Introduction

This file details the analyses performed in R to obtain the figures presented in Vea et al. Differential juvenile hormone modulations establish extreme sexual dimorphism in scale insects.

Data

This script uses 2 files obtained from qRT-PCR (see Materials and Methods of main text for equipment). All values are the SDM. - expressionprofile.csv: data for expression profiles - Pyri5mM.csv: data for effect of JHM treatments on male development

## Warning: package 'ggplot2' was built under R version 3.1.3

## Warning: package 'plyr' was built under R version 3.1.3

Loading datasets

setwd("/Users/ivea/Dropbox/_Github_reps/mealybugJH/")
dataA<-read.csv(file="expressionprofile.csv",header =TRUE)
dataB<-read.csv(file="Pyri5mM.csv",header = TRUE)

#reshaping data
dataA2<-gather(dataA,Gene,SDM,7:24)

#summarizing by gene, day after hatching and sex
dataA3<-ddply(dataA2,~Day.after.hatching +Sex+Gene,summarise,meanrp49=mean(SDM.rp49.2),meanSDM=mean(SDM))

#normalize dataB with housekeeping gene
dataB$gene.normal<-dataB$SDM.gene/dataB$rp49.2

#making mean and error values
dataBs<-summarySE(dataB, measurevar="gene.normal", groupvars=c("Treatment","Stage.treated","Gene"))

Expression profiles

Figure 2: Expression profiles of PkJHAMT, PkMet, PkTai, PkKr-h1-common

library(dplyr)

## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Figure2<-subset(dataA3,Gene=="SDM.JHAMT"|Gene=="SDM.Met"|Gene=="SDM.Tai"|Gene=="SDM.Pkkr.h1_26")

pFig1<-ggplot(Figure2, aes(x=Day.after.hatching,y=meanSDM/meanrp49,group=Sex)) +
  geom_point(aes(linetype=Sex),size=2)+
  geom_line(aes(linetype=Sex),size=0.5)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+
  ylab("Gene relative expression (/rpL32)") +xlab("days after oviposition") +
  theme_bw(15) + 
  theme(axis.title.x = element_text(colour = "#242424"))

pFig1+facet_grid(Gene~.,scales="free")

Figure 3: Effect on Met and Kr-h1

Figure 3C: PkMet

#boxplot

PkMetbox<-subset(dataB, Gene=="B.Met")
PkMetbox<-select(PkMetbox, gene.normal,Stage.treated, Treatment)
Metbox<-ggplot(data = PkMetbox, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) +
  ylab("PkMet relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey"))+
  scale_y_continuous(breaks=seq(0,2,by = 0.1)) + theme_bw(25)

Metbox

postscript("Figure3C-PkMetboxplot.ps")
Metbox
dev.off()

## quartz_off_screen 
##                 2

Statistical test Met

prepupatestMet<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="B.Met")
t.test(gene.normal~Treatment,data=prepupatestMet)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 0.2361, df = 9.06, p-value = 0.8186
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.04229839  0.05216887
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##                0.1313775                0.1264422

pupatestMet<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="B.Met")
t.test(gene.normal~Treatment,data=pupatestMet)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 1.3333, df = 5.846, p-value = 0.232
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.09264911  0.31141234
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##                0.3916784                0.2822968

Figure 3-D to F: Tai core, Tai-IN and Tai-DEL

PkTaibox<-subset(dataB, Gene=="O.Tai")
PkTaibox

##     Sample  Treatment Stage.treated  Gene SDM.gene   rp49.1   rp49.2
## 323 PyrT21 A.Methanol         PreD1 O.Tai 0.000438 0.001770 0.002420
## 324 PyrT11 A.Methanol         PreD1 O.Tai 0.000491 0.004210 0.004680
## 325 PyrT19 A.Methanol         PreD1 O.Tai 0.000648 0.002010 0.002680
## 326 PyrT20 A.Methanol         PreD1 O.Tai 0.000817 0.005270 0.004500
## 327 PyrT30 A.Methanol         PreD1 O.Tai 0.000700 0.003630 0.003430
## 328 PyrT37 A.Methanol         PreD1 O.Tai 0.000830 0.005320 0.005150
## 329 PyrT27 A.Methanol          PuD0 O.Tai 0.001010 0.000354 0.000534
## 330 PyrT34 A.Methanol          PuD0 O.Tai 0.000324 0.001180 0.001380
## 331 PyrT35 A.Methanol          PuD0 O.Tai 0.001050 0.001390 0.000939
## 332 PyrT36 A.Methanol          PuD0 O.Tai 0.001040 0.000917 0.001220
## 333 PyrT44 A.Methanol          PuD0 O.Tai 0.000244 0.000394 0.000717
## 334 PyrT54      B.5mM         PreD1 O.Tai 0.000324 0.000786 0.001430
## 335 PyrT55      B.5mM         PreD1 O.Tai 0.000629 0.002350 0.003510
## 336 PyrT56      B.5mM         PreD1 O.Tai 0.000431 0.001280 0.002580
## 337 PyrT57      B.5mM         PreD1 O.Tai 0.000805 0.001690 0.003140
## 338 PyrT61      B.5mM         PreD1 O.Tai 0.000408 0.002080 0.002640
## 339 PyrT62      B.5mM         PreD1 O.Tai 0.001080 0.005070 0.003120
## 340 PyrT52      B.5mM          PuD0 O.Tai 0.000491 0.007550 0.001940
## 341 PyrT58      B.5mM          PuD0 O.Tai 0.000487 0.000361 0.002640
## 342 PyrT59      B.5mM          PuD0 O.Tai 0.000361 0.001370 0.002130
## 343 PyrT60      B.5mM          PuD0 O.Tai 0.000293 0.001480 0.002030
## 344 PyrT63      B.5mM          PuD0 O.Tai 0.000518 0.001020 0.001820
## 345 PyrT64      B.5mM          PuD0 O.Tai 0.000204 0.000464 0.000970
##     gene.normal
## 323   0.1809917
## 324   0.1049145
## 325   0.2417910
## 326   0.1815556
## 327   0.2040816
## 328   0.1611650
## 329   1.8913858
## 330   0.2347826
## 331   1.1182109
## 332   0.8524590
## 333   0.3403068
## 334   0.2265734
## 335   0.1792023
## 336   0.1670543
## 337   0.2563694
## 338   0.1545455
## 339   0.3461538
## 340   0.2530928
## 341   0.1844697
## 342   0.1694836
## 343   0.1443350
## 344   0.2846154
## 345   0.2103093

PkTaibox<-select(PkTaibox, gene.normal,Stage.treated, Treatment)
Taibox<-ggplot(data = PkTaibox, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("PkTai relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.2)) + theme_bw(25)
Taibox

postscript("Figure3-PkTaiboxplot.ps")
Taibox
dev.off()

## quartz_off_screen 
##                 2

PkTaiINbox<-subset(dataB, Gene=="P.Tai.IN")
PkTaiINbox

##     Sample  Treatment Stage.treated     Gene SDM.gene   rp49.1   rp49.2
## 277 PyrT21 A.Methanol         PreD1 P.Tai.IN 0.000302 0.001770 0.002420
## 278 PyrT11 A.Methanol         PreD1 P.Tai.IN 0.000335 0.004210 0.004680
## 279 PyrT19 A.Methanol         PreD1 P.Tai.IN 0.000328 0.002010 0.002680
## 280 PyrT20 A.Methanol         PreD1 P.Tai.IN 0.000450 0.005270 0.004500
## 281 PyrT30 A.Methanol         PreD1 P.Tai.IN 0.000471 0.003630 0.003430
## 282 PyrT37 A.Methanol         PreD1 P.Tai.IN 0.000591 0.005320 0.005150
## 283 PyrT27 A.Methanol          PuD0 P.Tai.IN 0.000736 0.000354 0.000534
## 284 PyrT34 A.Methanol          PuD0 P.Tai.IN 0.000720 0.001180 0.001380
## 285 PyrT35 A.Methanol          PuD0 P.Tai.IN 0.001160 0.001390 0.000939
## 286 PyrT36 A.Methanol          PuD0 P.Tai.IN 0.001000 0.000917 0.001220
## 287 PyrT44 A.Methanol          PuD0 P.Tai.IN 0.000213 0.000394 0.000717
## 288 PyrT54      B.5mM         PreD1 P.Tai.IN 0.000201 0.000786 0.001430
## 289 PyrT55      B.5mM         PreD1 P.Tai.IN 0.000440 0.002350 0.003510
## 290 PyrT56      B.5mM         PreD1 P.Tai.IN 0.000325 0.001280 0.002580
## 291 PyrT57      B.5mM         PreD1 P.Tai.IN 0.000794 0.001690 0.003140
## 292 PyrT61      B.5mM         PreD1 P.Tai.IN 0.000528 0.002080 0.002640
## 293 PyrT62      B.5mM         PreD1 P.Tai.IN 0.000657 0.005070 0.003120
## 294 PyrT52      B.5mM          PuD0 P.Tai.IN 0.000356 0.007550 0.001940
## 295 PyrT58      B.5mM          PuD0 P.Tai.IN 0.000440 0.000361 0.002640
## 296 PyrT59      B.5mM          PuD0 P.Tai.IN 0.000293 0.001370 0.002130
## 297 PyrT60      B.5mM          PuD0 P.Tai.IN 0.000182 0.001480 0.002030
## 298 PyrT63      B.5mM          PuD0 P.Tai.IN 0.000569 0.001020 0.001820
## 299 PyrT64      B.5mM          PuD0 P.Tai.IN 0.000220 0.000464 0.000970
##     gene.normal
## 277  0.12479339
## 278  0.07158120
## 279  0.12238806
## 280  0.10000000
## 281  0.13731778
## 282  0.11475728
## 283  1.37827715
## 284  0.52173913
## 285  1.23535676
## 286  0.81967213
## 287  0.29707113
## 288  0.14055944
## 289  0.12535613
## 290  0.12596899
## 291  0.25286624
## 292  0.20000000
## 293  0.21057692
## 294  0.18350515
## 295  0.16666667
## 296  0.13755869
## 297  0.08965517
## 298  0.31263736
## 299  0.22680412

PkTaiINbox<-select(PkTaiINbox, gene.normal,Stage.treated, Treatment)
TaiINbox<-ggplot(data = PkTaiINbox, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("PkTai IN-1 expression (/rpL32)") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.2)) + theme_bw(25)
TaiINbox

postscript("Figure3-PkTaiINboxplot.ps")
TaiINbox
dev.off()

## quartz_off_screen 
##                 2

#boxplot
PkTaiDELbox<-subset(dataB, Gene=="Q.Tai-DEL")
PkTaiDELbox

##     Sample  Treatment Stage.treated      Gene SDM.gene   rp49.1   rp49.2
## 300 PyrT21 A.Methanol         PreD1 Q.Tai-DEL 2.33e-05 0.001770 0.002420
## 301 PyrT11 A.Methanol         PreD1 Q.Tai-DEL 2.18e-05 0.004210 0.004680
## 302 PyrT19 A.Methanol         PreD1 Q.Tai-DEL 3.30e-05 0.002010 0.002680
## 303 PyrT20 A.Methanol         PreD1 Q.Tai-DEL 5.12e-05 0.005270 0.004500
## 304 PyrT30 A.Methanol         PreD1 Q.Tai-DEL 6.59e-05 0.003630 0.003430
## 305 PyrT37 A.Methanol         PreD1 Q.Tai-DEL 7.69e-05 0.005320 0.005150
## 306 PyrT27 A.Methanol          PuD0 Q.Tai-DEL 2.35e-05 0.000354 0.000534
## 307 PyrT34 A.Methanol          PuD0 Q.Tai-DEL 1.32e-05 0.001180 0.001380
## 308 PyrT35 A.Methanol          PuD0 Q.Tai-DEL 2.80e-05 0.001390 0.000939
## 309 PyrT36 A.Methanol          PuD0 Q.Tai-DEL 2.04e-05 0.000917 0.001220
## 310 PyrT44 A.Methanol          PuD0 Q.Tai-DEL 5.47e-06 0.000394 0.000717
## 311 PyrT54      B.5mM         PreD1 Q.Tai-DEL 1.41e-05 0.000786 0.001430
## 312 PyrT55      B.5mM         PreD1 Q.Tai-DEL 7.04e-05 0.002350 0.003510
## 313 PyrT56      B.5mM         PreD1 Q.Tai-DEL 4.16e-05 0.001280 0.002580
## 314 PyrT57      B.5mM         PreD1 Q.Tai-DEL 6.59e-05 0.001690 0.003140
## 315 PyrT61      B.5mM         PreD1 Q.Tai-DEL 5.24e-05 0.002080 0.002640
## 316 PyrT62      B.5mM         PreD1 Q.Tai-DEL 1.47e-04 0.005070 0.003120
## 317 PyrT52      B.5mM          PuD0 Q.Tai-DEL 2.65e-05 0.007550 0.001940
## 318 PyrT58      B.5mM          PuD0 Q.Tai-DEL 2.43e-05 0.000361 0.002640
## 319 PyrT59      B.5mM          PuD0 Q.Tai-DEL 1.91e-05 0.001370 0.002130
## 320 PyrT60      B.5mM          PuD0 Q.Tai-DEL 1.45e-05 0.001480 0.002030
## 321 PyrT63      B.5mM          PuD0 Q.Tai-DEL 1.12e-05 0.001020 0.001820
## 322 PyrT64      B.5mM          PuD0 Q.Tai-DEL 4.64e-06 0.000464 0.000970
##     gene.normal
## 300 0.009628099
## 301 0.004658120
## 302 0.012313433
## 303 0.011377778
## 304 0.019212828
## 305 0.014932039
## 306 0.044007491
## 307 0.009565217
## 308 0.029818956
## 309 0.016721311
## 310 0.007629010
## 311 0.009860140
## 312 0.020056980
## 313 0.016124031
## 314 0.020987261
## 315 0.019848485
## 316 0.047115385
## 317 0.013659794
## 318 0.009204545
## 319 0.008967136
## 320 0.007142857
## 321 0.006153846
## 322 0.004783505

PkTaiDELbox<-select(PkTaiDELbox, gene.normal,Stage.treated, Treatment)
TaiDELbox<-ggplot(data = PkTaiDELbox, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size =1) + 
  ylab("PkTai DEL-1 expression (/rpL32)") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.01)) + theme_bw(25)
TaiDELbox

postscript("Figure3-PkTaiDELbox.ps")
TaiDELbox
dev.off()

## quartz_off_screen 
##                 2

Statistical tests

prepupatestPkTaiIN<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="P.Tai.IN")
t.test(gene.normal~Treatment,data=prepupatestPkTaiIN)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -2.7159, df = 6.857, p-value = 0.03054
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.120110587 -0.008052751
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##                0.1118063                0.1758880

pupatestPkTaiIN<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="P.Tai.IN")
t.test(gene.normal~Treatment,data=pupatestPkTaiIN)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 3.2001, df = 4.189, p-value = 0.03082
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.09804053 1.23053027
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##                0.8504233                0.1861379

prepupatestPkTaiDEL<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="Q.Tai-DEL")
t.test(gene.normal~Treatment,data=prepupatestPkTaiDEL)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -1.8401, df = 6.438, p-value = 0.112
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.023800974  0.003177646
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##               0.01202038               0.02233205

pupatestPkTaiDEL<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="Q.Tai-DEL")
t.test(gene.normal~Treatment,data=pupatestPkTaiDEL)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 1.904, df = 4.277, p-value = 0.125
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.005580255  0.032039822
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.021548397              0.008318614

prepupatestPkTai<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="O.Tai")
t.test(gene.normal~Treatment,data=prepupatestPkTai)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -1.2219, df = 8.444, p-value = 0.2547
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.12216797  0.03703492
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##                0.1790833                0.2216498

pupatestPkTai<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="O.Tai")
t.test(gene.normal~Treatment,data=pupatestPkTai)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 2.2676, df = 4.042, p-value = 0.08527
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.1491536  1.5085764
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##                0.8874290                0.2077176

Figure 3G to I: PkKr-h1 core, PkKr-h1 A and PkKr-h1 B

PkKrh1<-subset(dataBs,dataBs$Gene=="D.kr-h1")
PkKrh1box<-subset(dataB, Gene=="D.kr-h1")
PkKrh1box

##    Sample  Treatment Stage.treated    Gene SDM.gene   rp49.1   rp49.2
## 1  PyrT21 A.Methanol         PreD1 D.kr-h1 1.87e-05 0.001770 0.002420
## 2  PyrT11 A.Methanol         PreD1 D.kr-h1 1.99e-05 0.004210 0.004680
## 3  PyrT19 A.Methanol         PreD1 D.kr-h1 8.93e-06 0.002010 0.002680
## 4  PyrT20 A.Methanol         PreD1 D.kr-h1 1.64e-05 0.005270 0.004500
## 5  PyrT30 A.Methanol         PreD1 D.kr-h1 1.66e-05 0.003630 0.003430
## 6  PyrT37 A.Methanol         PreD1 D.kr-h1 1.32e-05 0.005320 0.005150
## 7  PyrT27 A.Methanol          PuD0 D.kr-h1 3.24e-05 0.000354 0.000534
## 8  PyrT34 A.Methanol          PuD0 D.kr-h1 1.63e-05 0.001180 0.001380
## 9  PyrT35 A.Methanol          PuD0 D.kr-h1 2.65e-05 0.001390 0.000939
## 10 PyrT36 A.Methanol          PuD0 D.kr-h1 1.80e-05 0.000917 0.001220
## 11 PyrT44 A.Methanol          PuD0 D.kr-h1 1.20e-05 0.000394 0.000717
## 12 PyrT54      B.5mM         PreD1 D.kr-h1 2.61e-04 0.000786 0.001430
## 13 PyrT55      B.5mM         PreD1 D.kr-h1 9.79e-04 0.002350 0.003510
## 14 PyrT56      B.5mM         PreD1 D.kr-h1 4.73e-04 0.001280 0.002580
## 15 PyrT57      B.5mM         PreD1 D.kr-h1 5.55e-04 0.001690 0.003140
## 16 PyrT61      B.5mM         PreD1 D.kr-h1 5.37e-04 0.002080 0.002640
## 17 PyrT62      B.5mM         PreD1 D.kr-h1 9.34e-04 0.005070 0.003120
## 18 PyrT52      B.5mM          PuD0 D.kr-h1 4.85e-04 0.007550 0.001940
## 19 PyrT58      B.5mM          PuD0 D.kr-h1 4.98e-04 0.000361 0.002640
## 20 PyrT59      B.5mM          PuD0 D.kr-h1 2.88e-04 0.001370 0.002130
## 21 PyrT60      B.5mM          PuD0 D.kr-h1 2.44e-04 0.001480 0.002030
## 22 PyrT63      B.5mM          PuD0 D.kr-h1 2.66e-04 0.001020 0.001820
## 23 PyrT64      B.5mM          PuD0 D.kr-h1 1.64e-04 0.000464 0.000970
##    gene.normal
## 1  0.007727273
## 2  0.004252137
## 3  0.003332090
## 4  0.003644444
## 5  0.004839650
## 6  0.002563107
## 7  0.060674157
## 8  0.011811594
## 9  0.028221512
## 10 0.014754098
## 11 0.016736402
## 12 0.182517483
## 13 0.278917379
## 14 0.183333333
## 15 0.176751592
## 16 0.203409091
## 17 0.299358974
## 18 0.250000000
## 19 0.188636364
## 20 0.135211268
## 21 0.120197044
## 22 0.146153846
## 23 0.169072165

PkKrh1box<-select(PkKrh1box, gene.normal,Stage.treated, Treatment)
Krh1box<-ggplot(data = PkKrh1box, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("PkKr-h1 relative amount (/rpL32)") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey"))+
  scale_y_continuous(breaks=seq(0,2,by = 0.05)) + theme_bw(25)

Krh1box

postscript("Figure3-PkKrh1boxplot.ps")
Krh1box
dev.off()

## quartz_off_screen 
##                 2

#boxplot
PkKrh1Abox<-subset(dataB, Gene=="E.Pkkr-h1A")
PkKrh1Abox

##    Sample  Treatment Stage.treated       Gene SDM.gene   rp49.1   rp49.2
## 24 PyrT21 A.Methanol         PreD1 E.Pkkr-h1A 4.43e-06 0.001770 0.002420
## 25 PyrT11 A.Methanol         PreD1 E.Pkkr-h1A 5.73e-06 0.004210 0.004680
## 26 PyrT19 A.Methanol         PreD1 E.Pkkr-h1A 1.93e-06 0.002010 0.002680
## 27 PyrT20 A.Methanol         PreD1 E.Pkkr-h1A 3.32e-06 0.005270 0.004500
## 28 PyrT30 A.Methanol         PreD1 E.Pkkr-h1A 3.74e-06 0.003630 0.003430
## 29 PyrT37 A.Methanol         PreD1 E.Pkkr-h1A 4.30e-06 0.005320 0.005150
## 30 PyrT27 A.Methanol          PuD0 E.Pkkr-h1A 4.80e-06 0.000354 0.000534
## 31 PyrT34 A.Methanol          PuD0 E.Pkkr-h1A 3.63e-06 0.001180 0.001380
## 32 PyrT35 A.Methanol          PuD0 E.Pkkr-h1A 5.40e-06 0.001390 0.000939
## 33 PyrT36 A.Methanol          PuD0 E.Pkkr-h1A 2.44e-06 0.000917 0.001220
## 34 PyrT44 A.Methanol          PuD0 E.Pkkr-h1A 2.03e-06 0.000394 0.000717
## 35 PyrT54      B.5mM         PreD1 E.Pkkr-h1A 6.53e-05 0.000786 0.001430
## 36 PyrT55      B.5mM         PreD1 E.Pkkr-h1A 3.12e-04 0.002350 0.003510
## 37 PyrT56      B.5mM         PreD1 E.Pkkr-h1A 1.42e-04 0.001280 0.002580
## 38 PyrT57      B.5mM         PreD1 E.Pkkr-h1A 1.72e-04 0.001690 0.003140
## 39 PyrT61      B.5mM         PreD1 E.Pkkr-h1A 1.74e-04 0.002080 0.002640
## 40 PyrT62      B.5mM         PreD1 E.Pkkr-h1A 2.19e-04 0.005070 0.003120
## 41 PyrT52      B.5mM          PuD0 E.Pkkr-h1A 8.27e-05 0.007550 0.001940
## 42 PyrT58      B.5mM          PuD0 E.Pkkr-h1A 4.86e-05 0.000361 0.002640
## 43 PyrT59      B.5mM          PuD0 E.Pkkr-h1A 2.52e-05 0.001370 0.002130
## 44 PyrT60      B.5mM          PuD0 E.Pkkr-h1A 3.08e-05 0.001480 0.002030
## 45 PyrT63      B.5mM          PuD0 E.Pkkr-h1A 2.54e-05 0.001020 0.001820
## 46 PyrT64      B.5mM          PuD0 E.Pkkr-h1A 1.42e-05 0.000464 0.000970
##     gene.normal
## 24 0.0018305785
## 25 0.0012243590
## 26 0.0007201493
## 27 0.0007377778
## 28 0.0010903790
## 29 0.0008349515
## 30 0.0089887640
## 31 0.0026304348
## 32 0.0057507987
## 33 0.0020000000
## 34 0.0028312413
## 35 0.0456643357
## 36 0.0888888889
## 37 0.0550387597
## 38 0.0547770701
## 39 0.0659090909
## 40 0.0701923077
## 41 0.0426288660
## 42 0.0184090909
## 43 0.0118309859
## 44 0.0151724138
## 45 0.0139560440
## 46 0.0146391753

PkKrh1Abox<-select(PkKrh1Abox, gene.normal,Stage.treated, Treatment)
Krh1Abox<-ggplot(data = PkKrh1Abox, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("PkKr-h1 A relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.02)) + theme_bw(25)

Krh1Abox

postscript("Figure3-PkKrh1Aboxplot.ps")
Krh1Abox
dev.off()

## quartz_off_screen 
##                 2

#boxplot
PkKrh1Bbox<-subset(dataB, Gene=="F.Pkkr-h1B")
PkKrh1Bbox

##    Sample  Treatment Stage.treated       Gene SDM.gene   rp49.1   rp49.2
## 47 PyrT21 A.Methanol         PreD1 F.Pkkr-h1B 6.83e-06 0.001770 0.002420
## 48 PyrT11 A.Methanol         PreD1 F.Pkkr-h1B 2.66e-08 0.004210 0.004680
## 49 PyrT19 A.Methanol         PreD1 F.Pkkr-h1B 8.81e-08 0.002010 0.002680
## 50 PyrT20 A.Methanol         PreD1 F.Pkkr-h1B 1.14e-08 0.005270 0.004500
## 51 PyrT30 A.Methanol         PreD1 F.Pkkr-h1B 8.27e-09 0.003630 0.003430
## 52 PyrT37 A.Methanol         PreD1 F.Pkkr-h1B 2.64e-08 0.005320 0.005150
## 53 PyrT27 A.Methanol          PuD0 F.Pkkr-h1B 3.34e-08 0.000354 0.000534
## 54 PyrT34 A.Methanol          PuD0 F.Pkkr-h1B 3.03e-07 0.001180 0.001380
## 55 PyrT35 A.Methanol          PuD0 F.Pkkr-h1B 4.83e-08 0.001390 0.000939
## 56 PyrT36 A.Methanol          PuD0 F.Pkkr-h1B 3.21e-07 0.000917 0.001220
## 57 PyrT44 A.Methanol          PuD0 F.Pkkr-h1B 1.21e-06 0.000394 0.000717
## 58 PyrT54      B.5mM         PreD1 F.Pkkr-h1B 9.56e-08 0.000786 0.001430
## 59 PyrT55      B.5mM         PreD1 F.Pkkr-h1B 9.12e-07 0.002350 0.003510
## 60 PyrT56      B.5mM         PreD1 F.Pkkr-h1B 3.64e-07 0.001280 0.002580
## 61 PyrT57      B.5mM         PreD1 F.Pkkr-h1B 2.19e-07 0.001690 0.003140
## 62 PyrT61      B.5mM         PreD1 F.Pkkr-h1B 2.75e-07 0.002080 0.002640
## 63 PyrT62      B.5mM         PreD1 F.Pkkr-h1B 4.78e-07 0.005070 0.003120
## 64 PyrT52      B.5mM          PuD0 F.Pkkr-h1B 2.96e-07 0.007550 0.001940
## 65 PyrT58      B.5mM          PuD0 F.Pkkr-h1B 1.52e-07 0.000361 0.002640
## 66 PyrT59      B.5mM          PuD0 F.Pkkr-h1B 5.59e-08 0.001370 0.002130
## 67 PyrT60      B.5mM          PuD0 F.Pkkr-h1B 1.06e-07 0.001480 0.002030
## 68 PyrT63      B.5mM          PuD0 F.Pkkr-h1B 3.60e-08 0.001020 0.001820
## 69 PyrT64      B.5mM          PuD0 F.Pkkr-h1B 7.64e-07 0.000464 0.000970
##     gene.normal
## 47 2.822314e-03
## 48 5.683761e-06
## 49 3.287313e-05
## 50 2.533333e-06
## 51 2.411079e-06
## 52 5.126214e-06
## 53 6.254682e-05
## 54 2.195652e-04
## 55 5.143770e-05
## 56 2.631148e-04
## 57 1.687587e-03
## 58 6.685315e-05
## 59 2.598291e-04
## 60 1.410853e-04
## 61 6.974522e-05
## 62 1.041667e-04
## 63 1.532051e-04
## 64 1.525773e-04
## 65 5.757576e-05
## 66 2.624413e-05
## 67 5.221675e-05
## 68 1.978022e-05
## 69 7.876289e-04

PkKrh1Bbox<-select(PkKrh1Bbox, gene.normal,Stage.treated, Treatment)
Krh1Bbox<-ggplot(data = PkKrh1Bbox, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8,size=1) + 
  ylab("PkKr-h1 B relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.0005)) + theme_bw(25)

Krh1Bbox

postscript("Figure3H-PkKrh1Bboxplot.ps")
Krh1Bbox
dev.off()

## quartz_off_screen 
##                 2

Statistical tests

#subsetting original data for statistical tests prepupae

prepupatestPkkrh1<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="D.kr-h1")
t.test(gene.normal~Treatment,data=prepupatestPkkrh1)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -9.7799, df = 5.011, p-value = 0.0001878
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.2731421 -0.1595010
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.004393117              0.220714642

pupatestPkkrh1<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="D.kr-h1")
t.test(gene.normal~Treatment,data=pupatestPkkrh1)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -6.7036, df = 7.026, p-value = 0.000272
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.19174301 -0.09180145
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##               0.02643955               0.16821178

prepupatestPkkrh1A<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="E.Pkkr-h1A")
t.test(gene.normal~Treatment,data=prepupatestPkkrh1A)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -10.0172, df = 5.008, p-value = 0.0001681
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.07832843 -0.04634899
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.001073032              0.063411742

pupatestPkkrh1A<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="E.Pkkr-h1A")
t.test(gene.normal~Treatment,data=pupatestPkkrh1A)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -3.0631, df = 5.756, p-value = 0.02335
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.027105631 -0.002892732
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.004440248              0.019439429

prepupatestPkkrh1B<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="F.Pkkr-h1B")
t.test(gene.normal~Treatment,data=prepupatestPkkrh1B)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 0.7367, df = 5.039, p-value = 0.4942
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.0008585921  0.0015506111
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##             0.0004784903             0.0001324807

pupatestPkkrh1B<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="F.Pkkr-h1B")
t.test(gene.normal~Treatment,data=pupatestPkkrh1B)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 0.8213, df = 5.241, p-value = 0.4472
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.0005721867  0.0011205463
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##             0.0004568503             0.0001826705

Figure 5: Broad

Figure 5A: Expression profile of Pkbr1 and Pkbr2

Figure5<-subset(dataA3,Gene=="SDM.Pkbr1"|Gene=="SDM.Pkbr2"|Gene=="SDM.Pkbr3")
pFig5<-ggplot(Figure5, aes(x=Day.after.hatching,y=meanSDM/meanrp49,group=Sex)) +
   geom_point(aes(linetype=Sex))+
   geom_line(aes(linetype=Sex))+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+
  ylab("Pkbr copies relative expression (/rpL32)") +xlab("days after oviposition") +
  theme_bw(15) + 
  theme(axis.title.x = element_text(colour = "#242424"))

pFig5+facet_grid(Gene~.,scale="free")

Figure S7A: Expression profile of Pkbr1Z2, Z4, Pkbr2, Z2 and Z4 and Pkbr3Z2

FigureS7A<-subset(dataA3,Gene=="SDM.Pkbr1.Z2"|Gene=="SDM.Pkbr1.Z4"|Gene=="SDM.Pkbr2.Z2"|Gene=="SDM.Pkbr2.Z4"|Gene=="SDM.Pkbr3.Z2")
pFigS7A<-ggplot(FigureS7A, aes(x=Day.after.hatching,y=meanSDM/meanrp49,group=Sex)) +
   geom_point(aes(linetype=Sex))+
   geom_line(aes(linetype=Sex))+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+
  ylab("Pkbr copies and isoforms relative expression (/rpL32)") +xlab("days after oviposition") +
  theme_bw(15) + 
  theme(axis.title.x = element_text(colour = "#242424"))

pFigS7A+facet_grid(Gene~.,scale="free")

Figure5B

#boxplot
Pkbr1box<-subset(dataB, Gene=="G.Pkbr1")
Pkbr1box

##     Sample  Treatment Stage.treated    Gene SDM.gene   rp49.1   rp49.2
## 93  PyrT21 A.Methanol         PreD1 G.Pkbr1 7.81e-06 0.001770 0.002420
## 94  PyrT11 A.Methanol         PreD1 G.Pkbr1 1.21e-03 0.004210 0.004680
## 95  PyrT19 A.Methanol         PreD1 G.Pkbr1 9.04e-06 0.002010 0.002680
## 96  PyrT20 A.Methanol         PreD1 G.Pkbr1 4.92e-05 0.005270 0.004500
## 97  PyrT30 A.Methanol         PreD1 G.Pkbr1 4.83e-05 0.003630 0.003430
## 98  PyrT37 A.Methanol         PreD1 G.Pkbr1 1.84e-04 0.005320 0.005150
## 99  PyrT27 A.Methanol          PuD0 G.Pkbr1 1.10e-05 0.000354 0.000534
## 100 PyrT34 A.Methanol          PuD0 G.Pkbr1 1.48e-05 0.001180 0.001380
## 101 PyrT35 A.Methanol          PuD0 G.Pkbr1 5.93e-06 0.001390 0.000939
## 102 PyrT36 A.Methanol          PuD0 G.Pkbr1 2.07e-06 0.000917 0.001220
## 103 PyrT44 A.Methanol          PuD0 G.Pkbr1 8.59e-07 0.000394 0.000717
## 104 PyrT54      B.5mM         PreD1 G.Pkbr1 4.27e-06 0.000786 0.001430
## 105 PyrT55      B.5mM         PreD1 G.Pkbr1 1.17e-05 0.002350 0.003510
## 106 PyrT56      B.5mM         PreD1 G.Pkbr1 4.43e-06 0.001280 0.002580
## 107 PyrT57      B.5mM         PreD1 G.Pkbr1 3.26e-05 0.001690 0.003140
## 108 PyrT61      B.5mM         PreD1 G.Pkbr1 1.48e-05 0.002080 0.002640
## 109 PyrT62      B.5mM         PreD1 G.Pkbr1 3.95e-05 0.005070 0.003120
## 110 PyrT52      B.5mM          PuD0 G.Pkbr1 6.21e-06 0.007550 0.001940
## 111 PyrT58      B.5mM          PuD0 G.Pkbr1 4.51e-06 0.000361 0.002640
## 112 PyrT59      B.5mM          PuD0 G.Pkbr1 1.52e-06 0.001370 0.002130
## 113 PyrT60      B.5mM          PuD0 G.Pkbr1 4.28e-07 0.001480 0.002030
## 114 PyrT63      B.5mM          PuD0 G.Pkbr1 1.10e-06 0.001020 0.001820
## 115 PyrT64      B.5mM          PuD0 G.Pkbr1 4.09e-07 0.000464 0.000970
##      gene.normal
## 93  0.0032272727
## 94  0.2585470085
## 95  0.0033731343
## 96  0.0109333333
## 97  0.0140816327
## 98  0.0357281553
## 99  0.0205992509
## 100 0.0107246377
## 101 0.0063152290
## 102 0.0016967213
## 103 0.0011980474
## 104 0.0029860140
## 105 0.0033333333
## 106 0.0017170543
## 107 0.0103821656
## 108 0.0056060606
## 109 0.0126602564
## 110 0.0032010309
## 111 0.0017083333
## 112 0.0007136150
## 113 0.0002108374
## 114 0.0006043956
## 115 0.0004216495

Pkbr1box<-select(Pkbr1box, gene.normal,Stage.treated, Treatment)
br1box<-ggplot(data = Pkbr1box, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("Pkbr1 relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.05)) + theme_bw(25)

br1box

postscript("Figure5B-Pkbr1Bboxplot.ps")
br1box
dev.off()

## quartz_off_screen 
##                 2

Figure5C

#boxplot
Pkbr1Z2box<-subset(dataB, Gene=="H.Pkbr1.Z2")
Pkbr1Z2box

##     Sample  Treatment Stage.treated       Gene SDM.gene   rp49.1   rp49.2
## 162 PyrT21 A.Methanol         PreD1 H.Pkbr1.Z2 8.22e-05 0.001770 0.002420
## 163 PyrT11 A.Methanol         PreD1 H.Pkbr1.Z2 8.16e-05 0.004210 0.004680
## 164 PyrT19 A.Methanol         PreD1 H.Pkbr1.Z2 6.33e-05 0.002010 0.002680
## 165 PyrT20 A.Methanol         PreD1 H.Pkbr1.Z2 8.40e-05 0.005270 0.004500
## 166 PyrT30 A.Methanol         PreD1 H.Pkbr1.Z2 6.07e-05 0.003630 0.003430
## 167 PyrT37 A.Methanol         PreD1 H.Pkbr1.Z2 1.11e-04 0.005320 0.005150
## 168 PyrT27 A.Methanol          PuD0 H.Pkbr1.Z2 1.44e-05 0.000354 0.000534
## 169 PyrT34 A.Methanol          PuD0 H.Pkbr1.Z2 3.86e-05 0.001180 0.001380
## 170 PyrT35 A.Methanol          PuD0 H.Pkbr1.Z2 3.89e-05 0.001390 0.000939
## 171 PyrT36 A.Methanol          PuD0 H.Pkbr1.Z2 2.87e-05 0.000917 0.001220
## 172 PyrT44 A.Methanol          PuD0 H.Pkbr1.Z2 2.31e-05 0.000394 0.000717
## 173 PyrT54      B.5mM         PreD1 H.Pkbr1.Z2 4.71e-05 0.000786 0.001430
## 174 PyrT55      B.5mM         PreD1 H.Pkbr1.Z2 5.20e-05 0.002350 0.003510
## 175 PyrT56      B.5mM         PreD1 H.Pkbr1.Z2 2.89e-05 0.001280 0.002580
## 176 PyrT57      B.5mM         PreD1 H.Pkbr1.Z2 6.84e-05 0.001690 0.003140
## 177 PyrT61      B.5mM         PreD1 H.Pkbr1.Z2 2.75e-05 0.002080 0.002640
## 178 PyrT62      B.5mM         PreD1 H.Pkbr1.Z2 4.39e-05 0.005070 0.003120
## 179 PyrT52      B.5mM          PuD0 H.Pkbr1.Z2 1.11e-05 0.007550 0.001940
## 180 PyrT58      B.5mM          PuD0 H.Pkbr1.Z2 9.55e-06 0.000361 0.002640
## 181 PyrT59      B.5mM          PuD0 H.Pkbr1.Z2 9.29e-06 0.001370 0.002130
## 182 PyrT60      B.5mM          PuD0 H.Pkbr1.Z2 5.63e-06 0.001480 0.002030
## 183 PyrT63      B.5mM          PuD0 H.Pkbr1.Z2 1.74e-05 0.001020 0.001820
## 184 PyrT64      B.5mM          PuD0 H.Pkbr1.Z2 9.76e-06 0.000464 0.000970
##     gene.normal
## 162 0.033966942
## 163 0.017435897
## 164 0.023619403
## 165 0.018666667
## 166 0.017696793
## 167 0.021553398
## 168 0.026966292
## 169 0.027971014
## 170 0.041427050
## 171 0.023524590
## 172 0.032217573
## 173 0.032937063
## 174 0.014814815
## 175 0.011201550
## 176 0.021783439
## 177 0.010416667
## 178 0.014070513
## 179 0.005721649
## 180 0.003617424
## 181 0.004361502
## 182 0.002773399
## 183 0.009560440
## 184 0.010061856

Pkbr1Z2box<-select(Pkbr1Z2box, gene.normal,Stage.treated, Treatment)
br1Z2box<-ggplot(data = Pkbr1Z2box, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("Pkbr1 Z2 relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.01)) + theme_bw(25)

br1Z2box

postscript("Figure5C-Pkbr1Z2boxplot.ps")
br1Z2box
dev.off()

## quartz_off_screen 
##                 2

Figure5D

#boxplot
Pkbr1Z4box<-subset(dataB, Gene=="I.Pkbr1.Z4")
Pkbr1Z4box

##     Sample  Treatment Stage.treated       Gene SDM.gene   rp49.1   rp49.2
## 185 PyrT21 A.Methanol         PreD1 I.Pkbr1.Z4 7.82e-05 0.001770 0.002420
## 186 PyrT11 A.Methanol         PreD1 I.Pkbr1.Z4 7.51e-05 0.004210 0.004680
## 187 PyrT19 A.Methanol         PreD1 I.Pkbr1.Z4 6.70e-05 0.002010 0.002680
## 188 PyrT20 A.Methanol         PreD1 I.Pkbr1.Z4 7.87e-05 0.005270 0.004500
## 189 PyrT30 A.Methanol         PreD1 I.Pkbr1.Z4 6.26e-05 0.003630 0.003430
## 190 PyrT37 A.Methanol         PreD1 I.Pkbr1.Z4 1.14e-04 0.005320 0.005150
## 191 PyrT27 A.Methanol          PuD0 I.Pkbr1.Z4 2.06e-05 0.000354 0.000534
## 192 PyrT34 A.Methanol          PuD0 I.Pkbr1.Z4 4.29e-05 0.001180 0.001380
## 193 PyrT35 A.Methanol          PuD0 I.Pkbr1.Z4 4.24e-05 0.001390 0.000939
## 194 PyrT36 A.Methanol          PuD0 I.Pkbr1.Z4 3.33e-05 0.000917 0.001220
## 195 PyrT44 A.Methanol          PuD0 I.Pkbr1.Z4 1.90e-05 0.000394 0.000717
## 196 PyrT54      B.5mM         PreD1 I.Pkbr1.Z4 3.63e-05 0.000786 0.001430
## 197 PyrT55      B.5mM         PreD1 I.Pkbr1.Z4 6.05e-05 0.002350 0.003510
## 198 PyrT56      B.5mM         PreD1 I.Pkbr1.Z4 3.63e-05 0.001280 0.002580
## 199 PyrT57      B.5mM         PreD1 I.Pkbr1.Z4 7.41e-05 0.001690 0.003140
## 200 PyrT61      B.5mM         PreD1 I.Pkbr1.Z4 3.17e-05 0.002080 0.002640
## 201 PyrT62      B.5mM         PreD1 I.Pkbr1.Z4 4.69e-05 0.005070 0.003120
## 202 PyrT52      B.5mM          PuD0 I.Pkbr1.Z4 2.15e-05 0.007550 0.001940
## 203 PyrT58      B.5mM          PuD0 I.Pkbr1.Z4 1.84e-05 0.000361 0.002640
## 204 PyrT59      B.5mM          PuD0 I.Pkbr1.Z4 1.32e-05 0.001370 0.002130
## 205 PyrT60      B.5mM          PuD0 I.Pkbr1.Z4 8.71e-06 0.001480 0.002030
## 206 PyrT63      B.5mM          PuD0 I.Pkbr1.Z4 2.24e-05 0.001020 0.001820
## 207 PyrT64      B.5mM          PuD0 I.Pkbr1.Z4 1.48e-05 0.000464 0.000970
##     gene.normal
## 185 0.032314050
## 186 0.016047009
## 187 0.025000000
## 188 0.017488889
## 189 0.018250729
## 190 0.022135922
## 191 0.038576779
## 192 0.031086957
## 193 0.045154420
## 194 0.027295082
## 195 0.026499303
## 196 0.025384615
## 197 0.017236467
## 198 0.014069767
## 199 0.023598726
## 200 0.012007576
## 201 0.015032051
## 202 0.011082474
## 203 0.006969697
## 204 0.006197183
## 205 0.004290640
## 206 0.012307692
## 207 0.015257732

Pkbr1Z4box<-select(Pkbr1Z4box, gene.normal,Stage.treated, Treatment)
br1Z4box<-ggplot(data = Pkbr1Z4box, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("Pkbr1 Z2 relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.01)) + theme_bw(25)
br1Z4box

postscript("Figure5D-Pkbr1Z4boxplot.ps")
br1Z4box
dev.off()

## quartz_off_screen 
##                 2

Figure5E

#boxplot
Pkbr3box<-subset(dataB, Gene=="M.Pkbr3")
Pkbr3box

##     Sample  Treatment Stage.treated    Gene SDM.gene   rp49.1   rp49.2
## 139 PyrT21 A.Methanol         PreD1 M.Pkbr3 3.08e-04 0.001770 0.002420
## 140 PyrT11 A.Methanol         PreD1 M.Pkbr3 3.36e-04 0.004210 0.004680
## 141 PyrT19 A.Methanol         PreD1 M.Pkbr3 3.52e-04 0.002010 0.002680
## 142 PyrT20 A.Methanol         PreD1 M.Pkbr3 5.97e-04 0.005270 0.004500
## 143 PyrT30 A.Methanol         PreD1 M.Pkbr3 5.73e-04 0.003630 0.003430
## 144 PyrT37 A.Methanol         PreD1 M.Pkbr3 6.05e-04 0.005320 0.005150
## 145 PyrT27 A.Methanol          PuD0 M.Pkbr3 1.41e-04 0.000354 0.000534
## 146 PyrT34 A.Methanol          PuD0 M.Pkbr3 2.43e-04 0.001180 0.001380
## 147 PyrT35 A.Methanol          PuD0 M.Pkbr3 1.63e-04 0.001390 0.000939
## 148 PyrT36 A.Methanol          PuD0 M.Pkbr3 1.39e-04 0.000917 0.001220
## 149 PyrT44 A.Methanol          PuD0 M.Pkbr3 6.93e-05 0.000394 0.000717
## 150 PyrT54      B.5mM         PreD1 M.Pkbr3 3.89e-04 0.000786 0.001430
## 151 PyrT55      B.5mM         PreD1 M.Pkbr3 6.09e-04 0.002350 0.003510
## 152 PyrT56      B.5mM         PreD1 M.Pkbr3 5.26e-04 0.001280 0.002580
## 153 PyrT57      B.5mM         PreD1 M.Pkbr3 6.86e-04 0.001690 0.003140
## 154 PyrT61      B.5mM         PreD1 M.Pkbr3 5.93e-04 0.002080 0.002640
## 155 PyrT62      B.5mM         PreD1 M.Pkbr3 8.11e-04 0.005070 0.003120
## 156 PyrT52      B.5mM          PuD0 M.Pkbr3 3.45e-04 0.007550 0.001940
## 157 PyrT58      B.5mM          PuD0 M.Pkbr3 3.95e-04 0.000361 0.002640
## 158 PyrT59      B.5mM          PuD0 M.Pkbr3 3.11e-04 0.001370 0.002130
## 159 PyrT60      B.5mM          PuD0 M.Pkbr3 2.83e-04 0.001480 0.002030
## 160 PyrT63      B.5mM          PuD0 M.Pkbr3 3.55e-04 0.001020 0.001820
## 161 PyrT64      B.5mM          PuD0 M.Pkbr3 1.86e-04 0.000464 0.000970
##     gene.normal
## 139  0.12727273
## 140  0.07179487
## 141  0.13134328
## 142  0.13266667
## 143  0.16705539
## 144  0.11747573
## 145  0.26404494
## 146  0.17608696
## 147  0.17358892
## 148  0.11393443
## 149  0.09665272
## 150  0.27202797
## 151  0.17350427
## 152  0.20387597
## 153  0.21847134
## 154  0.22462121
## 155  0.25993590
## 156  0.17783505
## 157  0.14962121
## 158  0.14600939
## 159  0.13940887
## 160  0.19505495
## 161  0.19175258

Pkbr3box<-select(Pkbr3box, gene.normal,Stage.treated, Treatment)
br3box<-ggplot(data = Pkbr3box, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("Pkbr1 Z2 relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.05)) + theme_bw(25)
br3box

postscript("Figure5E-Pkbr3boxplot.ps")
br3box
dev.off()

## quartz_off_screen 
##                 2

Figure5F

#boxplot
Pkbr3Z2box<-subset(dataB, Gene=="N.Pkbr3.Z2")
Pkbr3Z2box

##     Sample  Treatment Stage.treated       Gene SDM.gene   rp49.1   rp49.2
## 254 PyrT21 A.Methanol         PreD1 N.Pkbr3.Z2 1.66e-04 0.001770 0.002420
## 255 PyrT11 A.Methanol         PreD1 N.Pkbr3.Z2 1.66e-04 0.004210 0.004680
## 256 PyrT19 A.Methanol         PreD1 N.Pkbr3.Z2 1.90e-04 0.002010 0.002680
## 257 PyrT20 A.Methanol         PreD1 N.Pkbr3.Z2 2.77e-04 0.005270 0.004500
## 258 PyrT30 A.Methanol         PreD1 N.Pkbr3.Z2 2.81e-04 0.003630 0.003430
## 259 PyrT37 A.Methanol         PreD1 N.Pkbr3.Z2 3.83e-04 0.005320 0.005150
## 260 PyrT27 A.Methanol          PuD0 N.Pkbr3.Z2 6.10e-05 0.000354 0.000534
## 261 PyrT34 A.Methanol          PuD0 N.Pkbr3.Z2 1.09e-04 0.001180 0.001380
## 262 PyrT35 A.Methanol          PuD0 N.Pkbr3.Z2 9.62e-05 0.001390 0.000939
## 263 PyrT36 A.Methanol          PuD0 N.Pkbr3.Z2 7.10e-05 0.000917 0.001220
## 264 PyrT44 A.Methanol          PuD0 N.Pkbr3.Z2 3.41e-05 0.000394 0.000717
## 265 PyrT54      B.5mM         PreD1 N.Pkbr3.Z2 1.94e-04 0.000786 0.001430
## 266 PyrT55      B.5mM         PreD1 N.Pkbr3.Z2 3.02e-04 0.002350 0.003510
## 267 PyrT56      B.5mM         PreD1 N.Pkbr3.Z2 2.33e-04 0.001280 0.002580
## 268 PyrT57      B.5mM         PreD1 N.Pkbr3.Z2 3.59e-04 0.001690 0.003140
## 269 PyrT61      B.5mM         PreD1 N.Pkbr3.Z2 3.34e-04 0.002080 0.002640
## 270 PyrT62      B.5mM         PreD1 N.Pkbr3.Z2 4.55e-04 0.005070 0.003120
## 271 PyrT52      B.5mM          PuD0 N.Pkbr3.Z2 1.93e-04 0.007550 0.001940
## 272 PyrT58      B.5mM          PuD0 N.Pkbr3.Z2 1.89e-04 0.000361 0.002640
## 273 PyrT59      B.5mM          PuD0 N.Pkbr3.Z2 1.67e-04 0.001370 0.002130
## 274 PyrT60      B.5mM          PuD0 N.Pkbr3.Z2 1.42e-04 0.001480 0.002030
## 275 PyrT63      B.5mM          PuD0 N.Pkbr3.Z2 2.16e-04 0.001020 0.001820
## 276 PyrT64      B.5mM          PuD0 N.Pkbr3.Z2 1.00e-04 0.000464 0.000970
##     gene.normal
## 254  0.06859504
## 255  0.03547009
## 256  0.07089552
## 257  0.06155556
## 258  0.08192420
## 259  0.07436893
## 260  0.11423221
## 261  0.07898551
## 262  0.10244941
## 263  0.05819672
## 264  0.04755927
## 265  0.13566434
## 266  0.08603989
## 267  0.09031008
## 268  0.11433121
## 269  0.12651515
## 270  0.14583333
## 271  0.09948454
## 272  0.07159091
## 273  0.07840376
## 274  0.06995074
## 275  0.11868132
## 276  0.10309278

Pkbr3Z2box<-select(Pkbr3Z2box, gene.normal,Stage.treated, Treatment)
br3Z2box<-ggplot(data = Pkbr3Z2box, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("Pkbr1 Z2 relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.05)) + theme_bw(25)
br3Z2box

postscript("Figure5F-Pkbr3Z2boxplot.ps")
br3Z2box
dev.off()

## quartz_off_screen 
##                 2

Figure Pkbr2 and Pkbr2 Z2, Z4

#boxplot
Pkbr2box<-subset(dataB, Gene=="J.Pkbr2")
Pkbr2box

##     Sample  Treatment Stage.treated    Gene SDM.gene   rp49.1   rp49.2
## 116 PyrT21 A.Methanol         PreD1 J.Pkbr2 2.25e-05 0.001770 0.002420
## 117 PyrT11 A.Methanol         PreD1 J.Pkbr2 2.51e-05 0.004210 0.004680
## 118 PyrT19 A.Methanol         PreD1 J.Pkbr2 9.13e-06 0.002010 0.002680
## 119 PyrT20 A.Methanol         PreD1 J.Pkbr2 3.35e-05 0.005270 0.004500
## 120 PyrT30 A.Methanol         PreD1 J.Pkbr2 2.64e-05 0.003630 0.003430
## 121 PyrT37 A.Methanol         PreD1 J.Pkbr2 4.41e-05 0.005320 0.005150
## 122 PyrT27 A.Methanol          PuD0 J.Pkbr2 1.19e-05 0.000354 0.000534
## 123 PyrT34 A.Methanol          PuD0 J.Pkbr2 1.80e-05 0.001180 0.001380
## 124 PyrT35 A.Methanol          PuD0 J.Pkbr2 2.13e-05 0.001390 0.000939
## 125 PyrT36 A.Methanol          PuD0 J.Pkbr2 1.44e-05 0.000917 0.001220
## 126 PyrT44 A.Methanol          PuD0 J.Pkbr2 7.42e-06 0.000394 0.000717
## 127 PyrT54      B.5mM         PreD1 J.Pkbr2 1.11e-05 0.000786 0.001430
## 128 PyrT55      B.5mM         PreD1 J.Pkbr2 1.44e-05 0.002350 0.003510
## 129 PyrT56      B.5mM         PreD1 J.Pkbr2 1.76e-05 0.001280 0.002580
## 130 PyrT57      B.5mM         PreD1 J.Pkbr2 2.27e-05 0.001690 0.003140
## 131 PyrT61      B.5mM         PreD1 J.Pkbr2 2.01e-05 0.002080 0.002640
## 132 PyrT62      B.5mM         PreD1 J.Pkbr2 3.01e-05 0.005070 0.003120
## 133 PyrT52      B.5mM          PuD0 J.Pkbr2 1.71e-05 0.007550 0.001940
## 134 PyrT58      B.5mM          PuD0 J.Pkbr2 2.14e-05 0.000361 0.002640
## 135 PyrT59      B.5mM          PuD0 J.Pkbr2 1.94e-05 0.001370 0.002130
## 136 PyrT60      B.5mM          PuD0 J.Pkbr2 1.62e-05 0.001480 0.002030
## 137 PyrT63      B.5mM          PuD0 J.Pkbr2 1.99e-05 0.001020 0.001820
## 138 PyrT64      B.5mM          PuD0 J.Pkbr2 1.37e-05 0.000464 0.000970
##     gene.normal
## 116 0.009297521
## 117 0.005363248
## 118 0.003406716
## 119 0.007444444
## 120 0.007696793
## 121 0.008563107
## 122 0.022284644
## 123 0.013043478
## 124 0.022683706
## 125 0.011803279
## 126 0.010348675
## 127 0.007762238
## 128 0.004102564
## 129 0.006821705
## 130 0.007229299
## 131 0.007613636
## 132 0.009647436
## 133 0.008814433
## 134 0.008106061
## 135 0.009107981
## 136 0.007980296
## 137 0.010934066
## 138 0.014123711

Pkbr2box<-select(Pkbr2box, gene.normal,Stage.treated, Treatment)
br2box<-ggplot(data = Pkbr2box, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("Pkbr2 relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.005)) + theme_bw(25)
br2box

postscript("FigureS-Pkbr2boxplot.ps")
br2box
dev.off()

## quartz_off_screen 
##                 2

#boxplot
Pkbr2Z2box<-subset(dataB, Gene=="K.Pkbr2.Z2")
Pkbr2Z2box

##     Sample  Treatment Stage.treated       Gene SDM.gene   rp49.1   rp49.2
## 208 PyrT21 A.Methanol         PreD1 K.Pkbr2.Z2 4.69e-05 0.001770 0.002420
## 209 PyrT11 A.Methanol         PreD1 K.Pkbr2.Z2 3.40e-05 0.004210 0.004680
## 210 PyrT19 A.Methanol         PreD1 K.Pkbr2.Z2 1.49e-05 0.002010 0.002680
## 211 PyrT20 A.Methanol         PreD1 K.Pkbr2.Z2 4.38e-05 0.005270 0.004500
## 212 PyrT30 A.Methanol         PreD1 K.Pkbr2.Z2 3.48e-05 0.003630 0.003430
## 213 PyrT37 A.Methanol         PreD1 K.Pkbr2.Z2 4.94e-05 0.005320 0.005150
## 214 PyrT27 A.Methanol          PuD0 K.Pkbr2.Z2 2.09e-05 0.000354 0.000534
## 215 PyrT34 A.Methanol          PuD0 K.Pkbr2.Z2 2.12e-05 0.001180 0.001380
## 216 PyrT35 A.Methanol          PuD0 K.Pkbr2.Z2 2.48e-05 0.001390 0.000939
## 217 PyrT36 A.Methanol          PuD0 K.Pkbr2.Z2 2.09e-05 0.000917 0.001220
## 218 PyrT44 A.Methanol          PuD0 K.Pkbr2.Z2 1.13e-05 0.000394 0.000717
## 219 PyrT54      B.5mM         PreD1 K.Pkbr2.Z2 1.80e-05 0.000786 0.001430
## 220 PyrT55      B.5mM         PreD1 K.Pkbr2.Z2 1.73e-05 0.002350 0.003510
## 221 PyrT56      B.5mM         PreD1 K.Pkbr2.Z2 1.75e-05 0.001280 0.002580
## 222 PyrT57      B.5mM         PreD1 K.Pkbr2.Z2 2.88e-05 0.001690 0.003140
## 223 PyrT61      B.5mM         PreD1 K.Pkbr2.Z2 2.60e-05 0.002080 0.002640
## 224 PyrT62      B.5mM         PreD1 K.Pkbr2.Z2 3.61e-05 0.005070 0.003120
## 225 PyrT52      B.5mM          PuD0 K.Pkbr2.Z2 2.04e-05 0.007550 0.001940
## 226 PyrT58      B.5mM          PuD0 K.Pkbr2.Z2 3.13e-05 0.000361 0.002640
## 227 PyrT59      B.5mM          PuD0 K.Pkbr2.Z2 2.27e-05 0.001370 0.002130
## 228 PyrT60      B.5mM          PuD0 K.Pkbr2.Z2 1.87e-05 0.001480 0.002030
## 229 PyrT63      B.5mM          PuD0 K.Pkbr2.Z2 2.32e-05 0.001020 0.001820
## 230 PyrT64      B.5mM          PuD0 K.Pkbr2.Z2 1.44e-05 0.000464 0.000970
##     gene.normal
## 208 0.019380165
## 209 0.007264957
## 210 0.005559701
## 211 0.009733333
## 212 0.010145773
## 213 0.009592233
## 214 0.039138577
## 215 0.015362319
## 216 0.026411076
## 217 0.017131148
## 218 0.015760112
## 219 0.012587413
## 220 0.004928775
## 221 0.006782946
## 222 0.009171975
## 223 0.009848485
## 224 0.011570513
## 225 0.010515464
## 226 0.011856061
## 227 0.010657277
## 228 0.009211823
## 229 0.012747253
## 230 0.014845361

Pkbr2Z2box<-select(Pkbr2Z2box, gene.normal,Stage.treated, Treatment)
br2Z2box<-ggplot(data = Pkbr2Z2box, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("Pkbr2 Z2 relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.005)) + theme_bw(25)
br2Z2box

postscript("FigureS-Pkbr2Z2boxplot.ps")
br2Z2box
dev.off()

## quartz_off_screen 
##                 2

#boxplot
Pkbr2Z4box<-subset(dataB, Gene=="L.Pkbr2.Z4")
Pkbr2Z4box

##     Sample  Treatment Stage.treated       Gene SDM.gene   rp49.1   rp49.2
## 231 PyrT21 A.Methanol         PreD1 L.Pkbr2.Z4 3.59e-05 0.001770 0.002420
## 232 PyrT11 A.Methanol         PreD1 L.Pkbr2.Z4 3.09e-05 0.004210 0.004680
## 233 PyrT19 A.Methanol         PreD1 L.Pkbr2.Z4 1.73e-05 0.002010 0.002680
## 234 PyrT20 A.Methanol         PreD1 L.Pkbr2.Z4 5.00e-05 0.005270 0.004500
## 235 PyrT30 A.Methanol         PreD1 L.Pkbr2.Z4 4.83e-05 0.003630 0.003430
## 236 PyrT37 A.Methanol         PreD1 L.Pkbr2.Z4 5.40e-05 0.005320 0.005150
## 237 PyrT27 A.Methanol          PuD0 L.Pkbr2.Z4 2.61e-05 0.000354 0.000534
## 238 PyrT34 A.Methanol          PuD0 L.Pkbr2.Z4 3.05e-05 0.001180 0.001380
## 239 PyrT35 A.Methanol          PuD0 L.Pkbr2.Z4 3.32e-05 0.001390 0.000939
## 240 PyrT36 A.Methanol          PuD0 L.Pkbr2.Z4 3.03e-05 0.000917 0.001220
## 241 PyrT44 A.Methanol          PuD0 L.Pkbr2.Z4 1.58e-05 0.000394 0.000717
## 242 PyrT54      B.5mM         PreD1 L.Pkbr2.Z4 1.67e-05 0.000786 0.001430
## 243 PyrT55      B.5mM         PreD1 L.Pkbr2.Z4 2.28e-05 0.002350 0.003510
## 244 PyrT56      B.5mM         PreD1 L.Pkbr2.Z4 2.11e-05 0.001280 0.002580
## 245 PyrT57      B.5mM         PreD1 L.Pkbr2.Z4 3.79e-05 0.001690 0.003140
## 246 PyrT61      B.5mM         PreD1 L.Pkbr2.Z4 2.88e-05 0.002080 0.002640
## 247 PyrT62      B.5mM         PreD1 L.Pkbr2.Z4 5.37e-05 0.005070 0.003120
## 248 PyrT52      B.5mM          PuD0 L.Pkbr2.Z4 2.96e-05 0.007550 0.001940
## 249 PyrT58      B.5mM          PuD0 L.Pkbr2.Z4 3.85e-05 0.000361 0.002640
## 250 PyrT59      B.5mM          PuD0 L.Pkbr2.Z4 3.32e-05 0.001370 0.002130
## 251 PyrT60      B.5mM          PuD0 L.Pkbr2.Z4 2.57e-05 0.001480 0.002030
## 252 PyrT63      B.5mM          PuD0 L.Pkbr2.Z4 3.22e-05 0.001020 0.001820
## 253 PyrT64      B.5mM          PuD0 L.Pkbr2.Z4 1.88e-05 0.000464 0.000970
##     gene.normal
## 231 0.014834711
## 232 0.006602564
## 233 0.006455224
## 234 0.011111111
## 235 0.014081633
## 236 0.010485437
## 237 0.048876404
## 238 0.022101449
## 239 0.035356763
## 240 0.024836066
## 241 0.022036262
## 242 0.011678322
## 243 0.006495726
## 244 0.008178295
## 245 0.012070064
## 246 0.010909091
## 247 0.017211538
## 248 0.015257732
## 249 0.014583333
## 250 0.015586854
## 251 0.012660099
## 252 0.017692308
## 253 0.019381443

Pkbr2Z4box<-select(Pkbr2Z4box, gene.normal,Stage.treated, Treatment)
br2Z4box<-ggplot(data = Pkbr2Z4box, aes(x = Stage.treated, y = gene.normal)) + 
  geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) + 
  ylab("Pkbr2 Z2 relative amount/rpL32") +
  scale_fill_manual(name="", # Legend label, use darker colors
                  breaks=c("A.Methanol", "B.5mM"),
                  labels=c("methanol","5mM pyriproxyfen"),
                  values=c("white", "grey")) +
  scale_y_continuous(breaks=seq(0,2,by = 0.005)) + theme_bw(25)
br2Z4box

postscript("FigureS-Pkbr2Z4boxplot.ps")
br2Z4box
dev.off()

## quartz_off_screen 
##                 2

Statistical tests

#subsetting original data for statistical tests prepupae


prepupatestPkbr1<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="G.Pkbr1")
t.test(gene.normal~Treatment,data=prepupatestPkbr1)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 1.1707, df = 5.019, p-value = 0.2943
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05751883  0.15392071
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.054315089              0.006114147

pupatestPkbr1<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="G.Pkbr1")
t.test(gene.normal~Treatment,data=pupatestPkbr1)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 1.9342, df = 4.134, p-value = 0.1229
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.002905264  0.016832198
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.008106777              0.001143310

prepupatestPkbr1z2<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="H.Pkbr1.Z2")
t.test(gene.normal~Treatment,data=prepupatestPkbr1z2)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 1.0674, df = 9.169, p-value = 0.3131
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.005142703  0.014381054
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##               0.02215652               0.01753734

pupatestPkbr1z2<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="H.Pkbr1.Z2")
t.test(gene.normal~Treatment,data=pupatestPkbr1z2)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 7.3273, df = 5.341, p-value = 0.0005573
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.01600531 0.03280521
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.030421304              0.006016045

prepupatestPkbr1z4<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="I.Pkbr1.Z4")
t.test(gene.normal~Treatment,data=prepupatestPkbr1z4)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 1.1983, df = 9.866, p-value = 0.2588
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.003437932  0.011407063
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##               0.02187277               0.01788820

pupatestPkbr1z4<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="I.Pkbr1.Z4")
t.test(gene.normal~Treatment,data=pupatestPkbr1z4)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 6.1582, df = 5.806, p-value = 0.0009519
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.01460854 0.03413467
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.033722508              0.009350903

prepupatestPkbr2<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="J.Pkbr2")
t.test(gene.normal~Treatment,data=prepupatestPkbr2)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -0.2023, df = 9.64, p-value = 0.8439
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.002826624  0.002358274
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.006961972              0.007196146

pupatestPkbr2<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="J.Pkbr2")
t.test(gene.normal~Treatment,data=pupatestPkbr2)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 2.1821, df = 5.033, p-value = 0.08054
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.001087456  0.013464120
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.016032756              0.009844425

prepupatestPkbr2z2<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="K.Pkbr2.Z2")
t.test(gene.normal~Treatment,data=prepupatestPkbr2z2)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 0.4951, df = 8.198, p-value = 0.6335
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.004114794  0.006376813
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##              0.010279360              0.009148351

pupatestPkbr2z2<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="K.Pkbr2.Z2")
t.test(gene.normal~Treatment,data=pupatestPkbr2z2)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 2.3983, df = 4.252, p-value = 0.07069
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.001458084  0.023701631
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##               0.02276065               0.01163887

prepupatestPkbr2z4<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="L.Pkbr2.Z4")
t.test(gene.normal~Treatment,data=prepupatestPkbr2z4)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -0.2363, df = 9.986, p-value = 0.818
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.005168025  0.004177240
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##               0.01059511               0.01109051

pupatestPkbr2z4<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="L.Pkbr2.Z4")
t.test(gene.normal~Treatment,data=pupatestPkbr2z4)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = 2.8081, df = 4.28, p-value = 0.04482
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.0005363375 0.0290258504
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##               0.03064139               0.01586029

prepupatestPkbr3<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="M.Pkbr3")
t.test(gene.normal~Treatment,data=prepupatestPkbr3)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -5.1868, df = 9.75, p-value = 0.0004437
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.14425859 -0.05735074
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##                0.1246014                0.2254061

pupatestPkbr3<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="M.Pkbr3")
t.test(gene.normal~Treatment,data=pupatestPkbr3)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -0.0564, df = 4.933, p-value = 0.9572
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.08193307  0.07842891
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##                0.1648616                0.1666137

prepupatestPkbr3z2<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="N.Pkbr3.Z2")
t.test(gene.normal~Treatment,data=prepupatestPkbr3z2)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -4.2828, df = 8.702, p-value = 0.002206
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.07805003 -0.02391152
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##               0.06546822               0.11644900

pupatestPkbr3z2<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="N.Pkbr3.Z2")
t.test(gene.normal~Treatment,data=pupatestPkbr3z2)

## 
##  Welch Two Sample t-test
## 
## data:  gene.normal by Treatment
## t = -0.6603, df = 6.996, p-value = 0.5302
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.04543047  0.02559837
## sample estimates:
## mean in group A.Methanol      mean in group B.5mM 
##               0.08028463               0.09020067

Supplementary material

Figure Taiman 5A and B and INDEL

FigureSTai<-subset(dataA3,Gene=="SDM.Tai.IN"|Gene=="SDM.Tai.DEL")

pFigSTai<-ggplot(FigureSTai, aes(x=Day.after.hatching,y=meanSDM/meanrp49,group=Sex)) +
  geom_point(aes(linetype=Sex),size=2)+
  geom_line(aes(linetype=Sex),size=0.5)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+
  ylab("Gene relative expression (/rpL32)") +xlab("days after oviposition") +
  theme_bw(15) + 
  theme(axis.title.x = element_text(colour = "#242424"))

pFigSTai+facet_grid(Gene~.,scales="free")

FigureSTai5<-subset(dataA3,Gene=="SDM.Tai5A"|Gene=="SDM.Tai5B")

pFigSTai5<-ggplot(FigureSTai5, aes(x=Day.after.hatching,y=meanSDM/meanrp49,group=Sex)) +
  geom_point(aes(linetype=Sex),size=2)+
  geom_line(aes(linetype=Sex),size=0.5)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+
  ylab("Gene relative expression (/rpL32)") +xlab("days after oviposition") +
  theme_bw(15) + 
  theme(axis.title.x = element_text(colour = "#242424"))

pFigSTai5+facet_grid(Gene~.,scales="free")

R analyses for Vea et al. Differential juvenile hormone modulation establishes extreme sexual dimorphism, for submission to PloS ONE in scale insects

Isabelle Vea

September 14, 2015

Introduction

Data

Loading datasets

Expression profiles

Figure 2: Expression profiles of PkJHAMT, PkMet, PkTai, PkKr-h1-common

Figure 3: Effect on Met and Kr-h1

Figure 3C: PkMet

Statistical test Met

Figure 3-D to F: Tai core, Tai-IN and Tai-DEL

Statistical tests

Figure 3G to I: PkKr-h1 core, PkKr-h1 A and PkKr-h1 B

Statistical tests

Figure 5: Broad

Figure 5A: Expression profile of Pkbr1 and Pkbr2

Figure S7A: Expression profile of Pkbr1Z2, Z4, Pkbr2, Z2 and Z4 and Pkbr3Z2

Figure5B

Figure5C

Figure5D

Figure5E

Figure5F

Figure Pkbr2 and Pkbr2 Z2, Z4

Statistical tests

Supplementary material

Figure Taiman 5A and B and INDEL