The growth condition was exactly similar to the plants used for FY staining, seeds germinated for 4 complete days on control condition, and at d5 the seedlings were transferred to +/- 75 mM NaCl for two complete days. Then the roots were harvested and used to measure the suberin monomers using the GC-MS.

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getwd()
## [1] "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20221121_suberin_composition_quantification_col_duf-5_root_Ming"
list.files(pattern = ".csv")
## [1] "DicarboxylicAcids.csv" "FattyAlcohol.csv"      "HydroxyFattyAcid.csv" 
## [4] "TotaL-FA.csv"
Total.FA <- read.csv("TotaL-FA.csv")
Total.FA
colnames(Total.FA)[1]<-"Genotype"
colnames(Total.FA)[3]<-"sixteen"
colnames(Total.FA)[4]<-"eighteen"
colnames(Total.FA)[5]<-"twenty"
colnames(Total.FA)[6]<-"twenty.two"
colnames(Total.FA)[7]<-"twenty.four"
Total.FA
Total.FA$All.ID<-paste(Total.FA$Genotype,Total.FA$Condition, sep="_")
Total.FA
library(ggplot2)
library(ggpubr)
library(multcompView)
aov(sixteen ~ All.ID, data = Total.FA)
## Call:
##    aov(formula = sixteen ~ All.ID, data = Total.FA)
## 
## Terms:
##                   All.ID Residuals
## Sum of Squares  236.7247  224.3885
## Deg. of Freedom        3        16
## 
## Residual standard error: 3.7449
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(sixteen ~ All.ID, data = Total.FA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = sixteen ~ All.ID, data = Total.FA)
## 
## $All.ID
##                               diff         lwr        upr     p adj
## Col_Salt-Col_control     7.6412430   0.8649662 14.4175198 0.0244810
## duf_control-Col_control  5.7167147  -1.0595621 12.4929916 0.1144275
## duf_Salt-Col_control    -0.1304853  -6.9067622  6.6457915 0.9999373
## duf_control-Col_Salt    -1.9245283  -8.7008051  4.8517486 0.8476435
## duf_Salt-Col_Salt       -7.7717283 -14.5480052 -0.9954515 0.0219373
## duf_Salt-duf_control    -5.8472001 -12.6234769  0.9290768 0.1037204
P3 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P3)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"        "ab"         "b"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
sixteen_totalFA <- ggplot(data = Total.FA, mapping = aes(x = All.ID, y = sixteen, colour = Condition)) 
sixteen_totalFA <- sixteen_totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

sixteen_totalFA <- sixteen_totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
sixteen_totalFA <- sixteen_totalFA + scale_color_manual(values = c("blue","red"))

sixteen_totalFA <- sixteen_totalFA + ylab("C16:0 in total fatty acid, ug/mg dry weight") + xlab("")
sixteen_totalFA <- sixteen_totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
sixteen_totalFA <- sixteen_totalFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
sixteen_totalFA

sixteen_totalFA <- ggplot(data = Total.FA, mapping = aes(x = Genotype, y = sixteen, colour = Genotype)) 
sixteen_totalFA <- sixteen_totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

sixteen_totalFA <- sixteen_totalFA + facet_grid(~ Condition , scales = "free_y")
sixteen_totalFA <- sixteen_totalFA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
sixteen_totalFA <- sixteen_totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
sixteen_totalFA <- sixteen_totalFA + ylab("C16:0 in total fatty acid, ug/mg dry weight") + xlab("")
sixteen_totalFA <- sixteen_totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
sixteen_totalFA <- sixteen_totalFA + rremove("legend")
sixteen_totalFA

aov(eighteen ~ All.ID, data = Total.FA)
## Call:
##    aov(formula = eighteen ~ All.ID, data = Total.FA)
## 
## Terms:
##                   All.ID Residuals
## Sum of Squares  178.2993  183.6628
## Deg. of Freedom        3        16
## 
## Residual standard error: 3.388057
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(eighteen ~ All.ID, data = Total.FA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = eighteen ~ All.ID, data = Total.FA)
## 
## $All.ID
##                               diff         lwr        upr     p adj
## Col_Salt-Col_control     6.3290483   0.1984691 12.4596276 0.0418229
## duf_control-Col_control  4.6850797  -1.4454996 10.8156589 0.1692323
## duf_Salt-Col_control    -0.6629895  -6.7935688  5.4675897 0.9893328
## duf_control-Col_Salt    -1.6439687  -7.7745479  4.4866106 0.8679745
## duf_Salt-Col_Salt       -6.9920378 -13.1226171 -0.8614586 0.0227510
## duf_Salt-duf_control    -5.3480692 -11.4786484  0.7825101 0.0987871
P4 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P4)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"        "ab"         "b"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
eighteen_totalFA <- ggplot(data = Total.FA, mapping = aes(x = All.ID, y = eighteen, colour = Condition)) 
eighteen_totalFA <- eighteen_totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

eighteen_totalFA <- eighteen_totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
eighteen_totalFA <- eighteen_totalFA + scale_color_manual(values = c("blue","red"))

eighteen_totalFA <- eighteen_totalFA + ylab("C18:0 in total fatty acid, ug/mg dry weight") + xlab("")
eighteen_totalFA <- eighteen_totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
eighteen_totalFA <- eighteen_totalFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
eighteen_totalFA

eighteen_totalFA <- ggplot(data = Total.FA, mapping = aes(x = Genotype, y = eighteen, colour = Genotype)) 
eighteen_totalFA <- eighteen_totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

eighteen_totalFA <- eighteen_totalFA + facet_grid(~ Condition , scales = "free_y")
eighteen_totalFA <- eighteen_totalFA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
sixteen_totalFA <- eighteen_totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
eighteen_totalFA <- eighteen_totalFA + ylab("C18:0 in total fatty acid, ug/mg dry weight") + xlab("")
eighteen_totalFA <- eighteen_totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
eighteen_totalFA <- eighteen_totalFA + rremove("legend")
eighteen_totalFA

aov(twenty ~ All.ID, data = Total.FA)
## Call:
##    aov(formula = twenty ~ All.ID, data = Total.FA)
## 
## Terms:
##                     All.ID  Residuals
## Sum of Squares  0.14444056 0.09260118
## Deg. of Freedom          3         16
## 
## Residual standard error: 0.0760761
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty ~ All.ID, data = Total.FA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty ~ All.ID, data = Total.FA)
## 
## $All.ID
##                                diff         lwr       upr     p adj
## Col_Salt-Col_control    -0.01064118 -0.14829844 0.1270161 0.9960229
## duf_control-Col_control  0.17179480  0.03413754 0.3094521 0.0122639
## duf_Salt-Col_control     0.15646840  0.01881114 0.2941257 0.0232578
## duf_control-Col_Salt     0.18243598  0.04477872 0.3200932 0.0078290
## duf_Salt-Col_Salt        0.16710958  0.02945232 0.3047668 0.0149295
## duf_Salt-duf_control    -0.01532640 -0.15298366 0.1223309 0.9883891
P5 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P5)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "b"         "b"         "a"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_totalFA <- ggplot(data = Total.FA, mapping = aes(x = All.ID, y = twenty, colour = Condition)) 
twenty_totalFA <- twenty_totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_totalFA <- twenty_totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_totalFA <- twenty_totalFA + scale_color_manual(values = c("blue","red"))

twenty_totalFA <- twenty_totalFA + ylab("C20:0 in total fatty acid, ug/mg dry weight") + xlab("")
twenty_totalFA <- twenty_totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_totalFA <- twenty_totalFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_totalFA

twenty_totalFA <- ggplot(data = Total.FA, mapping = aes(x = Genotype, y = twenty, colour = Genotype)) 
twenty_totalFA <- twenty_totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_totalFA <- twenty_totalFA + facet_grid(~ Condition , scales = "free_y")
twenty_totalFA <- twenty_totalFA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
twenty_totalFA <- twenty_totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_totalFA <- twenty_totalFA + ylab("C20:0 in total fatty acid, ug/mg dry weight") + xlab("")
twenty_totalFA <- twenty_totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_totalFA <- twenty_totalFA + rremove("legend")
twenty_totalFA

aov(twenty.two ~ All.ID, data = Total.FA)
## Call:
##    aov(formula = twenty.two ~ All.ID, data = Total.FA)
## 
## Terms:
##                   All.ID Residuals
## Sum of Squares  2.351261  1.295894
## Deg. of Freedom        3        16
## 
## Residual standard error: 0.2845933
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty.two ~ All.ID, data = Total.FA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty.two ~ All.ID, data = Total.FA)
## 
## $All.ID
##                               diff         lwr       upr     p adj
## Col_Salt-Col_control    0.23492396 -0.28003847 0.7498864 0.5729195
## duf_control-Col_control 0.77766134  0.26269891 1.2926238 0.0026692
## duf_Salt-Col_control    0.78783523  0.27287280 1.3027977 0.0023800
## duf_control-Col_Salt    0.54273738  0.02777495 1.0576998 0.0370938
## duf_Salt-Col_Salt       0.55291127  0.03794884 1.0678737 0.0332094
## duf_Salt-duf_control    0.01017389 -0.50478854 0.5251363 0.9999322
P6 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P6)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "b"         "b"         "a"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_two_totalFA <- ggplot(data = Total.FA, mapping = aes(x = All.ID, y = twenty.two, colour = Condition)) 
twenty_two_totalFA <- twenty_two_totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_two_totalFA <- twenty_two_totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_two_totalFA <- twenty_two_totalFA + scale_color_manual(values = c("blue","red"))
twenty_two_totalFA <- twenty_two_totalFA + ylab("C22:0 in total fatty acid, ug/mg dry weight") + xlab("")
twenty_two_totalFA <- twenty_two_totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_two_totalFA <- twenty_two_totalFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_two_totalFA

twenty_two_totalFA <- ggplot(data = Total.FA, mapping = aes(x = Genotype, y = twenty.two, colour = Genotype)) 
twenty_two_totalFA <- twenty_two_totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_two_totalFA <- twenty_two_totalFA + facet_grid(~ Condition , scales = "free_y")
twenty_two_totalFA <- twenty_two_totalFA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
twenty_two_totalFA <- twenty_two_totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_two_totalFA <- twenty_two_totalFA + ylab("C22:0 in total fatty acid, ug/mg dry weight") + xlab("")
twenty_two_totalFA <- twenty_two_totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_two_totalFA <- twenty_two_totalFA + rremove("legend")
twenty_two_totalFA

aov(twenty.four ~ All.ID, data = Total.FA)
## Call:
##    aov(formula = twenty.four ~ All.ID, data = Total.FA)
## 
## Terms:
##                    All.ID Residuals
## Sum of Squares  0.6598592 0.2595728
## Deg. of Freedom         3        16
## 
## Residual standard error: 0.1273707
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty.four ~ All.ID, data = Total.FA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty.four ~ All.ID, data = Total.FA)
## 
## $All.ID
##                              diff         lwr       upr     p adj
## Col_Salt-Col_control    0.1881794 -0.04229383 0.4186526 0.1311461
## duf_control-Col_control 0.3050840  0.07461074 0.5355572 0.0079005
## duf_Salt-Col_control    0.5002525  0.26977927 0.7307257 0.0000671
## duf_control-Col_Salt    0.1169046 -0.11356867 0.3473778 0.4877354
## duf_Salt-Col_Salt       0.3120731  0.08159986 0.5425463 0.0066219
## duf_Salt-duf_control    0.1951685 -0.03530471 0.4256418 0.1125955
P7 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P7)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##        "ab"        "ac"         "c"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_four_totalFA <- ggplot(data = Total.FA, mapping = aes(x = All.ID, y = twenty.four, colour = Condition)) 
twenty_four_totalFA <- twenty_four_totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_four_totalFA <- twenty_four_totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_four_totalFA <- twenty_four_totalFA + scale_color_manual(values = c("blue","red"))
twenty_four_totalFA <- twenty_four_totalFA + ylab("C24:0 in total fatty acid, ug/mg dry weight") + xlab("")
twenty_four_totalFA <- twenty_four_totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_four_totalFA <- twenty_four_totalFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_four_totalFA <- twenty_four_totalFA + rremove("legend")
twenty_four_totalFA

twenty_four_totalFA <- ggplot(data = Total.FA, mapping = aes(x = Genotype, y = twenty.four, colour = Genotype)) 
twenty_four_totalFA <- twenty_four_totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_four_totalFA <- twenty_four_totalFA + facet_grid(~ Condition , scales = "free_y")
twenty_four_totalFA <- twenty_four_totalFA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
twenty_four_totalFA <- twenty_four_totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_four_totalFA <- twenty_four_totalFA + ylab("C24:0 in total fatty acid, ug/mg dry weight") + xlab("")
twenty_four_totalFA <- twenty_four_totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_four_totalFA <- twenty_four_totalFA + rremove("legend")
twenty_four_totalFA

aov(total.FA ~ All.ID, data = Total.FA)
## Call:
##    aov(formula = total.FA ~ All.ID, data = Total.FA)
## 
## Terms:
##                   All.ID Residuals
## Sum of Squares  825.3318  773.4920
## Deg. of Freedom        3        16
## 
## Residual standard error: 6.952931
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(total.FA ~ All.ID, data = Total.FA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = total.FA ~ All.ID, data = Total.FA)
## 
## $All.ID
##                                diff         lwr       upr     p adj
## Col_Salt-Col_control     14.3827535   1.8016488 26.963858 0.0224054
## duf_control-Col_control  11.6563345  -0.9247702 24.237439 0.0743832
## duf_Salt-Col_control      0.6510813 -11.9300234 13.232186 0.9987924
## duf_control-Col_Salt     -2.7264190 -15.3075236  9.854686 0.9241746
## duf_Salt-Col_Salt       -13.7316722 -26.3127769 -1.150568 0.0300510
## duf_Salt-duf_control    -11.0052532 -23.5863579  1.575851 0.0975774
P8 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P8)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"        "ab"         "b"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
totalFA <- ggplot(data = Total.FA, mapping = aes(x = All.ID, y = total.FA, colour = Condition)) 
totalFA <- totalFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

totalFA <- totalFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
totalFA <- totalFA + scale_color_manual(values = c("blue","red"))
totalFA <- totalFA + ylab("Total fatty acid, ug/mg dry weight") + xlab("")
totalFA <- totalFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
totalFA <- totalFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
totalFA

totalFA_final <- ggplot(data = Total.FA, mapping = aes(x = Genotype, y = total.FA, colour = Genotype)) 
totalFA_final <- totalFA_final + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

totalFA_final <- totalFA_final + facet_grid(~ Condition , scales = "free_y")
totalFA_final <- totalFA_final + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
totalFA_final <- totalFA_final + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
totalFA_final <- totalFA_final + ylab("Total fatty acid, ug/mg dry weight") + xlab("")
totalFA_final <- totalFA_final + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
totalFA_final <- totalFA_final + rremove("legend")
totalFA_final

#AS A conclusion, duf-5 seems to be contain higher fatty acids monomers under control condition compared to Col. However, under the salt stress, duf-5 seems containing higher amounts of VLC FA, starting from C20 to C24. However, the total FA dropped in salt-treated duf-5 mutant, just because it possesses less amount of C18 which is the most abumdant FA monomer. now lets move on to fatty alcohol……

Fatty_Alcohol <- read.csv("FattyAlcohol.csv")
Fatty_Alcohol
colnames(Fatty_Alcohol)[1]<-"Genotype"
colnames(Fatty_Alcohol)[3]<-"eighteen.OH"
colnames(Fatty_Alcohol)[4]<-"twenty.OH"
colnames(Fatty_Alcohol)[5]<-"twenty.two.OH"
colnames(Fatty_Alcohol)[6]<-"twenty.four.OH"
colnames(Fatty_Alcohol)[7]<-"second.twenty.four.OH"
Fatty_Alcohol
Fatty_Alcohol$All.ID<-paste(Fatty_Alcohol$Genotype,Fatty_Alcohol$Condition, sep="_")
Fatty_Alcohol
aov(total.OH ~ All.ID, data = Fatty_Alcohol)
## Call:
##    aov(formula = total.OH ~ All.ID, data = Fatty_Alcohol)
## 
## Terms:
##                    All.ID Residuals
## Sum of Squares  12.982189  5.249607
## Deg. of Freedom         3        16
## 
## Residual standard error: 0.5728005
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(total.OH ~ All.ID, data = Fatty_Alcohol))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = total.OH ~ All.ID, data = Fatty_Alcohol)
## 
## $All.ID
##                                  diff         lwr         upr     p adj
## Col_Salt-Col_control     1.0852730472  0.04880898  2.12173712 0.0385371
## duf_control-Col_control -0.0007838826 -1.03724795  1.03568019 1.0000000
## duf_Salt-Col_control     1.9152696832  0.87880561  2.95173375 0.0003876
## duf_control-Col_Salt    -1.0860569298 -2.12252100 -0.04959286 0.0383750
## duf_Salt-Col_Salt        0.8299966360 -0.20646743  1.86646071 0.1417351
## duf_Salt-duf_control     1.9160535658  0.87958950  2.95251763 0.0003860
P8 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P8)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "b"         "a"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = All.ID, y = total.OH, colour = Condition)) 
total_fatty_alcoh <- total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

total_fatty_alcoh <- total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
total_fatty_alcoh <- total_fatty_alcoh + scale_color_manual(values = c("blue","red"))
total_fatty_alcoh <- total_fatty_alcoh + ylab("Total fatty alcohol, ug/mg dry weight") + xlab("")
total_fatty_alcoh <- total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
total_fatty_alcoh <- total_fatty_alcoh + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
total_fatty_alcoh

total_fatty_alcoh_final <- ggplot(data = Fatty_Alcohol, mapping = aes(x = Genotype, y = total.OH, colour = Genotype)) 
total_fatty_alcoh_final <- total_fatty_alcoh_final + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
total_fatty_alcoh_final <- total_fatty_alcoh_final + facet_grid(~ Condition , scales = "free_y")


total_fatty_alcoh_final <- total_fatty_alcoh_final + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
total_fatty_alcoh_final <- total_fatty_alcoh_final + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
total_fatty_alcoh_final <- total_fatty_alcoh_final + ylab("Total fatty alcohol, ug/mg dry weight") + xlab("")
total_fatty_alcoh_final <- total_fatty_alcoh_final + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
total_fatty_alcoh_final <- total_fatty_alcoh_final + rremove("legend")
total_fatty_alcoh_final

aov(eighteen.OH ~ All.ID, data = Fatty_Alcohol)
## Call:
##    aov(formula = eighteen.OH ~ All.ID, data = Fatty_Alcohol)
## 
## Terms:
##                   All.ID Residuals
## Sum of Squares  2.936860  1.622228
## Deg. of Freedom        3        16
## 
## Residual standard error: 0.3184168
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(eighteen.OH ~ All.ID, data = Fatty_Alcohol))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = eighteen.OH ~ All.ID, data = Fatty_Alcohol)
## 
## $All.ID
##                                diff         lwr         upr     p adj
## Col_Salt-Col_control     0.50301937 -0.07314563  1.07918437 0.0984368
## duf_control-Col_control -0.09130035 -0.66746534  0.48486465 0.9680070
## duf_Salt-Col_control     0.85139368  0.27522868  1.42755868 0.0032230
## duf_control-Col_Salt    -0.59431971 -1.17048471 -0.01815472 0.0420237
## duf_Salt-Col_Salt        0.34837431 -0.22779068  0.92453931 0.3413341
## duf_Salt-duf_control     0.94269403  0.36652903  1.51885903 0.0012884
P3 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P3)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##        "ab"         "c"         "a"        "bc"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
eighteen_total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = All.ID, y = eighteen.OH, colour = Condition)) 
eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + scale_color_manual(values = c("blue","red"))
eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + ylab("C18:0-OH in total fatty alcohol, ug/mg dry weight") + xlab("")
eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
eighteen_total_fatty_alcoh

eighteen_total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = Genotype, y = eighteen.OH, colour = Genotype)) 
eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + facet_grid (~ Condition, scales = "free_y")

eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + ylab("C18:0-OH in total fatty alcohol, ug/mg dry weight") + xlab("") + rremove("legend")
eighteen_total_fatty_alcoh <- eighteen_total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
eighteen_total_fatty_alcoh 

aov(twenty.OH ~ All.ID, data = Fatty_Alcohol)
## Call:
##    aov(formula = twenty.OH ~ All.ID, data = Fatty_Alcohol)
## 
## Terms:
##                    All.ID Residuals
## Sum of Squares  1.0399240 0.2284482
## Deg. of Freedom         3        16
## 
## Residual standard error: 0.1194906
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty.OH ~ All.ID, data = Fatty_Alcohol))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty.OH ~ All.ID, data = Fatty_Alcohol)
## 
## $All.ID
##                                diff         lwr          upr     p adj
## Col_Salt-Col_control     0.30241787  0.08620343  0.518632307 0.0051053
## duf_control-Col_control  0.08032324 -0.13589120  0.296537682 0.7160735
## duf_Salt-Col_control     0.58787979  0.37166535  0.804094234 0.0000044
## duf_control-Col_Salt    -0.22209463 -0.43830907 -0.005880185 0.0430402
## duf_Salt-Col_Salt        0.28546193  0.06924749  0.501676367 0.0080610
## duf_Salt-duf_control     0.50755655  0.29134211  0.723770992 0.0000268
P4 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P4)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "b"         "c"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = All.ID, y = twenty.OH, colour = Condition)) 
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + scale_color_manual(values = c("blue","red"))
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + ylab("C20:0-OH in total fatty alcohol, ug/mg dry weight") + xlab("")
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_total_fatty_alcoh

twenty_total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = Genotype, y = twenty.OH, colour = Genotype)) 
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + facet_grid(~Condition, scales = "free_y")
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col") 
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + ylab("C20:0-OH in total fatty alcohol, ug/mg dry weight") + xlab("")+ rremove("legend")
twenty_total_fatty_alcoh <- twenty_total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_total_fatty_alcoh

aov(twenty.two.OH ~ All.ID, data = Fatty_Alcohol)
## Call:
##    aov(formula = twenty.two.OH ~ All.ID, data = Fatty_Alcohol)
## 
## Terms:
##                    All.ID Residuals
## Sum of Squares  0.6719018 0.4071505
## Deg. of Freedom         3        16
## 
## Residual standard error: 0.1595209
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty.two.OH ~ All.ID, data = Fatty_Alcohol))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty.two.OH ~ All.ID, data = Fatty_Alcohol)
## 
## $All.ID
##                                 diff         lwr        upr     p adj
## Col_Salt-Col_control     0.255194644 -0.03345323 0.54384252 0.0929563
## duf_control-Col_control  0.001882358 -0.28676552 0.29053023 0.9999976
## duf_Salt-Col_control     0.434561075  0.14591320 0.72320895 0.0027419
## duf_control-Col_Salt    -0.253312285 -0.54196016 0.03533559 0.0961551
## duf_Salt-Col_Salt        0.179366431 -0.10928144 0.46801431 0.3190902
## duf_Salt-duf_control     0.432678717  0.14403084 0.72132659 0.0028477
P5 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P5)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##        "ab"         "a"         "b"         "a"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_two_total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = All.ID, y = twenty.two.OH, colour = Condition)) 
twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + scale_color_manual(values = c("blue","red"))
twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + ylab("C22:0-OH in total fatty alcohol, ug/mg dry weight") + xlab("")
twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_two_total_fatty_alcoh

twenty_two_total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = Genotype, y = twenty.two.OH, colour = Genotype)) 
twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + facet_grid(~Condition, scales = "free_y")

twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + ylab("C22:0-OH in total fatty alcohol, ug/mg dry weight") + xlab("")+ rremove("legend")
twenty_two_total_fatty_alcoh <- twenty_two_total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_two_total_fatty_alcoh

aov(twenty.four.OH ~ All.ID, data = Fatty_Alcohol)
## Call:
##    aov(formula = twenty.four.OH ~ All.ID, data = Fatty_Alcohol)
## 
## Terms:
##                       All.ID    Residuals
## Sum of Squares  0.0006769876 0.0005038302
## Deg. of Freedom            3           16
## 
## Residual standard error: 0.00561154
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty.four.OH ~ All.ID, data = Fatty_Alcohol))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty.four.OH ~ All.ID, data = Fatty_Alcohol)
## 
## $All.ID
##                                  diff          lwr          upr     p adj
## Col_Salt-Col_control     0.0133940758  0.003240174  0.023547978 0.0081159
## duf_control-Col_control -0.0004914028 -0.010645305  0.009662499 0.9990112
## duf_Salt-Col_control     0.0081734904 -0.001980412  0.018327392 0.1388573
## duf_control-Col_Salt    -0.0138854786 -0.024039381 -0.003731577 0.0061229
## duf_Salt-Col_Salt       -0.0052205854 -0.015374487  0.004933317 0.4765351
## duf_Salt-duf_control     0.0086648932 -0.001489009  0.018818795 0.1089081
P6 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P6)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "b"        "ab"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_four_total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = All.ID, y = twenty.four.OH, colour = Condition)) 
twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + scale_color_manual(values = c("blue","red"))
twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + ylab("C24:0-OH in total fatty alcohol, ug/mg dry weight") + xlab("")
twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_four_total_fatty_alcoh

twenty_four_total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = Genotype, y = twenty.four.OH, colour = Genotype)) 
twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + facet_grid(~Condition, scales = "free_y")

twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + ylab("C24:0-OH in total fatty alcohol, ug/mg dry weight") + xlab("")+ rremove("legend")
twenty_four_total_fatty_alcoh <- twenty_four_total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_four_total_fatty_alcoh

aov(second.twenty.four.OH ~ All.ID, data = Fatty_Alcohol)
## Call:
##    aov(formula = second.twenty.four.OH ~ All.ID, data = Fatty_Alcohol)
## 
## Terms:
##                      All.ID   Residuals
## Sum of Squares  0.002998991 0.011496710
## Deg. of Freedom           3          16
## 
## Residual standard error: 0.02680568
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(second.twenty.four.OH ~ All.ID, data = Fatty_Alcohol))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = second.twenty.four.OH ~ All.ID, data = Fatty_Alcohol)
## 
## $All.ID
##                                 diff         lwr        upr     p adj
## Col_Salt-Col_control     0.011247093 -0.03725692 0.05975110 0.9092482
## duf_control-Col_control  0.008802267 -0.03970174 0.05730628 0.9532498
## duf_Salt-Col_control     0.033261642 -0.01524237 0.08176565 0.2427954
## duf_control-Col_Salt    -0.002444826 -0.05094884 0.04605919 0.9988836
## duf_Salt-Col_Salt        0.022014549 -0.02648946 0.07051856 0.5768715
## duf_Salt-duf_control     0.024459375 -0.02404464 0.07296339 0.4925665
P7 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P7)
stat.test
## $Letters
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "a"         "a"         "a" 
## 
## $LetterMatrix
##                a
## Col_Salt    TRUE
## duf_control TRUE
## duf_Salt    TRUE
## Col_control TRUE
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
second_twenty_four_total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = All.ID, y = second.twenty.four.OH, colour = Condition)) 
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + scale_color_manual(values = c("blue","red"))
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + ylab("2OH-C24:0 in total fatty alcohol, ug/mg dry weight") + xlab("")
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
second_twenty_four_total_fatty_alcoh

second_twenty_four_total_fatty_alcoh <- ggplot(data = Fatty_Alcohol, mapping = aes(x = Genotype, y = second.twenty.four.OH, colour = Genotype)) 
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + facet_grid(~ Condition, scales = "free_y")
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + scale_color_manual(values = c("blue","red"))+ stat_compare_means(method = "t.test", ref.group = "Col")
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + ylab("2OH-C24:0 in total fatty alcohol, ug/mg dry weight") + xlab("")+rremove("legend")
second_twenty_four_total_fatty_alcoh <- second_twenty_four_total_fatty_alcoh + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
second_twenty_four_total_fatty_alcoh

# for fatty alcohol, total fatty alcohol is increased for duf-5 under salt compared to Col. Almost all monomers were increased in duf-5 under the salt (C18, C20, C22, and 2OH-C24)except for C24:0-OH which decreased. there is almost no changes between col and duf-5 in terms of fatty alcohol under control condition.

#Hydroxy_Fatty_Acid=HFA 

HFA<- read.csv("HydroxyFattyAcid.csv")
HFA
colnames(HFA)[3]<-"sixteen.HFA"
colnames(HFA)[4]<-"eighteen.HFA"
colnames(HFA)[5]<-"twenty.HFA"
colnames(HFA)[6]<-"twenty.two.HFA"
colnames(HFA)[7]<-"twenty.four.HFA"
HFA
HFA$All.ID<-paste(HFA$Genotype,HFA$Condition, sep="_")
HFA
aov(total.wOH ~ All.ID, data = HFA)
## Call:
##    aov(formula = total.wOH ~ All.ID, data = HFA)
## 
## Terms:
##                    All.ID Residuals
## Sum of Squares  0.9353318 1.8853386
## Deg. of Freedom         3        16
## 
## Residual standard error: 0.3432691
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(total.wOH ~ All.ID, data = HFA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = total.wOH ~ All.ID, data = HFA)
## 
## $All.ID
##                                diff         lwr       upr     p adj
## Col_Salt-Col_control    -0.03200793 -0.65314228 0.5891264 0.9988076
## duf_control-Col_control  0.07115321 -0.54998114 0.6922876 0.9873864
## duf_Salt-Col_control     0.50496963 -0.11616472 1.1261040 0.1334549
## duf_control-Col_Salt     0.10316114 -0.51797322 0.7242955 0.9634843
## duf_Salt-Col_Salt        0.53697756 -0.08415679 1.1581119 0.1028620
## duf_Salt-duf_control     0.43381642 -0.18731793 1.0549508 0.2294887
P8 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P8)
stat.test
## $Letters
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "a"         "a"         "a" 
## 
## $LetterMatrix
##                a
## Col_Salt    TRUE
## duf_control TRUE
## duf_Salt    TRUE
## Col_control TRUE
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
total_HFA <- ggplot(data = HFA, mapping = aes(x = All.ID, y = total.wOH, colour = Condition)) 
total_HFA <- total_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

total_HFA <- total_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
total_HFA <- total_HFA + scale_color_manual(values = c("blue","red"))
total_HFA <- total_HFA + ylab("Total hydroxy fatty acid, ug/mg dry weight") + xlab("")
total_HFA <- total_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
total_HFA <- total_HFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
total_HFA

total_HFA_final <- ggplot(data = HFA, mapping = aes(x = Genotype, y = total.wOH, colour = Genotype)) 
total_HFA_final <- total_HFA_final + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
total_HFA_final <- total_HFA_final + facet_grid(~Condition, scales = "free_y")

total_HFA_final <- total_HFA_final + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
total_HFA_final <- total_HFA_final + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
total_HFA_final <- total_HFA_final + ylab("Total hydroxy fatty acid, ug/mg dry weight") + xlab("")+ rremove("legend")
total_HFA_final <- total_HFA_final + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
total_HFA_final

aov(sixteen.HFA ~ All.ID, data = HFA)
## Call:
##    aov(formula = sixteen.HFA ~ All.ID, data = HFA)
## 
## Terms:
##                    All.ID Residuals
## Sum of Squares  0.2405473 0.4753321
## Deg. of Freedom         3        16
## 
## Residual standard error: 0.1723608
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(sixteen.HFA ~ All.ID, data = HFA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = sixteen.HFA ~ All.ID, data = HFA)
## 
## $All.ID
##                                diff         lwr       upr     p adj
## Col_Salt-Col_control    -0.02785068 -0.33973205 0.2840307 0.9939131
## duf_control-Col_control  0.08007275 -0.23180862 0.3919541 0.8817812
## duf_Salt-Col_control     0.25357455 -0.05830682 0.5654559 0.1334096
## duf_control-Col_Salt     0.10792344 -0.20395793 0.4198048 0.7570790
## duf_Salt-Col_Salt        0.28142524 -0.03045613 0.5933066 0.0845057
## duf_Salt-duf_control     0.17350180 -0.13837957 0.4853832 0.4106269
P3 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P3)
stat.test
## $Letters
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "a"         "a"         "a" 
## 
## $LetterMatrix
##                a
## Col_Salt    TRUE
## duf_control TRUE
## duf_Salt    TRUE
## Col_control TRUE
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
sixteen_HFA <- ggplot(data = HFA, mapping = aes(x = All.ID, y = sixteen.HFA, colour = Condition)) 
sixteen_HFA <- sixteen_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

sixteen_HFA <- sixteen_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
sixteen_HFA <- sixteen_HFA + scale_color_manual(values = c("blue","red"))
sixteen_HFA <- sixteen_HFA + ylab("C16:0-wOH in total hydroxy fatty acid, ug/mg dry weight") + xlab("")
sixteen_HFA <- sixteen_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
sixteen_HFA <- sixteen_HFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
sixteen_HFA

sixteen_HFA <- ggplot(data = HFA, mapping = aes(x = Genotype, y = sixteen.HFA, colour = Genotype)) 
sixteen_HFA <- sixteen_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
sixteen_HFA <- sixteen_HFA + facet_grid(~Condition, scales = "free_y")

sixteen_HFA <- sixteen_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
sixteen_HFA <- sixteen_HFA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
sixteen_HFA <- sixteen_HFA + ylab("C16:0-wOH in total hydroxy fatty acid, ug/mg dry weight") + xlab("")
sixteen_HFA <- sixteen_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
sixteen_HFA

aov(eighteen.HFA ~ All.ID, data = HFA)
## Call:
##    aov(formula = eighteen.HFA ~ All.ID, data = HFA)
## 
## Terms:
##                      All.ID   Residuals
## Sum of Squares  0.002914193 0.024675625
## Deg. of Freedom           3          16
## 
## Residual standard error: 0.03927119
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(eighteen.HFA ~ All.ID, data = HFA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = eighteen.HFA ~ All.ID, data = HFA)
## 
## $All.ID
##                                 diff         lwr        upr     p adj
## Col_Salt-Col_control    -0.022039942 -0.09309990 0.04902002 0.8114021
## duf_control-Col_control -0.013705898 -0.08476586 0.05735406 0.9447020
## duf_Salt-Col_control     0.009223931 -0.06183603 0.08028389 0.9818910
## duf_control-Col_Salt     0.008334044 -0.06272591 0.07939400 0.9864943
## duf_Salt-Col_Salt        0.031263873 -0.03979609 0.10232383 0.6005600
## duf_Salt-duf_control     0.022929829 -0.04813013 0.09398979 0.7929852
P4 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P4)
stat.test
## $Letters
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "a"         "a"         "a" 
## 
## $LetterMatrix
##                a
## Col_Salt    TRUE
## duf_control TRUE
## duf_Salt    TRUE
## Col_control TRUE
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
eighteen_HFA <- ggplot(data = HFA, mapping = aes(x = All.ID, y = eighteen.HFA, colour = Condition)) 
eighteen_HFA <- eighteen_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

eighteen_HFA <- eighteen_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
eighteen_HFA <- eighteen_HFA + scale_color_manual(values = c("blue","red"))
eighteen_HFA <- eighteen_HFA + ylab("C18:0-wOH in total hydroxy fatty acid, ug/mg dry weight") + xlab("")
eighteen_HFA <- eighteen_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
eighteen_HFA <- eighteen_HFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
eighteen_HFA

eighteen_HFA <- ggplot(data = HFA, mapping = aes(x = Genotype, y = eighteen.HFA, colour = Genotype)) 
eighteen_HFA <- eighteen_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
eighteen_HFA <- eighteen_HFA + facet_grid(~Condition, scales = "free_y")

eighteen_HFA <- eighteen_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
eighteen_HFA <- eighteen_HFA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
eighteen_HFA <- eighteen_HFA + ylab("C18:0-wOH in total hydroxy fatty acid, ug/mg dry weight") + xlab("")
eighteen_HFA <- eighteen_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
eighteen_HFA

aov(twenty.HFA ~ All.ID, data = HFA)
## Call:
##    aov(formula = twenty.HFA ~ All.ID, data = HFA)
## 
## Terms:
##                      All.ID   Residuals
## Sum of Squares  0.006902880 0.004495092
## Deg. of Freedom           3          16
## 
## Residual standard error: 0.01676136
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty.HFA ~ All.ID, data = HFA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty.HFA ~ All.ID, data = HFA)
## 
## $All.ID
##                                  diff          lwr        upr     p adj
## Col_Salt-Col_control     0.0060922744 -0.024236870 0.03642142 0.9382169
## duf_control-Col_control  0.0001503874 -0.030178757 0.03047953 0.9999989
## duf_Salt-Col_control     0.0446081774  0.014279033 0.07493732 0.0033547
## duf_control-Col_Salt    -0.0059418870 -0.036271032 0.02438726 0.9422873
## duf_Salt-Col_Salt        0.0385159030  0.008186758 0.06884505 0.0107997
## duf_Salt-duf_control     0.0444577900  0.014128645 0.07478693 0.0034529
P5 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P5)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "a"         "b"         "a"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_HFA <- ggplot(data = HFA, mapping = aes(x = All.ID, y = twenty.HFA, colour = Condition)) 
twenty_HFA <- twenty_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_HFA <- twenty_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_HFA <- twenty_HFA + scale_color_manual(values = c("blue","red"))
twenty_HFA <- twenty_HFA + ylab("C20:0-wOH in total hydroxy fatty acid, ug/mg dry weight") + xlab("")
twenty_HFA <- twenty_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_HFA <- twenty_HFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_HFA

twenty_HFA <- ggplot(data = HFA, mapping = aes(x = Genotype, y = twenty.HFA, colour = Genotype)) 
twenty_HFA <- twenty_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
twenty_HFA <- twenty_HFA + facet_grid(~Condition, scales = "free_y")
twenty_HFA <- twenty_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_HFA <- twenty_HFA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group="Col")
twenty_HFA <- twenty_HFA + ylab("C20:0-wOH in total hydroxy fatty acid, ug/mg dry weight") + xlab("")
twenty_HFA <- twenty_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_HFA

aov(twenty.two.HFA ~ All.ID, data = HFA)
## Call:
##    aov(formula = twenty.two.HFA ~ All.ID, data = HFA)
## 
## Terms:
##                     All.ID  Residuals
## Sum of Squares  0.09120194 0.25774772
## Deg. of Freedom          3         16
## 
## Residual standard error: 0.1269222
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty.two.HFA ~ All.ID, data = HFA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty.two.HFA ~ All.ID, data = HFA)
## 
## $All.ID
##                                 diff         lwr       upr     p adj
## Col_Salt-Col_control    -0.013244318 -0.24290587 0.2164172 0.9983328
## duf_control-Col_control  0.001009182 -0.22865237 0.2306707 0.9999993
## duf_Salt-Col_control     0.151330148 -0.07833140 0.3809917 0.2728216
## duf_control-Col_Salt     0.014253500 -0.21540805 0.2439150 0.9979259
## duf_Salt-Col_Salt        0.164574465 -0.06508708 0.3942360 0.2113895
## duf_Salt-duf_control     0.150320965 -0.07934058 0.3799825 0.2779833
P6 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P6)
stat.test
## $Letters
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "a"         "a"         "a" 
## 
## $LetterMatrix
##                a
## Col_Salt    TRUE
## duf_control TRUE
## duf_Salt    TRUE
## Col_control TRUE
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_two_HFA <- ggplot(data = HFA, mapping = aes(x = All.ID, y = twenty.two.HFA, colour = Condition)) 
twenty_two_HFA <- twenty_two_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_two_HFA <- twenty_two_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_two_HFA <- twenty_two_HFA + scale_color_manual(values = c("blue","red"))
twenty_two_HFA <- twenty_two_HFA + ylab("C22:0-wOH in total hydroxy fatty acid, ug/mg dry weight") + xlab("")
twenty_two_HFA <- twenty_two_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_two_HFA <- twenty_two_HFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_two_HFA

twenty_two_HFA <- ggplot(data = HFA, mapping = aes(x = Genotype, y = twenty.two.HFA, colour = Genotype)) 
twenty_two_HFA <- twenty_two_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
twenty_two_HFA <- twenty_two_HFA + facet_grid(~Condition, scales = "free_y")
twenty_two_HFA <- twenty_two_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_two_HFA <- twenty_two_HFA + scale_color_manual(values = c("blue","red"))+stat_compare_means(method = "t.test", ref.group = "Col")
twenty_two_HFA <- twenty_two_HFA + ylab("C22:0-wOH in total hydroxy fatty acid, ug/mg dry weight") + xlab("")
twenty_two_HFA <- twenty_two_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_two_HFA

aov(twenty.four.HFA ~ All.ID, data = HFA)
## Call:
##    aov(formula = twenty.four.HFA ~ All.ID, data = HFA)
## 
## Terms:
##                      All.ID   Residuals
## Sum of Squares  0.006875375 0.008742498
## Deg. of Freedom           3          16
## 
## Residual standard error: 0.02337533
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty.four.HFA ~ All.ID, data = HFA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty.four.HFA ~ All.ID, data = HFA)
## 
## $All.ID
##                                 diff           lwr        upr     p adj
## Col_Salt-Col_control     0.025034743 -0.0172621676 0.06733165 0.3589068
## duf_control-Col_control  0.003626787 -0.0386701236 0.04592370 0.9945991
## duf_Salt-Col_control     0.046232826  0.0039359162 0.08852974 0.0297806
## duf_control-Col_Salt    -0.021407956 -0.0637048662 0.02088895 0.4895307
## duf_Salt-Col_Salt        0.021198084 -0.0210988264 0.06349499 0.4976446
## duf_Salt-duf_control     0.042606040  0.0003091296 0.08490295 0.0480328
P7 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P7)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##        "ab"         "a"         "b"         "a"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_four_HFA <- ggplot(data = HFA, mapping = aes(x = All.ID, y = twenty.four.HFA, colour = Condition)) 
twenty_four_HFA <- twenty_four_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_four_HFA <- twenty_four_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_four_HFA <- twenty_four_HFA + scale_color_manual(values = c("blue","red"))
twenty_four_HFA <- twenty_four_HFA + ylab("C24:0-wOH in total hydroxy fatty acid, ug/mg dry weight") + xlab("")
twenty_four_HFA <- twenty_four_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_four_HFA <- twenty_four_HFA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_four_HFA

twenty_four_HFA <- ggplot(data = HFA, mapping = aes(x = Genotype, y = twenty.four.HFA, colour = Genotype)) 
twenty_four_HFA <- twenty_four_HFA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
twenty_four_HFA <- twenty_four_HFA + facet_grid(~Condition, scales = "free_y")

twenty_four_HFA <- twenty_four_HFA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_four_HFA <- twenty_four_HFA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
twenty_four_HFA <- twenty_four_HFA + ylab("C24:0-wOH in total hydroxy fatty acid, ug/mg dry weight") + xlab("")
twenty_four_HFA <- twenty_four_HFA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_four_HFA

## Again, there is an increase in the VLCFA monomers corresponding to the hydroxy fatty acid (HFA) for duf-5 mutant under salt.

DCA<- read.csv("DicarboxylicAcids.csv")
DCA
colnames(DCA)[3]<-"sixteen.DCA"
colnames(DCA)[4]<-"eighteen.DCA"
colnames(DCA)[5]<-"twenty.DCA"
colnames(DCA)[6]<-"twenty.two.DCA"
DCA
DCA$All.ID<-paste(DCA$Genotype,DCA$Condition, sep="_")
DCA
aov(sixteen.DCA ~ All.ID, data = DCA)
## Call:
##    aov(formula = sixteen.DCA ~ All.ID, data = DCA)
## 
## Terms:
##                    All.ID Residuals
## Sum of Squares  0.2223829 0.1438017
## Deg. of Freedom         3        16
## 
## Residual standard error: 0.09480297
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(sixteen.DCA ~ All.ID, data = DCA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = sixteen.DCA ~ All.ID, data = DCA)
## 
## $All.ID
##                                diff         lwr       upr     p adj
## Col_Salt-Col_control     0.04605510 -0.12548782 0.2175980 0.8675810
## duf_control-Col_control  0.25578172  0.08423879 0.4273246 0.0029818
## duf_Salt-Col_control     0.19926795  0.02772503 0.3708109 0.0201673
## duf_control-Col_Salt     0.20972661  0.03818369 0.3812695 0.0142038
## duf_Salt-Col_Salt        0.15321285 -0.01833008 0.3247558 0.0886778
## duf_Salt-duf_control    -0.05651376 -0.22805669 0.1150292 0.7827879
P3 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P3)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##        "ab"         "c"        "ac"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
sixteen_DCA <- ggplot(data = DCA, mapping = aes(x = All.ID, y = sixteen.DCA, colour = Condition)) 
sixteen_DCA <- sixteen_DCA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

sixteen_DCA <- sixteen_DCA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
sixteen_DCA <- sixteen_DCA + scale_color_manual(values = c("blue","red"))
sixteen_DCA <- sixteen_DCA + ylab("C16:0-DCA in total dicarboxylic acids, ug/mg dry weight") + xlab("")
sixteen_DCA <- sixteen_DCA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
sixteen_DCA <- sixteen_DCA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
sixteen_DCA

sixteen_DCA <- ggplot(data = DCA, mapping = aes(x = Genotype, y = sixteen.DCA, colour = Genotype)) 
sixteen_DCA <- sixteen_DCA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
sixteen_DCA <- sixteen_DCA + facet_grid(~Condition, scales = "free_y")

sixteen_DCA <- sixteen_DCA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
sixteen_DCA <- sixteen_DCA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
sixteen_DCA <- sixteen_DCA + ylab("C16:0-DCA in total dicarboxylic acids, ug/mg dry weight") + xlab("")
sixteen_DCA <- sixteen_DCA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
sixteen_DCA

aov(eighteen.DCA ~ All.ID, data = DCA)
## Call:
##    aov(formula = eighteen.DCA ~ All.ID, data = DCA)
## 
## Terms:
##                     All.ID  Residuals
## Sum of Squares  0.01102612 0.01140925
## Deg. of Freedom          3         16
## 
## Residual standard error: 0.02670353
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(eighteen.DCA ~ All.ID, data = DCA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = eighteen.DCA ~ All.ID, data = DCA)
## 
## $All.ID
##                               diff          lwr        upr     p adj
## Col_Salt-Col_control    0.01763546 -0.030683713 0.06595463 0.7267296
## duf_control-Col_control 0.03974209 -0.008577086 0.08806126 0.1272804
## duf_Salt-Col_control    0.06251824  0.014199071 0.11083741 0.0093990
## duf_control-Col_Salt    0.02210663 -0.026212544 0.07042580 0.5706844
## duf_Salt-Col_Salt       0.04488278 -0.003436387 0.09320196 0.0734452
## duf_Salt-duf_control    0.02277616 -0.025543015 0.07109533 0.5472446
P4 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P4)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##        "ab"        "ab"         "a"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
eighteen_DCA <- ggplot(data = DCA, mapping = aes(x = All.ID, y = eighteen.DCA, colour = Condition)) 
eighteen_DCA <- eighteen_DCA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

eighteen_DCA <- eighteen_DCA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
eighteen_DCA <- eighteen_DCA + scale_color_manual(values = c("blue","red"))
eighteen_DCA <- eighteen_DCA + ylab("C18:0-DCA in total dicarboxylic acids, ug/mg dry weight") + xlab("")
eighteen_DCA <- eighteen_DCA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
eighteen_DCA <- eighteen_DCA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
eighteen_DCA

eighteen_DCA <- ggplot(data = DCA, mapping = aes(x = Genotype, y = eighteen.DCA, colour = Genotype)) 
eighteen_DCA <- eighteen_DCA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
eighteen_DCA <- eighteen_DCA + facet_grid(~Condition, scales = "free_y")

eighteen_DCA <- eighteen_DCA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
eighteen_DCA <- eighteen_DCA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
eighteen_DCA <- eighteen_DCA + ylab("C18:0-DCA in total dicarboxylic acids, ug/mg dry weight") + xlab("")
eighteen_DCA <- eighteen_DCA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
eighteen_DCA

aov(twenty.DCA ~ All.ID, data = DCA)
## Call:
##    aov(formula = twenty.DCA ~ All.ID, data = DCA)
## 
## Terms:
##                       All.ID    Residuals
## Sum of Squares  0.0007626785 0.0007453073
## Deg. of Freedom            3           16
## 
## Residual standard error: 0.006825079
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty.DCA ~ All.ID, data = DCA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty.DCA ~ All.ID, data = DCA)
## 
## $All.ID
##                                  diff           lwr         upr     p adj
## Col_Salt-Col_control     0.0117316200 -0.0006181411 0.024081381 0.0656194
## duf_control-Col_control  0.0168873580  0.0045375969 0.029237119 0.0061257
## duf_Salt-Col_control     0.0114500646 -0.0008996965 0.023799826 0.0741253
## duf_control-Col_Salt     0.0051557380 -0.0071940231 0.017505499 0.6389127
## duf_Salt-Col_Salt       -0.0002815554 -0.0126313165 0.012068206 0.9998959
## duf_Salt-duf_control    -0.0054372934 -0.0177870545 0.006912468 0.6000303
P5 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P5)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##        "ab"         "a"        "ab"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_DCA <- ggplot(data = DCA, mapping = aes(x = All.ID, y = twenty.DCA, colour = Condition)) 
twenty_DCA <- twenty_DCA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_DCA <- twenty_DCA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_DCA <- twenty_DCA + scale_color_manual(values = c("blue","red"))
twenty_DCA <- twenty_DCA + ylab("C20:0-DCA in total dicarboxylic acids, ug/mg dry weight") + xlab("")
twenty_DCA <- twenty_DCA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_DCA <- twenty_DCA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_DCA

twenty_DCA <- ggplot(data = DCA, mapping = aes(x = Genotype, y = twenty.DCA, colour = Genotype)) 
twenty_DCA <- twenty_DCA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
twenty_DCA <- twenty_DCA + facet_grid(~Condition, scales = "free_y")

twenty_DCA <- twenty_DCA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_DCA <- twenty_DCA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
twenty_DCA <- twenty_DCA + ylab("C20:0-DCA in total dicarboxylic acids, ug/mg dry weight") + xlab("")
twenty_DCA <- twenty_DCA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_DCA

aov(twenty.two.DCA ~ All.ID, data = DCA)
## Call:
##    aov(formula = twenty.two.DCA ~ All.ID, data = DCA)
## 
## Terms:
##                       All.ID    Residuals
## Sum of Squares  0.0001337626 0.0007454766
## Deg. of Freedom            3           16
## 
## Residual standard error: 0.006825854
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(twenty.two.DCA ~ All.ID, data = DCA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = twenty.two.DCA ~ All.ID, data = DCA)
## 
## $All.ID
##                                 diff          lwr         upr     p adj
## Col_Salt-Col_control     0.001474437 -0.010876727 0.013825601 0.9857829
## duf_control-Col_control  0.006794656 -0.005556508 0.019145819 0.4200037
## duf_Salt-Col_control     0.004023300 -0.008327864 0.016374464 0.7883915
## duf_control-Col_Salt     0.005320218 -0.007030945 0.017671382 0.6162899
## duf_Salt-Col_Salt        0.002548863 -0.009802301 0.014900027 0.9335243
## duf_Salt-duf_control    -0.002771355 -0.015122519 0.009579808 0.9168043
P6 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P6)
stat.test
## $Letters
##    Col_Salt duf_control    duf_Salt Col_control 
##         "a"         "a"         "a"         "a" 
## 
## $LetterMatrix
##                a
## Col_Salt    TRUE
## duf_control TRUE
## duf_Salt    TRUE
## Col_control TRUE
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
twenty_two_DCA <- ggplot(data = DCA, mapping = aes(x = All.ID, y = twenty.two.DCA, colour = Condition)) 
twenty_two_DCA <- twenty_two_DCA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

twenty_two_DCA <- twenty_two_DCA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_two_DCA <- twenty_two_DCA + scale_color_manual(values = c("blue","red"))
twenty_two_DCA <- twenty_two_DCA + ylab("C22:0-DCA in total dicarboxylic acids, ug/mg dry weight") + xlab("")
twenty_two_DCA <- twenty_two_DCA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_two_DCA <- twenty_two_DCA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
twenty_two_DCA

twenty_two_DCA <- ggplot(data = DCA, mapping = aes(x = Genotype, y = twenty.two.DCA, colour = Genotype)) 
twenty_two_DCA <- twenty_two_DCA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
twenty_two_DCA <- twenty_two_DCA + facet_grid(~Condition, scales = "free_y")

twenty_two_DCA <- twenty_two_DCA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
twenty_two_DCA <- twenty_two_DCA + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
twenty_two_DCA <- twenty_two_DCA + ylab("C22:0-DCA in total dicarboxylic acids, ug/mg dry weight") + xlab("")
twenty_two_DCA <- twenty_two_DCA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
twenty_two_DCA

aov(total.DCA ~ All.ID, data = DCA)
## Call:
##    aov(formula = total.DCA ~ All.ID, data = DCA)
## 
## Terms:
##                    All.ID Residuals
## Sum of Squares  0.3566211 0.2392476
## Deg. of Freedom         3        16
## 
## Residual standard error: 0.1222824
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(total.DCA ~ All.ID, data = DCA))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = total.DCA ~ All.ID, data = DCA)
## 
## $All.ID
##                                diff         lwr       upr     p adj
## Col_Salt-Col_control     0.07689662 -0.14436937 0.2981626 0.7547272
## duf_control-Col_control  0.31920582  0.09793982 0.5404718 0.0039524
## duf_Salt-Col_control     0.27725956  0.05599357 0.4985256 0.0119091
## duf_control-Col_Salt     0.24230920  0.02104320 0.4635752 0.0294382
## duf_Salt-Col_Salt        0.20036294 -0.02090305 0.4216289 0.0831041
## duf_Salt-duf_control    -0.04194626 -0.26321225 0.1793197 0.9472637
P7 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P7)
stat.test
##    Col_Salt duf_control    duf_Salt Col_control 
##        "ab"         "c"        "ac"         "b"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
total_DCA <- ggplot(data = DCA, mapping = aes(x = All.ID, y = total.DCA, colour = Condition)) 
total_DCA <- total_DCA + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

total_DCA <- total_DCA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
total_DCA <- total_DCA + scale_color_manual(values = c("blue","red"))
total_DCA <- total_DCA + ylab("Total dicarboxylic acids, ug/mg dry weight") + xlab("")
total_DCA <- total_DCA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
total_DCA <- total_DCA + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
total_DCA

total_DCA_final <- ggplot(data = DCA, mapping = aes(x = Genotype, y = total.DCA, colour = Genotype)) 
total_DCA_final <- total_DCA_final + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
total_DCA_final <- total_DCA_final + facet_grid(~Condition, scales = "free_y")

total_DCA_final <- total_DCA_final + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
total_DCA_final <- total_DCA_final + scale_color_manual(values = c("blue","red")) + stat_compare_means(method = "t.test", ref.group = "Col")
total_DCA_final <- total_DCA_final + ylab("Total dicarboxylic acids, ug/mg dry weight") + xlab("") + rremove("legend")
total_DCA_final <- total_DCA_final + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
total_DCA_final

#for DCA, it seems duf-5 has higher total DCA in both control and salt conditions compared to col, and almost there is no changes in the amounts of DCA in duf-5 between control and salt conditions. Especifically, duf-5 is enriched in LCFA (c16 and c18), and the VLCFA either reduces or reamined without any changes in duf-5 mutant…..the pattern is opposit to what we have seen for fatty acids pattern…….

library(cowplot)
## 
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggpubr':
## 
##     get_legend
pdf("Suberin_root_monomers_quantification.pdf", height = 4, width = 13)
plot_grid(totalFA_final, total_fatty_alcoh_final, total_HFA_final, total_DCA_final, ncol = 4)
dev.off()
## png 
##   2

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