS = df.final.suc$M3CSENS/df.final.all$M3CSENS,
M1.C.ACC = df.final.suc$M1CACC/df.final.all$M1CACC,
M3.C.ACC = df.final.suc$M3CACC/df.final.all$M3CACC)
#colwise(summary)(df.ratio)
df.diff.l <- melt(df.diff)
df.diff.l <- data.frame(colsplit(df.diff.l$variable, pattern = "\\.",
names = c("method", "bioq", "stat")), value = df.diff.l$value)
df.ratio.l <- melt(df.ratio)
df.ratio.l <- data.frame(colsplit(df.ratio.l$variable, pattern = "\\.",
names = c("method", "bioq", "stat")), value = df.ratio.l$value)
df.diff.l$statf <- factor(df.diff.l$stat)
levels(df.diff.l$statf) <- c("Accuracy", "Sensitivity", "Specificity")
df.ratio.l$statf <- factor(df.ratio.l$stat)
levels(df.ratio.l$statf) <- c("Accuracy", "Sensitivity", "Specificity")
theme_set(theme_bw())
titletext1 <- expression(atop("Change in Results (Accuracy, Sensitivity, Specificity)",
atop("Difference between using only successfully minimised runs and using all runs to determine bioequivalence")))
plotobj1 <- NULL
plotobj1 <- ggplot(data = df.diff.l[df.diff.l$bioq == "F", ])
plotobj1 <- plotobj1 + ggtitle(titletext1)
plotobj1 <- plotobj1 + geom_boxplot(aes(factor(method), value))
plotobj1 <- plotobj1 + scale_x_discrete("\nMethod")
plotobj1 <- plotobj1 + scale_y_continuous("Change in Percentage\n")
plotobj1 <- plotobj1 + facet_wrap(~statf)
plotobj1
ggsave("DiffPlot_F1.png", width=20, height=16, units=c("cm"))
CI90lo <- function(x) quantile(x,probs = 0.05)
CI90hi <- function(x) quantile(x,probs = 0.95)
df.diff.stat <- ddply(df.diff.l, .(method, bioq, statf), function(x) {
c(CI90lo(x$value), mean(x$value), CI90hi(x$value))
})
names(df.diff.stat)[4:6] <- c("ci90lo", "mean", "ci90hi")
titletext1 <- expression(atop("Change in Results (Accuracy, Sensitivity, Specificity)",
atop("CIs of difference between use of successfully minimised runs and all runs to determine bioequivalence")))
plotobj1a <- NULL
plotobj1a <- ggplot(data = df.diff.stat[df.diff.stat$bioq == "F", ], aes(factor(method), mean))
#plotobj1a <- plotobj1a + ggtitle(titletext1)
plotobj1a <- plotobj1a + geom_point()
plotobj1a <- plotobj1a + geom_errorbar(aes(ymin = ci90lo, ymax = ci90hi), width = 0.5)
plotobj1a <- plotobj1a + scale_x_discrete("\nMethod")
plotobj1a <- plotobj1a + scale_y_continuous("Change in Percentage\n",
labels = dollar_format(suffix = "%", prefix = ""), lim = c(-8, 8))
plotobj1a <- plotobj1a + facet_wrap(~statf)
plotobj1a
ggsave("CIDiffPlot_F1_lim.png", width=20, height=8, units=c("cm"))
# plotobj2 <- NULL
# plotobj2 <- ggplot(data = df.diff.l[df.diff.l$bioq == "C", ])
# plotobj2 <- plotobj2 + ggtitle(titletext1)
# plotobj2 <- plotobj2 + geom_boxplot(aes(factor(method), value))
# plotobj2 <- plotobj2 + scale_x_discrete("\nMethod")
# plotobj2 <- plotobj2 + scale_y_continuous("Change in Percentage\n")
# plotobj2 <- plotobj2 + facet_wrap(~statf)
# plotobj2
# ggsave("DiffPlot_CM.png", width=20, height=16, units=c("cm"))
# PLOT SET 2
#Identify patterns that show change in shrinkages relating to Type 1 error
#Do this by finding median, upper and lower bounds of shrinkages
shk.all$Scen <- shk.all$SCEN
shk.all$Scen[shk.all$RUN %% 2 == 0] <- shk.all$SCEN[shk.all$RUN %% 2 == 0] + 9
shk.all$Run <- ceiling(shk.all$RUN/2)
shk.all.l <- melt(shk.all[shk.all$Term == "Success", ], c("RUN","SCEN","Run","Scen","Sim","Method","Term"))
shk.all.l$RunVar <- paste("Run",shk.all.l$Run,shk.all.l$variable)
titletext3 <- expression(atop("Box Plot for Shrinkages on each ETA",
atop("Shrinkages grouped by Simulation Run")))
plotobj3 <- NULL
plotobj3 <- ggplot(data = shk.all.l)
plotobj3 <- plotobj3 + ggtitle(titletext3)
plotobj3 <- plotobj3 + geom_boxplot(aes(factor(Run), value))
plotobj3 <- plotobj3 + scale_x_discrete("\nRun")
plotobj3 <- plotobj3 + scale_y_continuous("Shrinkage (%)\n", lim = c(0, 100))
plotobj3 <- plotobj3 + facet_wrap(~variable)
plotobj3
ggsave("SHKplot_byRun.png", width=20, height=16, units=c("cm"))
titletext4 <- expression(atop("Box Plot for Shrinkages on each ETA",
atop("Shrinkages grouped by Scenario")))
plotobj4 <- NULL
plotobj4 <- ggplot(data = shk.all.l)
plotobj4 <- plotobj4 + ggtitle(titletext4)
plotobj4 <- plotobj4 + geom_boxplot(aes(factor(Scen), value))
plotobj4 <- plotobj4 + scale_x_discrete("\nScenario")
plotobj4 <- plotobj4 + scale_y_continuous("Shrinkage (%)\n", lim = c(0, 100))
plotobj4 <- plotobj4 + facet_wrap(~variable)
plotobj4
ggsave("SHKplot_byScen.png", width=20, height=16, units=c("cm"))
titletext5 <- expression(atop("Box Plot for Shrinkages on each ETA",
atop("Shrinkages grouped by Run and Scenario")))
plotobj5 <- NULL
plotobj5 <- ggplot(data = shk.all.l)
plotobj5 <- plotobj5 + ggtitle(titletext5)
plotobj5 <- plotobj5 + geom_boxplot(aes(factor(Scen), value), outlier.size = 0.5)
plotobj5 <- plotobj5 + scale_x_discrete("\nScenario")
plotobj5 <- plotobj5 + scale_y_continuous("Shrinkage (%)\n", lim = c(0, 100))
plotobj5 <- plotobj5 + facet_wrap(~RunVar)
#plotobj5
ggsave("SHKplot_byScen_facetRun.png", width=30, height=24, units=c("cm"))
titletext6 <- expression(atop("Box Plot for Shrinkages on each ETA",
atop("Shrinkages grouped by Run and Scenario")))
plotobj6 <- NULL
plotobj6 <- ggplot(data = shk.all.l[shk.all.l$RunVar == "", ])
plotobj6 <- plotobj6 + ggtitle(titletext6)
plotobj6 <- plotobj6 + geom_boxplot(aes(factor(Scen), value), outlier.size = 0.5)
plotobj6 <- plotobj6 + scale_x_discrete("\nScenario")
plotobj6 <- plotobj6 + scale_y_continuous("Shrinkage (%)\n", lim = c(0, 100))
#plotobj6 <- plotobj6 + facet_wrap(~RunVar)
#plotobj6
ggsave("SHKplot_byScen_facetRun.png", width=30, height=24, units=c("cm"))
# PLOT SET 3
#Identify patterns that show change in shrinkages relating to Type 1 error
#Do this by plotting median shrinkages against type 1 error percentage
shk.median <- dcast(
ddply(na.omit(shk.all.l), .(Run, Scen, Method, variable),
function(x) summary(x$value)["Median"]),
Run+Scen+Method ~ variable
)
shk.median.d <- arrange(rbind(shk.median, shk.median), Run, Scen, Method)
shk.median.d$Meth <- c("M1F1","M1PH","M3F1","M3PH")
df.suc.sub <- df.final.suc[c(1:2, 15:18)]
names(df.suc.sub)[3:6] <- c("M1F1","M1PH","M3F1","M3PH")
df.suc.l <- arrange(melt(df.suc.sub, c("RUN", "SCEN")), RUN, SCEN, variable)
shk.t1.df <- data.frame(
shk.median.d[-3],
T1 = df.suc.l$value
)
shk.t1.df.l <- melt(shk.t1.df, c("Run", "Scen", "Meth", "T1"))
shk.t1.r2 <- ddply(shk.t1.df.l, .(Meth, variable), function(x) summary(lm(x$T1 ~ x$value))$r.squared)
titletext7 <- expression(atop("Percent ETA Shrinkage versus Type 1 Error",
atop("Using M1 and the \"F estimate\" method ")))
plotobj7 <- NULL
plotobj7 <- ggplot(data = shk.t1.df.l[shk.t1.df.l$Meth == "M1F1", ])
plotobj7 <- plotobj7 + ggtitle(titletext7)
plotobj7 <- plotobj7 + geom_point(aes(x = value, y = T1))
plotobj7 <- plotobj7 + scale_x_continuous("Shrinkage (%)")
plotobj7 <- plotobj7 + scale_y_continuous("Type 1 Error (%)")
plotobj7 <- plotobj7 + facet_wrap(~ variable, nrow = 2, ncol = 4)
plotobj7
titletext8 <- expression(atop("Percent ETA Shrinkage versus Type 1 Error",
atop("Using M1 and the \"Post-Hoc\" method ")))
plotobj8 <- NULL
plotobj8 <- ggplot(data = shk.t1.df.l[shk.t1.df.l$Meth == "M1PH", ])
plotobj8 <- plotobj8 + ggtitle(titletext8)
plotobj8 <- plotobj8 + geom_point(aes(x = value, y = T1))
plotobj8 <- plotobj8 + scale_x_continuous("Shrinkage (%)")
plotobj8 <- plotobj8 + scale_y_continuous("Type 1 Error (%)")
plotobj8 <- plotobj8 + facet_wrap(~ variable, nrow = 2, ncol = 4)
plotobj8
titletext9 <- expression(atop("Percent ETA Shrinkage versus Type 1 Error",
atop("Using M3 and the \"F estimate\" method ")))
plotobj9 <- NULL
plotobj9 <- ggplot(data = shk.t1.df.l[shk.t1.df.l$Meth == "M3F1", ])
plotobj9 <- plotobj9 + ggtitle(titletext9)
plotobj9 <- plotobj9 + geom_point(aes(x = value, y = T1))
plotobj9 <- plotobj9 + scale_x_continuous("Shrinkage (%)")
plotobj9 <- plotobj9 + scale_y_continuous("Type 1 Error (%)")
plotobj9 <- plotobj9 + facet_wrap(~ variable, nrow = 2, ncol = 4)
plotobj9
titletext0 <- expression(atop("Percent ETA Shrinkage versus Type 1 Error",
atop("Using M3 and the \"Post-Hoc\" method ")))
plotobj0 <- NULL
plotobj0 <- ggplot(data = shk.t1.df.l[shk.t1.df.l$Meth == "M3PH", ])
plotobj0 <- plotobj0 + ggtitle(titletext0)
plotobj0 <- plotobj0 + geom_point(aes(x = value, y = T1))
plotobj0 <- plotobj0 + scale_x_continuous("Shrinkage (%)")
plotobj0 <- plotobj0 + scale_y_continuous("Type 1 Error (%)")
plotobj0 <- plotobj0 + facet_wrap(~ variable, nrow = 2, ncol = 4)
plotobj0
shk.grid.df <- shk.t1.df.l[shk.t1.df.l$variable %in% c("BSVCL", "BSVV2", "BSVKA", "BSVF1"), ]
shk.grid.df$MethVar <- factor(paste(shk.grid.df$Meth, shk.grid.df$variable),
levels = c(
"M1F1 BSVCL", "M1F1 BSVV2", "M1PH BSVCL", "M1PH BSVV2",
"M1F1 BSVKA", "M1F1 BSVF1", "M1PH BSVKA", "M1PH BSVF1",
"M3F1 BSVCL", "M3F1 BSVV2", "M3PH BSVCL", "M3PH BSVV2",
"M3F1 BSVKA", "M3F1 BSVF1", "M3PH BSVKA", "M3PH BSVF1")
)
titletext10 <- expression(atop("Percent BSV ETA Shrinkage versus Type 1 Error",
atop("Split into different methods of determining bioequivalence")))
plotobj10 <- NULL
plotobj10 <- ggplot(data = shk.grid.df)
plotobj10 <- plotobj10 + ggtitle(titletext10)
plotobj10 <- plotobj10 + geom_point(aes(x = value, y = T1), size = 0.5)
plotobj10 <- plotobj10 + scale_x_continuous("Shrinkage (%)")
plotobj10 <- plotobj10 + scale_y_continuous("Type 1 Error (%)")
plotobj10 <- plotobj10 + facet_wrap(~ MethVar, nrow = 4, ncol = 4)
plotobj10
ggsave("SHKT1plot_grid.png", width=20, height=16, units=c("cm"))