ICP-MS on root and shoot of Arabidopsis duf247-5 and col-0 grown on plate containing varoius concentrations of NaCl including 0, 75, and 125 mM NaCl. However, the 125 mM samples were not sent for the analysis because we have not seen any phenotype in RSA of col-0 and duf mutant grown at this concentration.So this is the ICP-MS result for col and duf160-5 root and shoot grown on the 1/2 MS plate +/-75 mM salt. After two weeks salt stress, shoot and root were weighted and harvested for ICP-MS. this is the same batch as RSA analysis for this allele on January 2021.

getwd()
## [1] "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20211004_duf160-5_RSA_correct_genotype"
setwd("C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20211004_duf160-5_RSA_correct_genotype/")
list.files(pattern = ".csv")
## [1] "20211011_duf-5_growth_factors.csv"                                         
## [2] "20211013_duf-5_growth_factors.csv"                                         
## [3] "8-19-21 FW for duf160-5 correct genotype at 0 75 125 salt_2weeksOnSalt.csv"
## [4] "d11_duf160-5_correct_Genotype.csv"                                         
## [5] "d13_duf160-5_correct_Genotype.csv"                                         
## [6] "d5_duf160-5_correct_Genotype.csv"                                          
## [7] "d7_duf160-5_correct_Genotype.csv"                                          
## [8] "d9_duf160-5_correct_Genotype.csv"                                          
## [9] "Eric10282021R1and R2_analyzed v3 for R analysis.csv"
duf_ICP<-read.csv("Eric10282021R1and R2_analyzed v3 for R analysis.csv")
duf_ICP
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.0.5
library(ggpubr)
## Warning: package 'ggpubr' was built under R version 4.0.5
library(multcompView)
## Warning: package 'multcompView' was built under R version 4.0.5
colnames(duf_ICP)
## [1] "ï..All.ID"               "Genotype"               
## [3] "Condition"               "Tissue"                 
## [5] "DW.mg"                   "K.con..mg.mg.dry.weight"
## [7] "Na.con.mg.mg.dry.weight" "Na.K.ratio"
duf_ICP$GenoConTiss <- paste(duf_ICP$Condition,  duf_ICP$Tissue, duf_ICP$Genotype, sep="_")
duf_ICP
aov(Na.con.mg.mg.dry.weight ~ GenoConTiss, data = duf_ICP)
## Call:
##    aov(formula = Na.con.mg.mg.dry.weight ~ GenoConTiss, data = duf_ICP)
## 
## Terms:
##                 GenoConTiss Residuals
## Sum of Squares     7441.536   224.720
## Deg. of Freedom           7        16
## 
## Residual standard error: 3.747668
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(Na.con.mg.mg.dry.weight ~ GenoConTiss, data = duf_ICP))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Na.con.mg.mg.dry.weight ~ GenoConTiss, data = duf_ICP)
## 
## $GenoConTiss
##                                            diff        lwr       upr     p adj
## control_root_duf-control_root_col    0.10033969 -10.493696 10.694376 1.0000000
## control_shoot_col-control_root_col   0.37299364 -10.221042 10.967029 1.0000000
## control_shoot_duf-control_root_col   0.39462602 -10.199410 10.988662 1.0000000
## salt_root_col-control_root_col      17.96392949   7.369894 28.557965 0.0004909
## salt_root_duf-control_root_col      14.46663165   3.872596 25.060667 0.0043333
## salt_shoot_col-control_root_col     42.41290184  31.818866 53.006938 0.0000000
## salt_shoot_duf-control_root_col     44.08117200  33.487136 54.675208 0.0000000
## control_shoot_col-control_root_duf   0.27265395 -10.321382 10.866690 1.0000000
## control_shoot_duf-control_root_duf   0.29428633 -10.299750 10.888322 1.0000000
## salt_root_col-control_root_duf      17.86358980   7.269554 28.457626 0.0005217
## salt_root_duf-control_root_duf      14.36629195   3.772256 24.960328 0.0046187
## salt_shoot_col-control_root_duf     42.31256214  31.718526 52.906598 0.0000000
## salt_shoot_duf-control_root_duf     43.98083230  33.386796 54.574868 0.0000000
## control_shoot_duf-control_shoot_col  0.02163238 -10.572403 10.615668 1.0000000
## salt_root_col-control_shoot_col     17.59093585   6.996900 28.184972 0.0006159
## salt_root_duf-control_shoot_col     14.09363800   3.499602 24.687674 0.0054937
## salt_shoot_col-control_shoot_col    42.03990819  31.445872 52.633944 0.0000000
## salt_shoot_duf-control_shoot_col    43.70817835  33.114143 54.302214 0.0000000
## salt_root_col-control_shoot_duf     17.56930347   6.975268 28.163339 0.0006241
## salt_root_duf-control_shoot_duf     14.07200562   3.477970 24.666041 0.0055699
## salt_shoot_col-control_shoot_duf    42.01827581  31.424240 52.612312 0.0000000
## salt_shoot_duf-control_shoot_duf    43.68654597  33.092510 54.280582 0.0000000
## salt_root_duf-salt_root_col         -3.49729784 -14.091334  7.096738 0.9368946
## salt_shoot_col-salt_root_col        24.44897235  13.854936 35.043008 0.0000126
## salt_shoot_duf-salt_root_col        26.11724251  15.523207 36.711278 0.0000053
## salt_shoot_col-salt_root_duf        27.94627019  17.352234 38.540306 0.0000022
## salt_shoot_duf-salt_root_duf        29.61454035  19.020505 40.208576 0.0000010
## salt_shoot_duf-salt_shoot_col        1.66827016  -8.925766 12.262306 0.9991126
P7 = Output$GenoConTiss[,'p adj']
stat.test<- multcompLetters(P7)
stat.test
##  control_root_duf control_shoot_col control_shoot_duf     salt_root_col 
##               "a"               "a"               "a"               "b" 
##     salt_root_duf    salt_shoot_col    salt_shoot_duf  control_root_col 
##               "b"               "c"               "c"               "a"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
Na_content <- ggplot(data = duf_ICP, mapping = aes(x = GenoConTiss, y = Na.con.mg.mg.dry.weight, colour = Condition)) 
Na_content <- Na_content + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

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

when I did tetest compare to col in excel, duf accumulates less Na+ in root but here there is no difference.

#I have to facet grid condition, 
Na_content <- ggplot(data = duf_ICP, mapping = aes(x = Genotype, y = Na.con.mg.mg.dry.weight, colour = Genotype)) 
Na_content <- Na_content + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

Na_content <- Na_content + facet_grid(Tissue ~ Condition)

Na_content <- Na_content + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Na_content <- Na_content+ scale_color_manual(values= c("blue", "red"))
Na_content <- Na_content + ylab("Na content, mg/mg dry weight") + xlab("")
Na_content <- Na_content + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Na_content <- Na_content + rremove("legend")
#my_box_plot2 <- my_box_plot2 + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
Na_content

aov(K.con..mg.mg.dry.weight ~ GenoConTiss, data = duf_ICP)
## Call:
##    aov(formula = K.con..mg.mg.dry.weight ~ GenoConTiss, data = duf_ICP)
## 
## Terms:
##                 GenoConTiss Residuals
## Sum of Squares     1377.611  2290.678
## Deg. of Freedom           7        16
## 
## Residual standard error: 11.96526
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(K.con..mg.mg.dry.weight ~ GenoConTiss, data = duf_ICP))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K.con..mg.mg.dry.weight ~ GenoConTiss, data = duf_ICP)
## 
## $GenoConTiss
##                                            diff       lwr      upr     p adj
## control_root_duf-control_root_col     0.7099204 -33.11388 34.53372 1.0000000
## control_shoot_col-control_root_col  -15.2225373 -49.04634 18.60126 0.7667731
## control_shoot_duf-control_root_col  -19.7048391 -53.52864 14.11896 0.5009242
## salt_root_col-control_root_col      -12.1794505 -46.00325 21.64435 0.9053795
## salt_root_duf-control_root_col      -18.6910846 -52.51488 15.13271 0.5615436
## salt_shoot_col-control_root_col     -16.5657970 -50.38960 17.25800 0.6900430
## salt_shoot_duf-control_root_col     -16.3307343 -50.15453 17.49307 0.7038912
## control_shoot_col-control_root_duf  -15.9324578 -49.75626 17.89134 0.7269969
## control_shoot_duf-control_root_duf  -20.4147595 -54.23856 13.40904 0.4597816
## salt_root_col-control_root_duf      -12.8893710 -46.71317 20.93443 0.8786539
## salt_root_duf-control_root_duf      -19.4010051 -53.22480 14.42279 0.5189043
## salt_shoot_col-control_root_duf     -17.2757174 -51.09952 16.54808 0.6475203
## salt_shoot_duf-control_root_duf     -17.0406547 -50.86445 16.78314 0.6616943
## control_shoot_duf-control_shoot_col  -4.4823017 -38.30610 29.34150 0.9997108
## salt_root_col-control_shoot_col       3.0430868 -30.78071 36.86689 0.9999784
## salt_root_duf-control_shoot_col      -3.4685473 -37.29235 30.35525 0.9999476
## salt_shoot_col-control_shoot_col     -1.3432596 -35.16706 32.48054 0.9999999
## salt_shoot_duf-control_shoot_col     -1.1081970 -34.93200 32.71560 1.0000000
## salt_root_col-control_shoot_duf       7.5253885 -26.29841 41.34919 0.9925058
## salt_root_duf-control_shoot_duf       1.0137544 -32.81004 34.83755 1.0000000
## salt_shoot_col-control_shoot_duf      3.1390421 -30.68476 36.96284 0.9999733
## salt_shoot_duf-control_shoot_duf      3.3741047 -30.44969 37.19790 0.9999565
## salt_root_duf-salt_root_col          -6.5116341 -40.33543 27.31217 0.9968630
## salt_shoot_col-salt_root_col         -4.3863465 -38.21015 29.43745 0.9997492
## salt_shoot_duf-salt_root_col         -4.1512838 -37.97508 29.67252 0.9998258
## salt_shoot_col-salt_root_duf          2.1252876 -31.69851 35.94909 0.9999982
## salt_shoot_duf-salt_root_duf          2.3603503 -31.46345 36.18415 0.9999962
## salt_shoot_duf-salt_shoot_col         0.2350627 -33.58874 34.05886 1.0000000
P6 = Output$GenoConTiss[,'p adj']
stat.test<- multcompLetters(P6)
stat.test
## $Letters
##  control_root_duf control_shoot_col control_shoot_duf     salt_root_col 
##               "a"               "a"               "a"               "a" 
##     salt_root_duf    salt_shoot_col    salt_shoot_duf  control_root_col 
##               "a"               "a"               "a"               "a" 
## 
## $LetterMatrix
##                      a
## control_root_duf  TRUE
## control_shoot_col TRUE
## control_shoot_duf TRUE
## salt_root_col     TRUE
## salt_root_duf     TRUE
## salt_shoot_col    TRUE
## salt_shoot_duf    TRUE
## control_root_col  TRUE
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
k_content <- ggplot(data = duf_ICP, mapping = aes(x = GenoConTiss, y = K.con..mg.mg.dry.weight, colour = Condition)) 
k_content <- k_content + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

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

k_content <- ggplot(data = duf_ICP, mapping = aes(x = Genotype, y = K.con..mg.mg.dry.weight, colour = Genotype)) 
k_content <- k_content + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

k_content <- k_content + facet_grid(Tissue ~ Condition)

k_content <- k_content + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
k_content <- k_content + scale_color_manual(values= c("blue", "red"))
k_content <- k_content + ylab("K content, mg/mg dry weight") + xlab("")
k_content <- k_content + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
k_content <- k_content + rremove("legend")
#my_box_plot2 <- my_box_plot2 + stat_pvalue_manual(test, label = "Tukey", y.position = 80)
k_content

aov(Na.K.ratio ~ GenoConTiss, data = duf_ICP)
## Call:
##    aov(formula = Na.K.ratio ~ GenoConTiss, data = duf_ICP)
## 
## Terms:
##                 GenoConTiss Residuals
## Sum of Squares    2.5296248 0.0739099
## Deg. of Freedom           7        16
## 
## Residual standard error: 0.06796596
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(Na.K.ratio ~ GenoConTiss, data = duf_ICP))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Na.K.ratio ~ GenoConTiss, data = duf_ICP)
## 
## $GenoConTiss
##                                             diff         lwr       upr
## control_root_duf-control_root_col    0.001336107 -0.19079238 0.1934646
## control_shoot_col-control_root_col   0.011596602 -0.18053189 0.2037251
## control_shoot_duf-control_root_col   0.013071667 -0.17905682 0.2052002
## salt_root_col-control_root_col       0.308294440  0.11616595 0.5004229
## salt_root_duf-control_root_col       0.279999840  0.08787135 0.4721283
## salt_shoot_col-control_root_col      0.779478922  0.58735043 0.9716074
## salt_shoot_duf-control_root_col      0.821901962  0.62977347 1.0140305
## control_shoot_col-control_root_duf   0.010260494 -0.18186799 0.2023890
## control_shoot_duf-control_root_duf   0.011735560 -0.18039293 0.2038640
## salt_root_col-control_root_duf       0.306958333  0.11482984 0.4990868
## salt_root_duf-control_root_duf       0.278663733  0.08653524 0.4707922
## salt_shoot_col-control_root_duf      0.778142814  0.58601433 0.9702713
## salt_shoot_duf-control_root_duf      0.820565855  0.62843737 1.0126943
## control_shoot_duf-control_shoot_col  0.001475066 -0.19065342 0.1936036
## salt_root_col-control_shoot_col      0.296697838  0.10456935 0.4888263
## salt_root_duf-control_shoot_col      0.268403238  0.07627475 0.4605317
## salt_shoot_col-control_shoot_col     0.767882320  0.57575383 0.9600108
## salt_shoot_duf-control_shoot_col     0.810305361  0.61817687 1.0024338
## salt_root_col-control_shoot_duf      0.295222773  0.10309428 0.4873513
## salt_root_duf-control_shoot_duf      0.266928173  0.07479968 0.4590567
## salt_shoot_col-control_shoot_duf     0.766407254  0.57427877 0.9585357
## salt_shoot_duf-control_shoot_duf     0.808830295  0.61670181 1.0009588
## salt_root_duf-salt_root_col         -0.028294600 -0.22042309 0.1638339
## salt_shoot_col-salt_root_col         0.471184482  0.27905599 0.6633130
## salt_shoot_duf-salt_root_col         0.513607522  0.32147903 0.7057360
## salt_shoot_col-salt_root_duf         0.499479082  0.30735059 0.6916076
## salt_shoot_duf-salt_root_duf         0.541902122  0.34977363 0.7340306
## salt_shoot_duf-salt_shoot_col        0.042423041 -0.14970545 0.2345515
##                                         p adj
## control_root_duf-control_root_col   1.0000000
## control_shoot_col-control_root_col  0.9999986
## control_shoot_duf-control_root_col  0.9999968
## salt_root_col-control_root_col      0.0008851
## salt_root_duf-control_root_col      0.0023414
## salt_shoot_col-control_root_col     0.0000000
## salt_shoot_duf-control_root_col     0.0000000
## control_shoot_col-control_root_duf  0.9999994
## control_shoot_duf-control_root_duf  0.9999985
## salt_root_col-control_root_duf      0.0009263
## salt_root_duf-control_root_duf      0.0024526
## salt_shoot_col-control_root_duf     0.0000000
## salt_shoot_duf-control_root_duf     0.0000000
## control_shoot_duf-control_shoot_col 1.0000000
## salt_root_col-control_shoot_col     0.0013153
## salt_root_duf-control_shoot_col     0.0035073
## salt_shoot_col-control_shoot_col    0.0000000
## salt_shoot_duf-control_shoot_col    0.0000000
## salt_root_col-control_shoot_duf     0.0013836
## salt_root_duf-control_shoot_duf     0.0036929
## salt_shoot_col-control_shoot_duf    0.0000000
## salt_shoot_duf-control_shoot_duf    0.0000000
## salt_root_duf-salt_root_col         0.9994238
## salt_shoot_col-salt_root_col        0.0000057
## salt_shoot_duf-salt_root_col        0.0000018
## salt_shoot_col-salt_root_duf        0.0000026
## salt_shoot_duf-salt_root_duf        0.0000009
## salt_shoot_duf-salt_shoot_col       0.9928336
P8 = Output$GenoConTiss[,'p adj']
stat.test<- multcompLetters(P8)
stat.test
##  control_root_duf control_shoot_col control_shoot_duf     salt_root_col 
##               "a"               "a"               "a"               "b" 
##     salt_root_duf    salt_shoot_col    salt_shoot_duf  control_root_col 
##               "b"               "c"               "c"               "a"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
ratio_content <- ggplot(data = duf_ICP, mapping = aes(x = GenoConTiss, y = Na.K.ratio, colour = Condition)) 
ratio_content <- ratio_content + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

ratio_content <- ratio_content + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
ratio_content <- ratio_content + scale_color_manual(values= c("blue", "red"))
ratio_content <- ratio_content + ylab("Na/K") + xlab("")
ratio_content <- ratio_content + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
ratio_content <- ratio_content + stat_pvalue_manual(test, label = "Tukey", y.position = 10)
ratio_content

ratio_content <- ggplot(data = duf_ICP, mapping = aes(x = Genotype, y = Na.K.ratio, colour = Genotype)) 
ratio_content <- ratio_content + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
ratio_content <- ratio_content + facet_grid(Tissue ~ Condition)
ratio_content <- ratio_content + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
ratio_content <- ratio_content + scale_color_manual(values= c("blue", "red"))
ratio_content <- ratio_content + ylab("Na/K ratio") + xlab("")
ratio_content <- ratio_content + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
ratio_content <- ratio_content + rremove("legend")
ratio_content

#pdf("20220610_Arabidopsis_duf-5_ICP-MS_From_PlateRSA_Graphs.pdf", width = 13, height = 5)
#plot_grid(Na_content , k_content, ratio_content, labels = c("AUTO"), ncol = 3)
#dev.off()

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