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
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 3.1.3Loading datasets
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 oviposition and sex
dataA3<-ddply(dataA2,~Day.after.oviposition +Sex+Gene,summarise,meanrp49=mean(SDM.rp49.2),meanSDM=mean(SDM))
#normalize dataB with housekeeping gene
dataB$gene.normal<-dataB$SDM.gene/dataB$rp49.2
#making mean and error values
dataBs<-summarySE(dataB, measurevar="gene.normal", groupvars=c("Treatment","Stage.treated","Gene"))Expression profiles
Figure 2: Expression profiles of PkJHAMT, PkMet, PkTai, PkKr-h1-common
library(dplyr)##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following object is masked from 'package:stats':
##
## filter
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionFigure2<-subset(dataA3,Gene=="SDM.JHAMT"|Gene=="SDM.Met"|Gene=="SDM.Tai"|Gene=="SDM.Pkkr.h1_26")
pFig1<-ggplot(Figure2, aes(x=Day.after.oviposition,y=meanSDM/meanrp49,group=Sex)) +
geom_point(aes(linetype=Sex),size=2)+
geom_line(aes(linetype=Sex),size=0.5)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+
ylab("Gene relative expression (/rpL32)") +xlab("days after oviposition") +
theme_bw(15) +
theme(axis.title.x = element_text(colour = "#242424"))
pFig1+facet_grid(Gene~.,scales="free")
Figure 3: Effect on Met and Kr-h1
Figure 3C: PkMet
#boxplot
PkMetbox<-subset(dataB, Gene=="B.Met")
PkMetbox<-select(PkMetbox, gene.normal,Stage.treated, Treatment)
Metbox<-ggplot(data = PkMetbox, aes(x = Stage.treated, y = gene.normal)) +
geom_boxplot(aes(fill = Treatment), width = 0.8, size=1) +
ylab("PkMet relative amount/rpL32") +
scale_fill_manual(name="", # Legend label, use darker colors
breaks=c("A.Methanol", "B.5mM"),
labels=c("methanol","5mM pyriproxyfen"),
values=c("white", "grey"))+
scale_y_continuous(breaks=seq(0,2,by = 0.1)) + theme_bw(25)
Metbox
postscript("Figure3C-PkMetboxplot.ps")
Metbox
dev.off()## quartz_off_screen
## 2Statistical test Met
prepupatestMet<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="B.Met")
t.test(gene.normal~Treatment,data=prepupatestMet)##
## Welch Two Sample t-test
##
## data: gene.normal by Treatment
## t = 0.2361, df = 9.06, p-value = 0.8186
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.04229839 0.05216887
## sample estimates:
## mean in group A.Methanol mean in group B.5mM
## 0.1313775 0.1264422pupatestMet<-subset(dataB,dataB$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 0.2822968Figure 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 rp49.2 gene.normal
## 323 PyrT21 A.Methanol PreD1 O.Tai 0.000438 0.002420 0.1809917
## 324 PyrT11 A.Methanol PreD1 O.Tai 0.000491 0.004680 0.1049145
## 325 PyrT19 A.Methanol PreD1 O.Tai 0.000648 0.002680 0.2417910
## 326 PyrT20 A.Methanol PreD1 O.Tai 0.000817 0.004500 0.1815556
## 327 PyrT30 A.Methanol PreD1 O.Tai 0.000700 0.003430 0.2040816
## 328 PyrT37 A.Methanol PreD1 O.Tai 0.000830 0.005150 0.1611650
## 329 PyrT27 A.Methanol PuD0 O.Tai 0.001010 0.000534 1.8913858
## 330 PyrT34 A.Methanol PuD0 O.Tai 0.000324 0.001380 0.2347826
## 331 PyrT35 A.Methanol PuD0 O.Tai 0.001050 0.000939 1.1182109
## 332 PyrT36 A.Methanol PuD0 O.Tai 0.001040 0.001220 0.8524590
## 333 PyrT44 A.Methanol PuD0 O.Tai 0.000244 0.000717 0.3403068
## 334 PyrT54 B.5mM PreD1 O.Tai 0.000324 0.001430 0.2265734
## 335 PyrT55 B.5mM PreD1 O.Tai 0.000629 0.003510 0.1792023
## 336 PyrT56 B.5mM PreD1 O.Tai 0.000431 0.002580 0.1670543
## 337 PyrT57 B.5mM PreD1 O.Tai 0.000805 0.003140 0.2563694
## 338 PyrT61 B.5mM PreD1 O.Tai 0.000408 0.002640 0.1545455
## 339 PyrT62 B.5mM PreD1 O.Tai 0.001080 0.003120 0.3461538
## 340 PyrT52 B.5mM PuD0 O.Tai 0.000491 0.001940 0.2530928
## 341 PyrT58 B.5mM PuD0 O.Tai 0.000487 0.002640 0.1844697
## 342 PyrT59 B.5mM PuD0 O.Tai 0.000361 0.002130 0.1694836
## 343 PyrT60 B.5mM PuD0 O.Tai 0.000293 0.002030 0.1443350
## 344 PyrT63 B.5mM PuD0 O.Tai 0.000518 0.001820 0.2846154
## 345 PyrT64 B.5mM PuD0 O.Tai 0.000204 0.000970 0.2103093PkTaibox<-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
## 2PkTaiINbox<-subset(dataB, Gene=="P.Tai.IN")
PkTaiINbox## Sample Treatment Stage.treated Gene SDM.gene rp49.2 gene.normal
## 277 PyrT21 A.Methanol PreD1 P.Tai.IN 0.000302 0.002420 0.12479339
## 278 PyrT11 A.Methanol PreD1 P.Tai.IN 0.000335 0.004680 0.07158120
## 279 PyrT19 A.Methanol PreD1 P.Tai.IN 0.000328 0.002680 0.12238806
## 280 PyrT20 A.Methanol PreD1 P.Tai.IN 0.000450 0.004500 0.10000000
## 281 PyrT30 A.Methanol PreD1 P.Tai.IN 0.000471 0.003430 0.13731778
## 282 PyrT37 A.Methanol PreD1 P.Tai.IN 0.000591 0.005150 0.11475728
## 283 PyrT27 A.Methanol PuD0 P.Tai.IN 0.000736 0.000534 1.37827715
## 284 PyrT34 A.Methanol PuD0 P.Tai.IN 0.000720 0.001380 0.52173913
## 285 PyrT35 A.Methanol PuD0 P.Tai.IN 0.001160 0.000939 1.23535676
## 286 PyrT36 A.Methanol PuD0 P.Tai.IN 0.001000 0.001220 0.81967213
## 287 PyrT44 A.Methanol PuD0 P.Tai.IN 0.000213 0.000717 0.29707113
## 288 PyrT54 B.5mM PreD1 P.Tai.IN 0.000201 0.001430 0.14055944
## 289 PyrT55 B.5mM PreD1 P.Tai.IN 0.000440 0.003510 0.12535613
## 290 PyrT56 B.5mM PreD1 P.Tai.IN 0.000325 0.002580 0.12596899
## 291 PyrT57 B.5mM PreD1 P.Tai.IN 0.000794 0.003140 0.25286624
## 292 PyrT61 B.5mM PreD1 P.Tai.IN 0.000528 0.002640 0.20000000
## 293 PyrT62 B.5mM PreD1 P.Tai.IN 0.000657 0.003120 0.21057692
## 294 PyrT52 B.5mM PuD0 P.Tai.IN 0.000356 0.001940 0.18350515
## 295 PyrT58 B.5mM PuD0 P.Tai.IN 0.000440 0.002640 0.16666667
## 296 PyrT59 B.5mM PuD0 P.Tai.IN 0.000293 0.002130 0.13755869
## 297 PyrT60 B.5mM PuD0 P.Tai.IN 0.000182 0.002030 0.08965517
## 298 PyrT63 B.5mM PuD0 P.Tai.IN 0.000569 0.001820 0.31263736
## 299 PyrT64 B.5mM PuD0 P.Tai.IN 0.000220 0.000970 0.22680412PkTaiINbox<-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 rp49.2
## 300 PyrT21 A.Methanol PreD1 Q.Tai-DEL 2.33e-05 0.002420
## 301 PyrT11 A.Methanol PreD1 Q.Tai-DEL 2.18e-05 0.004680
## 302 PyrT19 A.Methanol PreD1 Q.Tai-DEL 3.30e-05 0.002680
## 303 PyrT20 A.Methanol PreD1 Q.Tai-DEL 5.12e-05 0.004500
## 304 PyrT30 A.Methanol PreD1 Q.Tai-DEL 6.59e-05 0.003430
## 305 PyrT37 A.Methanol PreD1 Q.Tai-DEL 7.69e-05 0.005150
## 306 PyrT27 A.Methanol PuD0 Q.Tai-DEL 2.35e-05 0.000534
## 307 PyrT34 A.Methanol PuD0 Q.Tai-DEL 1.32e-05 0.001380
## 308 PyrT35 A.Methanol PuD0 Q.Tai-DEL 2.80e-05 0.000939
## 309 PyrT36 A.Methanol PuD0 Q.Tai-DEL 2.04e-05 0.001220
## 310 PyrT44 A.Methanol PuD0 Q.Tai-DEL 5.47e-06 0.000717
## 311 PyrT54 B.5mM PreD1 Q.Tai-DEL 1.41e-05 0.001430
## 312 PyrT55 B.5mM PreD1 Q.Tai-DEL 7.04e-05 0.003510
## 313 PyrT56 B.5mM PreD1 Q.Tai-DEL 4.16e-05 0.002580
## 314 PyrT57 B.5mM PreD1 Q.Tai-DEL 6.59e-05 0.003140
## 315 PyrT61 B.5mM PreD1 Q.Tai-DEL 5.24e-05 0.002640
## 316 PyrT62 B.5mM PreD1 Q.Tai-DEL 1.47e-04 0.003120
## 317 PyrT52 B.5mM PuD0 Q.Tai-DEL 2.65e-05 0.001940
## 318 PyrT58 B.5mM PuD0 Q.Tai-DEL 2.43e-05 0.002640
## 319 PyrT59 B.5mM PuD0 Q.Tai-DEL 1.91e-05 0.002130
## 320 PyrT60 B.5mM PuD0 Q.Tai-DEL 1.45e-05 0.002030
## 321 PyrT63 B.5mM PuD0 Q.Tai-DEL 1.12e-05 0.001820
## 322 PyrT64 B.5mM PuD0 Q.Tai-DEL 4.64e-06 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 0.004783505PkTaiDELbox<-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
## 2Statistical tests
prepupatestPkTaiIN<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="P.Tai.IN")
t.test(gene.normal~Treatment,data=prepupatestPkTaiIN)##
## Welch Two Sample t-test
##
## data: gene.normal by Treatment
## t = -2.7159, df = 6.857, p-value = 0.03054
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.120110587 -0.008052751
## sample estimates:
## mean in group A.Methanol mean in group B.5mM
## 0.1118063 0.1758880pupatestPkTaiIN<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="P.Tai.IN")
t.test(gene.normal~Treatment,data=pupatestPkTaiIN)##
## Welch Two Sample t-test
##
## data: gene.normal by Treatment
## t = 3.2001, df = 4.189, p-value = 0.03082
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.09804053 1.23053027
## sample estimates:
## mean in group A.Methanol mean in group B.5mM
## 0.8504233 0.1861379prepupatestPkTaiDEL<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="Q.Tai-DEL")
t.test(gene.normal~Treatment,data=prepupatestPkTaiDEL)##
## Welch Two Sample t-test
##
## data: gene.normal by Treatment
## t = -1.8401, df = 6.438, p-value = 0.112
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.023800974 0.003177646
## sample estimates:
## mean in group A.Methanol mean in group B.5mM
## 0.01202038 0.02233205pupatestPkTaiDEL<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="Q.Tai-DEL")
t.test(gene.normal~Treatment,data=pupatestPkTaiDEL)##
## Welch Two Sample t-test
##
## data: gene.normal by Treatment
## t = 1.904, df = 4.277, p-value = 0.125
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.005580255 0.032039822
## sample estimates:
## mean in group A.Methanol mean in group B.5mM
## 0.021548397 0.008318614prepupatestPkTai<-subset(dataB,dataB$Stage.treated=="PreD1" & dataB$Gene=="O.Tai")
t.test(gene.normal~Treatment,data=prepupatestPkTai)##
## Welch Two Sample t-test
##
## data: gene.normal by Treatment
## t = -1.2219, df = 8.444, p-value = 0.2547
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.12216797 0.03703492
## sample estimates:
## mean in group A.Methanol mean in group B.5mM
## 0.1790833 0.2216498pupatestPkTai<-subset(dataB,dataB$Stage.treated=="PuD0" & dataB$Gene=="O.Tai")
t.test(gene.normal~Treatment,data=pupatestPkTai)##
## Welch Two Sample t-test
##
## data: gene.normal by Treatment
## t = 2.2676, df = 4.042, p-value = 0.08527
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1491536 1.5085764
## sample estimates:
## mean in group A.Methanol mean in group B.5mM
## 0.8874290 0.2077176Figure 3G to I: PkKr-h1 core, PkKr-h1 A and PkKr-h1 B
PkKrh1<-subset(dataBs,dataBs$Gene=="D.kr-h1")
PkKrh1box<-subset(dataB, Gene=="D.kr-h1")
PkKrh1box## Sample Treatment Stage.treated Gene SDM.gene rp49.2 gene.normal
## 1 PyrT21 A.Methanol PreD1 D.kr-h1 1.87e-05 0.002420 0.007727273
## 2 PyrT11 A.Methanol PreD1 D.kr-h1 1.99e-05 0.004680 0.004252137
## 3 PyrT19 A.Methanol PreD1 D.kr-h1 8.93e-06 0.002680 0.003332090
## 4 PyrT20 A.Methanol PreD1 D.kr-h1 1.64e-05 0.004500 0.003644444
## 5 PyrT30 A.Methanol PreD1 D.kr-h1 1.66e-05 0.003430 0.004839650
## 6 PyrT37 A.Methanol PreD1 D.kr-h1 1.32e-05 0.005150 0.002563107
## 7 PyrT27 A.Methanol PuD0 D.kr-h1 3.24e-05 0.000534 0.060674157
## 8 PyrT34 A.Methanol PuD0 D.kr-h1 1.63e-05 0.001380 0.011811594
## 9 PyrT35 A.Methanol PuD0 D.kr-h1 2.65e-05 0.000939 0.028221512
## 10 PyrT36 A.Methanol PuD0 D.kr-h1 1.80e-05 0.001220 0.014754098
## 11 PyrT44 A.Methanol PuD0 D.kr-h1 1.20e-05 0.000717 0.016736402
## 12 PyrT54 B.5mM PreD1 D.kr-h1 2.61e-04 0.001430 0.182517483
## 13 PyrT55 B.5mM PreD1 D.kr-h1 9.79e-04 0.003510 0.278917379
## 14 PyrT56 B.5mM PreD1 D.kr-h1 4.73e-04 0.002580 0.183333333
## 15 PyrT57 B.5mM PreD1 D.kr-h1 5.55e-04 0.003140 0.176751592
## 16 PyrT61 B.5mM PreD1 D.kr-h1 5.37e-04 0.002640 0.203409091
## 17 PyrT62 B.5mM PreD1 D.kr-h1 9.34e-04 0.003120 0.299358974
## 18 PyrT52 B.5mM PuD0 D.kr-h1 4.85e-04 0.001940 0.250000000
## 19 PyrT58 B.5mM PuD0 D.kr-h1 4.98e-04 0.002640 0.188636364
## 20 PyrT59 B.5mM PuD0 D.kr-h1 2.88e-04 0.002130 0.135211268
## 21 PyrT60 B.5mM PuD0 D.kr-h1 2.44e-04 0.002030 0.120197044
## 22 PyrT63 B.5mM PuD0 D.kr-h1 2.66e-04 0.001820 0.146153846
## 23 PyrT64 B.5mM PuD0 D.kr-h1 1.64e-04 0.000970 0.169072165PkKrh1box<-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 rp49.2
## 24 PyrT21 A.Methanol PreD1 E.Pkkr-h1A 4.43e-06 0.002420
## 25 PyrT11 A.Methanol PreD1 E.Pkkr-h1A 5.73e-06 0.004680
## 26 PyrT19 A.Methanol PreD1 E.Pkkr-h1A 1.93e-06 0.002680
## 27 PyrT20 A.Methanol PreD1 E.Pkkr-h1A 3.32e-06 0.004500
## 28 PyrT30 A.Methanol PreD1 E.Pkkr-h1A 3.74e-06 0.003430
## 29 PyrT37 A.Methanol PreD1 E.Pkkr-h1A 4.30e-06 0.005150
## 30 PyrT27 A.Methanol PuD0 E.Pkkr-h1A 4.80e-06 0.000534
## 31 PyrT34 A.Methanol PuD0 E.Pkkr-h1A 3.63e-06 0.001380
## 32 PyrT35 A.Methanol PuD0 E.Pkkr-h1A 5.40e-06 0.000939
## 33 PyrT36 A.Methanol PuD0 E.Pkkr-h1A 2.44e-06 0.001220
## 34 PyrT44 A.Methanol PuD0 E.Pkkr-h1A 2.03e-06 0.000717
## 35 PyrT54 B.5mM PreD1 E.Pkkr-h1A 6.53e-05 0.001430
## 36 PyrT55 B.5mM PreD1 E.Pkkr-h1A 3.12e-04 0.003510
## 37 PyrT56 B.5mM PreD1 E.Pkkr-h1A 1.42e-04 0.002580
## 38 PyrT57 B.5mM PreD1 E.Pkkr-h1A 1.72e-04 0.003140
## 39 PyrT61 B.5mM PreD1 E.Pkkr-h1A 1.74e-04 0.002640
## 40 PyrT62 B.5mM PreD1 E.Pkkr-h1A 2.19e-04 0.003120
## 41 PyrT52 B.5mM PuD0 E.Pkkr-h1A 8.27e-05 0.001940
## 42 PyrT58 B.5mM PuD0 E.Pkkr-h1A 4.86e-05 0.002640
## 43 PyrT59 B.5mM PuD0 E.Pkkr-h1A 2.52e-05 0.002130
## 44 PyrT60 B.5mM PuD0 E.Pkkr-h1A 3.08e-05 0.002030
## 45 PyrT63 B.5mM PuD0 E.Pkkr-h1A 2.54e-05 0.001820
## 46 PyrT64 B.5mM PuD0 E.Pkkr-h1A 1.42e-05 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 0.0146391753PkKrh1Abox<-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 rp49.2
## 47 PyrT21 A.Methanol PreD1 F.Pkkr-h1B 6.83e-06 0.002420
## 48 PyrT11 A.Methanol PreD1 F.Pkkr-h1B 2.66e-08 0.004680
## 49 PyrT19 A.Methanol PreD1 F.Pkkr-h1B 8.81e-08 0.002680
## 50 PyrT20 A.Methanol PreD1 F.Pkkr-h1B 1.14e-08 0.004500
## 51 PyrT30 A.Methanol PreD1 F.Pkkr-h1B 8.27e-09 0.003430
## 52 PyrT37 A.Methanol PreD1 F.Pkkr-h1B 2.64e-08 0.005150
## 53 PyrT27 A.Methanol PuD0 F.Pkkr-h1B 3.34e-08 0.000534
## 54 PyrT34 A.Methanol PuD0 F.Pkkr-h1B 3.03e-07 0.001380
## 55 PyrT35 A.Methanol PuD0 F.Pkkr-h1B 4.83e-08 0.000939
## 56 PyrT36 A.Methanol PuD0 F.Pkkr-h1B 3.21e-07 0.001220
## 57 PyrT44 A.Methanol PuD0 F.Pkkr-h1B 1.21e-06 0.000717
## 58 PyrT54 B.5mM PreD1 F.Pkkr-h1B 9.56e-08 0.001430
## 59 PyrT55 B.5mM PreD1 F.Pkkr-h1B 9.12e-07 0.003510
## 60 PyrT56 B.5mM PreD1 F.Pkkr-h1B 3.64e-07 0.002580
## 61 PyrT57 B.5mM PreD1 F.Pkkr-h1B 2.19e-07 0.003140
## 62 PyrT61 B.5mM PreD1 F.Pkkr-h1B 2.75e-07 0.002640
## 63 PyrT62 B.5mM PreD1 F.Pkkr-h1B 4.78e-07 0.003120
## 64 PyrT52 B.5mM PuD0 F.Pkkr-h1B 2.96e-07 0.001940
## 65 PyrT58 B.5mM PuD0 F.Pkkr-h1B 1.52e-07 0.002640
## 66 PyrT59 B.5mM PuD0 F.Pkkr-h1B 5.59e-08 0.002130
## 67 PyrT60 B.5mM PuD0 F.Pkkr-h1B 1.06e-07 0.002030
## 68 PyrT63 B.5mM PuD0 F.Pkkr-h1B 3.60e-08 0.001820
## 69 PyrT64 B.5mM PuD0 F.Pkkr-h1B 7.64e-07 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-04PkKrh1Bbox<-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
## 2Statistical 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 0.220714642pupatestPkkrh1<-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 0.16821178prepupatestPkkrh1A<-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 0.063411742pupatestPkkrh1A<-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 0.019439429prepupatestPkkrh1B<-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 0.0001324807pupatestPkkrh1B<-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 0.0001826705Figure 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.oviposition,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.oviposition,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 rp49.2 gene.normal
## 93 PyrT21 A.Methanol PreD1 G.Pkbr1 7.81e-06 0.002420 0.0032272727
## 94 PyrT11 A.Methanol PreD1 G.Pkbr1 1.21e-03 0.004680 0.2585470085
## 95 PyrT19 A.Methanol PreD1 G.Pkbr1 9.04e-06 0.002680 0.0033731343
## 96 PyrT20 A.Methanol PreD1 G.Pkbr1 4.92e-05 0.004500 0.0109333333
## 97 PyrT30 A.Methanol PreD1 G.Pkbr1 4.83e-05 0.003430 0.0140816327
## 98 PyrT37 A.Methanol PreD1 G.Pkbr1 1.84e-04 0.005150 0.0357281553
## 99 PyrT27 A.Methanol PuD0 G.Pkbr1 1.10e-05 0.000534 0.0205992509
## 100 PyrT34 A.Methanol PuD0 G.Pkbr1 1.48e-05 0.001380 0.0107246377
## 101 PyrT35 A.Methanol PuD0 G.Pkbr1 5.93e-06 0.000939 0.0063152290
## 102 PyrT36 A.Methanol PuD0 G.Pkbr1 2.07e-06 0.001220 0.0016967213
## 103 PyrT44 A.Methanol PuD0 G.Pkbr1 8.59e-07 0.000717 0.0011980474
## 104 PyrT54 B.5mM PreD1 G.Pkbr1 4.27e-06 0.001430 0.0029860140
## 105 PyrT55 B.5mM PreD1 G.Pkbr1 1.17e-05 0.003510 0.0033333333
## 106 PyrT56 B.5mM PreD1 G.Pkbr1 4.43e-06 0.002580 0.0017170543
## 107 PyrT57 B.5mM PreD1 G.Pkbr1 3.26e-05 0.003140 0.0103821656
## 108 PyrT61 B.5mM PreD1 G.Pkbr1 1.48e-05 0.002640 0.0056060606
## 109 PyrT62 B.5mM PreD1 G.Pkbr1 3.95e-05 0.003120 0.0126602564
## 110 PyrT52 B.5mM PuD0 G.Pkbr1 6.21e-06 0.001940 0.0032010309
## 111 PyrT58 B.5mM PuD0 G.Pkbr1 4.51e-06 0.002640 0.0017083333
## 112 PyrT59 B.5mM PuD0 G.Pkbr1 1.52e-06 0.002130 0.0007136150
## 113 PyrT60 B.5mM PuD0 G.Pkbr1 4.28e-07 0.002030 0.0002108374
## 114 PyrT63 B.5mM PuD0 G.Pkbr1 1.10e-06 0.001820 0.0006043956
## 115 PyrT64 B.5mM PuD0 G.Pkbr1 4.09e-07 0.000970 0.0004216495Pkbr1box<-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
## 2Figure5C
#boxplot
Pkbr1Z2box<-subset(dataB, Gene=="H.Pkbr1.Z2")
Pkbr1Z2box## Sample Treatment Stage.treated Gene SDM.gene rp49.2
## 162 PyrT21 A.Methanol PreD1 H.Pkbr1.Z2 8.22e-05 0.002420
## 163 PyrT11 A.Methanol PreD1 H.Pkbr1.Z2 8.16e-05 0.004680
## 164 PyrT19 A.Methanol PreD1 H.Pkbr1.Z2 6.33e-05 0.002680
## 165 PyrT20 A.Methanol PreD1 H.Pkbr1.Z2 8.40e-05 0.004500
## 166 PyrT30 A.Methanol PreD1 H.Pkbr1.Z2 6.07e-05 0.003430
## 167 PyrT37 A.Methanol PreD1 H.Pkbr1.Z2 1.11e-04 0.005150
## 168 PyrT27 A.Methanol PuD0 H.Pkbr1.Z2 1.44e-05 0.000534
## 169 PyrT34 A.Methanol PuD0 H.Pkbr1.Z2 3.86e-05 0.001380
## 170 PyrT35 A.Methanol PuD0 H.Pkbr1.Z2 3.89e-05 0.000939
## 171 PyrT36 A.Methanol PuD0 H.Pkbr1.Z2 2.87e-05 0.001220
## 172 PyrT44 A.Methanol PuD0 H.Pkbr1.Z2 2.31e-05 0.000717
## 173 PyrT54 B.5mM PreD1 H.Pkbr1.Z2 4.71e-05 0.001430
## 174 PyrT55 B.5mM PreD1 H.Pkbr1.Z2 5.20e-05 0.003510
## 175 PyrT56 B.5mM PreD1 H.Pkbr1.Z2 2.89e-05 0.002580
## 176 PyrT57 B.5mM PreD1 H.Pkbr1.Z2 6.84e-05 0.003140
## 177 PyrT61 B.5mM PreD1 H.Pkbr1.Z2 2.75e-05 0.002640
## 178 PyrT62 B.5mM PreD1 H.Pkbr1.Z2 4.39e-05 0.003120
## 179 PyrT52 B.5mM PuD0 H.Pkbr1.Z2 1.11e-05 0.001940
## 180 PyrT58 B.5mM PuD0 H.Pkbr1.Z2 9.55e-06 0.002640
## 181 PyrT59 B.5mM PuD0 H.Pkbr1.Z2 9.29e-06 0.002130
## 182 PyrT60 B.5mM PuD0 H.Pkbr1.Z2 5.63e-06 0.002030
## 183 PyrT63 B.5mM PuD0 H.Pkbr1.Z2 1.74e-05 0.001820
## 184 PyrT64 B.5mM PuD0 H.Pkbr1.Z2 9.76e-06 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 0.010061856Pkbr1Z2box<-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
## 2Figure5D
#boxplot
Pkbr1Z4box<-subset(dataB, Gene=="I.Pkbr1.Z4")
Pkbr1Z4box## Sample Treatment Stage.treated Gene SDM.gene rp49.2
## 185 PyrT21 A.Methanol PreD1 I.Pkbr1.Z4 7.82e-05 0.002420
## 186 PyrT11 A.Methanol PreD1 I.Pkbr1.Z4 7.51e-05 0.004680
## 187 PyrT19 A.Methanol PreD1 I.Pkbr1.Z4 6.70e-05 0.002680
## 188 PyrT20 A.Methanol PreD1 I.Pkbr1.Z4 7.87e-05 0.004500
## 189 PyrT30 A.Methanol PreD1 I.Pkbr1.Z4 6.26e-05 0.003430
## 190 PyrT37 A.Methanol PreD1 I.Pkbr1.Z4 1.14e-04 0.005150
## 191 PyrT27 A.Methanol PuD0 I.Pkbr1.Z4 2.06e-05 0.000534
## 192 PyrT34 A.Methanol PuD0 I.Pkbr1.Z4 4.29e-05 0.001380
## 193 PyrT35 A.Methanol PuD0 I.Pkbr1.Z4 4.24e-05 0.000939
## 194 PyrT36 A.Methanol PuD0 I.Pkbr1.Z4 3.33e-05 0.001220
## 195 PyrT44 A.Methanol PuD0 I.Pkbr1.Z4 1.90e-05 0.000717
## 196 PyrT54 B.5mM PreD1 I.Pkbr1.Z4 3.63e-05 0.001430
## 197 PyrT55 B.5mM PreD1 I.Pkbr1.Z4 6.05e-05 0.003510
## 198 PyrT56 B.5mM PreD1 I.Pkbr1.Z4 3.63e-05 0.002580
## 199 PyrT57 B.5mM PreD1 I.Pkbr1.Z4 7.41e-05 0.003140
## 200 PyrT61 B.5mM PreD1 I.Pkbr1.Z4 3.17e-05 0.002640
## 201 PyrT62 B.5mM PreD1 I.Pkbr1.Z4 4.69e-05 0.003120
## 202 PyrT52 B.5mM PuD0 I.Pkbr1.Z4 2.15e-05 0.001940
## 203 PyrT58 B.5mM PuD0 I.Pkbr1.Z4 1.84e-05 0.002640
## 204 PyrT59 B.5mM PuD0 I.Pkbr1.Z4 1.32e-05 0.002130
## 205 PyrT60 B.5mM PuD0 I.Pkbr1.Z4 8.71e-06 0.002030
## 206 PyrT63 B.5mM PuD0 I.Pkbr1.Z4 2.24e-05 0.001820
## 207 PyrT64 B.5mM PuD0 I.Pkbr1.Z4 1.48e-05 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 0.015257732Pkbr1Z4box<-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
## 2Figure5E
#boxplot
Pkbr3box<-subset(dataB, Gene=="M.Pkbr3")
Pkbr3box## Sample Treatment Stage.treated Gene SDM.gene rp49.2 gene.normal
## 139 PyrT21 A.Methanol PreD1 M.Pkbr3 3.08e-04 0.002420 0.12727273
## 140 PyrT11 A.Methanol PreD1 M.Pkbr3 3.36e-04 0.004680 0.07179487
## 141 PyrT19 A.Methanol PreD1 M.Pkbr3 3.52e-04 0.002680 0.13134328
## 142 PyrT20 A.Methanol PreD1 M.Pkbr3 5.97e-04 0.004500 0.13266667
## 143 PyrT30 A.Methanol PreD1 M.Pkbr3 5.73e-04 0.003430 0.16705539
## 144 PyrT37 A.Methanol PreD1 M.Pkbr3 6.05e-04 0.005150 0.11747573
## 145 PyrT27 A.Methanol PuD0 M.Pkbr3 1.41e-04 0.000534 0.26404494
## 146 PyrT34 A.Methanol PuD0 M.Pkbr3 2.43e-04 0.001380 0.17608696
## 147 PyrT35 A.Methanol PuD0 M.Pkbr3 1.63e-04 0.000939 0.17358892
## 148 PyrT36 A.Methanol PuD0 M.Pkbr3 1.39e-04 0.001220 0.11393443
## 149 PyrT44 A.Methanol PuD0 M.Pkbr3 6.93e-05 0.000717 0.09665272
## 150 PyrT54 B.5mM PreD1 M.Pkbr3 3.89e-04 0.001430 0.27202797
## 151 PyrT55 B.5mM PreD1 M.Pkbr3 6.09e-04 0.003510 0.17350427
## 152 PyrT56 B.5mM PreD1 M.Pkbr3 5.26e-04 0.002580 0.20387597
## 153 PyrT57 B.5mM PreD1 M.Pkbr3 6.86e-04 0.003140 0.21847134
## 154 PyrT61 B.5mM PreD1 M.Pkbr3 5.93e-04 0.002640 0.22462121
## 155 PyrT62 B.5mM PreD1 M.Pkbr3 8.11e-04 0.003120 0.25993590
## 156 PyrT52 B.5mM PuD0 M.Pkbr3 3.45e-04 0.001940 0.17783505
## 157 PyrT58 B.5mM PuD0 M.Pkbr3 3.95e-04 0.002640 0.14962121
## 158 PyrT59 B.5mM PuD0 M.Pkbr3 3.11e-04 0.002130 0.14600939
## 159 PyrT60 B.5mM PuD0 M.Pkbr3 2.83e-04 0.002030 0.13940887
## 160 PyrT63 B.5mM PuD0 M.Pkbr3 3.55e-04 0.001820 0.19505495
## 161 PyrT64 B.5mM PuD0 M.Pkbr3 1.86e-04 0.000970 0.19175258Pkbr3box<-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
## 2Figure5F
#boxplot
Pkbr3Z2box<-subset(dataB, Gene=="N.Pkbr3.Z2")
Pkbr3Z2box## Sample Treatment Stage.treated Gene SDM.gene rp49.2
## 254 PyrT21 A.Methanol PreD1 N.Pkbr3.Z2 1.66e-04 0.002420
## 255 PyrT11 A.Methanol PreD1 N.Pkbr3.Z2 1.66e-04 0.004680
## 256 PyrT19 A.Methanol PreD1 N.Pkbr3.Z2 1.90e-04 0.002680
## 257 PyrT20 A.Methanol PreD1 N.Pkbr3.Z2 2.77e-04 0.004500
## 258 PyrT30 A.Methanol PreD1 N.Pkbr3.Z2 2.81e-04 0.003430
## 259 PyrT37 A.Methanol PreD1 N.Pkbr3.Z2 3.83e-04 0.005150
## 260 PyrT27 A.Methanol PuD0 N.Pkbr3.Z2 6.10e-05 0.000534
## 261 PyrT34 A.Methanol PuD0 N.Pkbr3.Z2 1.09e-04 0.001380
## 262 PyrT35 A.Methanol PuD0 N.Pkbr3.Z2 9.62e-05 0.000939
## 263 PyrT36 A.Methanol PuD0 N.Pkbr3.Z2 7.10e-05 0.001220
## 264 PyrT44 A.Methanol PuD0 N.Pkbr3.Z2 3.41e-05 0.000717
## 265 PyrT54 B.5mM PreD1 N.Pkbr3.Z2 1.94e-04 0.001430
## 266 PyrT55 B.5mM PreD1 N.Pkbr3.Z2 3.02e-04 0.003510
## 267 PyrT56 B.5mM PreD1 N.Pkbr3.Z2 2.33e-04 0.002580
## 268 PyrT57 B.5mM PreD1 N.Pkbr3.Z2 3.59e-04 0.003140
## 269 PyrT61 B.5mM PreD1 N.Pkbr3.Z2 3.34e-04 0.002640
## 270 PyrT62 B.5mM PreD1 N.Pkbr3.Z2 4.55e-04 0.003120
## 271 PyrT52 B.5mM PuD0 N.Pkbr3.Z2 1.93e-04 0.001940
## 272 PyrT58 B.5mM PuD0 N.Pkbr3.Z2 1.89e-04 0.002640
## 273 PyrT59 B.5mM PuD0 N.Pkbr3.Z2 1.67e-04 0.002130
## 274 PyrT60 B.5mM PuD0 N.Pkbr3.Z2 1.42e-04 0.002030
## 275 PyrT63 B.5mM PuD0 N.Pkbr3.Z2 2.16e-04 0.001820
## 276 PyrT64 B.5mM PuD0 N.Pkbr3.Z2 1.00e-04 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 0.10309278Pkbr3Z2box<-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
## 2Figure Pkbr2 and Pkbr2 Z2, Z4
#boxplot
Pkbr2box<-subset(dataB, Gene=="J.Pkbr2")
Pkbr2box## Sample Treatment Stage.treated Gene SDM.gene rp49.2 gene.normal
## 116 PyrT21 A.Methanol PreD1 J.Pkbr2 2.25e-05 0.002420 0.009297521
## 117 PyrT11 A.Methanol PreD1 J.Pkbr2 2.51e-05 0.004680 0.005363248
## 118 PyrT19 A.Methanol PreD1 J.Pkbr2 9.13e-06 0.002680 0.003406716
## 119 PyrT20 A.Methanol PreD1 J.Pkbr2 3.35e-05 0.004500 0.007444444
## 120 PyrT30 A.Methanol PreD1 J.Pkbr2 2.64e-05 0.003430 0.007696793
## 121 PyrT37 A.Methanol PreD1 J.Pkbr2 4.41e-05 0.005150 0.008563107
## 122 PyrT27 A.Methanol PuD0 J.Pkbr2 1.19e-05 0.000534 0.022284644
## 123 PyrT34 A.Methanol PuD0 J.Pkbr2 1.80e-05 0.001380 0.013043478
## 124 PyrT35 A.Methanol PuD0 J.Pkbr2 2.13e-05 0.000939 0.022683706
## 125 PyrT36 A.Methanol PuD0 J.Pkbr2 1.44e-05 0.001220 0.011803279
## 126 PyrT44 A.Methanol PuD0 J.Pkbr2 7.42e-06 0.000717 0.010348675
## 127 PyrT54 B.5mM PreD1 J.Pkbr2 1.11e-05 0.001430 0.007762238
## 128 PyrT55 B.5mM PreD1 J.Pkbr2 1.44e-05 0.003510 0.004102564
## 129 PyrT56 B.5mM PreD1 J.Pkbr2 1.76e-05 0.002580 0.006821705
## 130 PyrT57 B.5mM PreD1 J.Pkbr2 2.27e-05 0.003140 0.007229299
## 131 PyrT61 B.5mM PreD1 J.Pkbr2 2.01e-05 0.002640 0.007613636
## 132 PyrT62 B.5mM PreD1 J.Pkbr2 3.01e-05 0.003120 0.009647436
## 133 PyrT52 B.5mM PuD0 J.Pkbr2 1.71e-05 0.001940 0.008814433
## 134 PyrT58 B.5mM PuD0 J.Pkbr2 2.14e-05 0.002640 0.008106061
## 135 PyrT59 B.5mM PuD0 J.Pkbr2 1.94e-05 0.002130 0.009107981
## 136 PyrT60 B.5mM PuD0 J.Pkbr2 1.62e-05 0.002030 0.007980296
## 137 PyrT63 B.5mM PuD0 J.Pkbr2 1.99e-05 0.001820 0.010934066
## 138 PyrT64 B.5mM PuD0 J.Pkbr2 1.37e-05 0.000970 0.014123711Pkbr2box<-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 rp49.2
## 208 PyrT21 A.Methanol PreD1 K.Pkbr2.Z2 4.69e-05 0.002420
## 209 PyrT11 A.Methanol PreD1 K.Pkbr2.Z2 3.40e-05 0.004680
## 210 PyrT19 A.Methanol PreD1 K.Pkbr2.Z2 1.49e-05 0.002680
## 211 PyrT20 A.Methanol PreD1 K.Pkbr2.Z2 4.38e-05 0.004500
## 212 PyrT30 A.Methanol PreD1 K.Pkbr2.Z2 3.48e-05 0.003430
## 213 PyrT37 A.Methanol PreD1 K.Pkbr2.Z2 4.94e-05 0.005150
## 214 PyrT27 A.Methanol PuD0 K.Pkbr2.Z2 2.09e-05 0.000534
## 215 PyrT34 A.Methanol PuD0 K.Pkbr2.Z2 2.12e-05 0.001380
## 216 PyrT35 A.Methanol PuD0 K.Pkbr2.Z2 2.48e-05 0.000939
## 217 PyrT36 A.Methanol PuD0 K.Pkbr2.Z2 2.09e-05 0.001220
## 218 PyrT44 A.Methanol PuD0 K.Pkbr2.Z2 1.13e-05 0.000717
## 219 PyrT54 B.5mM PreD1 K.Pkbr2.Z2 1.80e-05 0.001430
## 220 PyrT55 B.5mM PreD1 K.Pkbr2.Z2 1.73e-05 0.003510
## 221 PyrT56 B.5mM PreD1 K.Pkbr2.Z2 1.75e-05 0.002580
## 222 PyrT57 B.5mM PreD1 K.Pkbr2.Z2 2.88e-05 0.003140
## 223 PyrT61 B.5mM PreD1 K.Pkbr2.Z2 2.60e-05 0.002640
## 224 PyrT62 B.5mM PreD1 K.Pkbr2.Z2 3.61e-05 0.003120
## 225 PyrT52 B.5mM PuD0 K.Pkbr2.Z2 2.04e-05 0.001940
## 226 PyrT58 B.5mM PuD0 K.Pkbr2.Z2 3.13e-05 0.002640
## 227 PyrT59 B.5mM PuD0 K.Pkbr2.Z2 2.27e-05 0.002130
## 228 PyrT60 B.5mM PuD0 K.Pkbr2.Z2 1.87e-05 0.002030
## 229 PyrT63 B.5mM PuD0 K.Pkbr2.Z2 2.32e-05 0.001820
## 230 PyrT64 B.5mM PuD0 K.Pkbr2.Z2 1.44e-05 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 0.014845361Pkbr2Z2box<-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 rp49.2
## 231 PyrT21 A.Methanol PreD1 L.Pkbr2.Z4 3.59e-05 0.002420
## 232 PyrT11 A.Methanol PreD1 L.Pkbr2.Z4 3.09e-05 0.004680
## 233 PyrT19 A.Methanol PreD1 L.Pkbr2.Z4 1.73e-05 0.002680
## 234 PyrT20 A.Methanol PreD1 L.Pkbr2.Z4 5.00e-05 0.004500
## 235 PyrT30 A.Methanol PreD1 L.Pkbr2.Z4 4.83e-05 0.003430
## 236 PyrT37 A.Methanol PreD1 L.Pkbr2.Z4 5.40e-05 0.005150
## 237 PyrT27 A.Methanol PuD0 L.Pkbr2.Z4 2.61e-05 0.000534
## 238 PyrT34 A.Methanol PuD0 L.Pkbr2.Z4 3.05e-05 0.001380
## 239 PyrT35 A.Methanol PuD0 L.Pkbr2.Z4 3.32e-05 0.000939
## 240 PyrT36 A.Methanol PuD0 L.Pkbr2.Z4 3.03e-05 0.001220
## 241 PyrT44 A.Methanol PuD0 L.Pkbr2.Z4 1.58e-05 0.000717
## 242 PyrT54 B.5mM PreD1 L.Pkbr2.Z4 1.67e-05 0.001430
## 243 PyrT55 B.5mM PreD1 L.Pkbr2.Z4 2.28e-05 0.003510
## 244 PyrT56 B.5mM PreD1 L.Pkbr2.Z4 2.11e-05 0.002580
## 245 PyrT57 B.5mM PreD1 L.Pkbr2.Z4 3.79e-05 0.003140
## 246 PyrT61 B.5mM PreD1 L.Pkbr2.Z4 2.88e-05 0.002640
## 247 PyrT62 B.5mM PreD1 L.Pkbr2.Z4 5.37e-05 0.003120
## 248 PyrT52 B.5mM PuD0 L.Pkbr2.Z4 2.96e-05 0.001940
## 249 PyrT58 B.5mM PuD0 L.Pkbr2.Z4 3.85e-05 0.002640
## 250 PyrT59 B.5mM PuD0 L.Pkbr2.Z4 3.32e-05 0.002130
## 251 PyrT60 B.5mM PuD0 L.Pkbr2.Z4 2.57e-05 0.002030
## 252 PyrT63 B.5mM PuD0 L.Pkbr2.Z4 3.22e-05 0.001820
## 253 PyrT64 B.5mM PuD0 L.Pkbr2.Z4 1.88e-05 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 0.019381443Pkbr2Z4box<-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
## 2Statistical 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 0.006114147pupatestPkbr1<-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 0.001143310prepupatestPkbr1z2<-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 0.01753734pupatestPkbr1z2<-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 0.006016045prepupatestPkbr1z4<-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 0.01788820pupatestPkbr1z4<-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 0.009350903prepupatestPkbr2<-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 0.007196146pupatestPkbr2<-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 0.009844425prepupatestPkbr2z2<-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 0.009148351pupatestPkbr2z2<-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 0.01163887prepupatestPkbr2z4<-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 0.01109051pupatestPkbr2z4<-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 0.01586029prepupatestPkbr3<-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 0.2254061pupatestPkbr3<-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 0.1666137prepupatestPkbr3z2<-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 0.11644900pupatestPkbr3z2<-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 0.09020067Supplementary 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.oviposition,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.oviposition,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")