These are ICP-MS analysis of the root and shoot samples of the IAA shoot application for LA1511 performed on the plate. LA1511 treated on plate with or without salt. at d5 the seedlings were transferred to +/- salt, while their shoot that are exposed to outside of plates being sprayed +/- IAA for 5 days. after 10 days at salt, seedlings were harvested for ICPMS.

getwd()
## [1] "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20230526_RSA_and _ICP-MS_for_LA1511_IAA_100mM_Salt_shoot_application_d9_only"
list.files(pattern = ".csv")
## [1] "d9-IAA-shoot-spray-root-corrected.csv"
## [2] "Eric06142023NaK-analyzed.csv"
ICP <- read.csv("Eric06142023NaK-analyzed.csv")
ICP
ICP$All.condition<-paste(ICP$Condition,ICP$Condition2, sep="+")
ICP
ICP$All.ID<-paste(ICP$Accession,ICP$All.condition, ICP$Tissue, sep="_")
ICP
library(ggplot2)
library(ggpubr)
library(multcompView)
aov(Na.con.mg.mg.dry.weight ~ All.ID, data = ICP)
## Call:
##    aov(formula = Na.con.mg.mg.dry.weight ~ All.ID, data = ICP)
## 
## Terms:
##                    All.ID Residuals
## Sum of Squares  13162.706   369.429
## Deg. of Freedom        11        24
## 
## Residual standard error: 3.923376
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(Na.con.mg.mg.dry.weight ~ All.ID, data = ICP))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Na.con.mg.mg.dry.weight ~ All.ID, data = ICP)
## 
## $All.ID
##                                      diff        lwr        upr     p adj
## LA_C+IAA_SH-LA_C+IAA_RO       1.457746203 -10.092610 13.0081027 0.9999980
## LA_C+MOCK_RO-LA_C+IAA_RO      0.142860576 -11.407496 11.6932171 1.0000000
## LA_C+MOCK_SH-LA_C+IAA_RO      1.455133676 -10.095223 13.0054902 0.9999980
## LA_C+NOIAA_RO-LA_C+IAA_RO     0.095887811 -11.454469 11.6462443 1.0000000
## LA_C+NOIAA_SH-LA_C+IAA_RO     1.211150999 -10.339206 12.7615075 0.9999997
## LA_S+IAA_RO-LA_C+IAA_RO      42.070721851  30.520365 53.6210784 0.0000000
## LA_S+IAA_SH-LA_C+IAA_RO      35.391659367  23.841303 46.9420159 0.0000000
## LA_S+MOCK_RO-LA_C+IAA_RO     43.391200897  31.840844 54.9415574 0.0000000
## LA_S+MOCK_SH-LA_C+IAA_RO     30.666900991  19.116544 42.2172575 0.0000001
## LA_S+NOIAA_RO-LA_C+IAA_RO    45.556738744  34.006382 57.1070953 0.0000000
## LA_S+NOIAA_SH-LA_C+IAA_RO    31.085944637  19.535588 42.6363011 0.0000001
## LA_C+MOCK_RO-LA_C+IAA_SH     -1.314885627 -12.865242 10.2354709 0.9999993
## LA_C+MOCK_SH-LA_C+IAA_SH     -0.002612527 -11.552969 11.5477440 1.0000000
## LA_C+NOIAA_RO-LA_C+IAA_SH    -1.361858392 -12.912215 10.1884981 0.9999990
## LA_C+NOIAA_SH-LA_C+IAA_SH    -0.246595203 -11.796952 11.3037613 1.0000000
## LA_S+IAA_RO-LA_C+IAA_SH      40.612975648  29.062619 52.1633322 0.0000000
## LA_S+IAA_SH-LA_C+IAA_SH      33.933913165  22.383557 45.4842697 0.0000000
## LA_S+MOCK_RO-LA_C+IAA_SH     41.933454695  30.383098 53.4838112 0.0000000
## LA_S+MOCK_SH-LA_C+IAA_SH     29.209154788  17.658798 40.7595113 0.0000002
## LA_S+NOIAA_RO-LA_C+IAA_SH    44.098992541  32.548636 55.6493491 0.0000000
## LA_S+NOIAA_SH-LA_C+IAA_SH    29.628198435  18.077842 41.1785549 0.0000001
## LA_C+MOCK_SH-LA_C+MOCK_RO     1.312273100 -10.238083 12.8626296 0.9999993
## LA_C+NOIAA_RO-LA_C+MOCK_RO   -0.046972765 -11.597329 11.5033837 1.0000000
## LA_C+NOIAA_SH-LA_C+MOCK_RO    1.068290423 -10.482066 12.6186469 0.9999999
## LA_S+IAA_RO-LA_C+MOCK_RO     41.927861275  30.377505 53.4782178 0.0000000
## LA_S+IAA_SH-LA_C+MOCK_RO     35.248798791  23.698442 46.7991553 0.0000000
## LA_S+MOCK_RO-LA_C+MOCK_RO    43.248340321  31.697984 54.7986968 0.0000000
## LA_S+MOCK_SH-LA_C+MOCK_RO    30.524040415  18.973684 42.0743969 0.0000001
## LA_S+NOIAA_RO-LA_C+MOCK_RO   45.413878168  33.863522 56.9642347 0.0000000
## LA_S+NOIAA_SH-LA_C+MOCK_RO   30.943084061  19.392728 42.4934406 0.0000001
## LA_C+NOIAA_RO-LA_C+MOCK_SH   -1.359245865 -12.909602 10.1911106 0.9999990
## LA_C+NOIAA_SH-LA_C+MOCK_SH   -0.243982677 -11.794339 11.3063738 1.0000000
## LA_S+IAA_RO-LA_C+MOCK_SH     40.615588175  29.065232 52.1659447 0.0000000
## LA_S+IAA_SH-LA_C+MOCK_SH     33.936525691  22.386169 45.4868822 0.0000000
## LA_S+MOCK_RO-LA_C+MOCK_SH    41.936067221  30.385711 53.4864237 0.0000000
## LA_S+MOCK_SH-LA_C+MOCK_SH    29.211767315  17.661411 40.7621238 0.0000002
## LA_S+NOIAA_RO-LA_C+MOCK_SH   44.101605068  32.551249 55.6519616 0.0000000
## LA_S+NOIAA_SH-LA_C+MOCK_SH   29.630810961  18.080454 41.1811675 0.0000001
## LA_C+NOIAA_SH-LA_C+NOIAA_RO   1.115263189 -10.435093 12.6656197 0.9999999
## LA_S+IAA_RO-LA_C+NOIAA_RO    41.974834040  30.424478 53.5251906 0.0000000
## LA_S+IAA_SH-LA_C+NOIAA_RO    35.295771557  23.745415 46.8461281 0.0000000
## LA_S+MOCK_RO-LA_C+NOIAA_RO   43.295313087  31.744957 54.8456696 0.0000000
## LA_S+MOCK_SH-LA_C+NOIAA_RO   30.571013180  19.020657 42.1213697 0.0000001
## LA_S+NOIAA_RO-LA_C+NOIAA_RO  45.460850933  33.910494 57.0112074 0.0000000
## LA_S+NOIAA_SH-LA_C+NOIAA_RO  30.990056827  19.439700 42.5404133 0.0000001
## LA_S+IAA_RO-LA_C+NOIAA_SH    40.859570851  29.309214 52.4099274 0.0000000
## LA_S+IAA_SH-LA_C+NOIAA_SH    34.180508368  22.630152 45.7308649 0.0000000
## LA_S+MOCK_RO-LA_C+NOIAA_SH   42.180049898  30.629693 53.7304064 0.0000000
## LA_S+MOCK_SH-LA_C+NOIAA_SH   29.455749991  17.905393 41.0061065 0.0000001
## LA_S+NOIAA_RO-LA_C+NOIAA_SH  44.345587745  32.795231 55.8959443 0.0000000
## LA_S+NOIAA_SH-LA_C+NOIAA_SH  29.874793638  18.324437 41.4251502 0.0000001
## LA_S+IAA_SH-LA_S+IAA_RO      -6.679062483 -18.229419  4.8712940 0.6368276
## LA_S+MOCK_RO-LA_S+IAA_RO      1.320479047 -10.229877 12.8708356 0.9999993
## LA_S+MOCK_SH-LA_S+IAA_RO    -11.403820860 -22.954177  0.1465357 0.0551546
## LA_S+NOIAA_RO-LA_S+IAA_RO     3.486016893  -8.064340 15.0363734 0.9924041
## LA_S+NOIAA_SH-LA_S+IAA_RO   -10.984777213 -22.535134  0.5655793 0.0726695
## LA_S+MOCK_RO-LA_S+IAA_SH      7.999541530  -3.550815 19.5498980 0.3864810
## LA_S+MOCK_SH-LA_S+IAA_SH     -4.724758377 -16.275115  6.8255981 0.9337081
## LA_S+NOIAA_RO-LA_S+IAA_SH    10.165079377  -1.385277 21.7154359 0.1217195
## LA_S+NOIAA_SH-LA_S+IAA_SH    -4.305714730 -15.856071  7.2446418 0.9637115
## LA_S+MOCK_SH-LA_S+MOCK_RO   -12.724299907 -24.274656 -1.1739434 0.0221936
## LA_S+NOIAA_RO-LA_S+MOCK_RO    2.165537847  -9.384819 13.7158944 0.9998919
## LA_S+NOIAA_SH-LA_S+MOCK_RO  -12.305256260 -23.855613 -0.7548997 0.0298014
## LA_S+NOIAA_RO-LA_S+MOCK_SH   14.889837753   3.339481 26.4401943 0.0045692
## LA_S+NOIAA_SH-LA_S+MOCK_SH    0.419043647 -11.131313 11.9694002 1.0000000
## LA_S+NOIAA_SH-LA_S+NOIAA_RO -14.470794107 -26.021151 -2.9204376 0.0062378
P7 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P7)
stat.test
##   LA_C+IAA_SH  LA_C+MOCK_RO  LA_C+MOCK_SH LA_C+NOIAA_RO LA_C+NOIAA_SH 
##           "a"           "a"           "a"           "a"           "a" 
##   LA_S+IAA_RO   LA_S+IAA_SH  LA_S+MOCK_RO  LA_S+MOCK_SH LA_S+NOIAA_RO 
##          "bc"          "bc"           "b"           "c"           "b" 
## LA_S+NOIAA_SH   LA_C+IAA_RO 
##           "c"           "a"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
ICP$All.condition<- factor(ICP$All.condition, levels=c("C+NOIAA", "C+MOCK", "C+IAA","S+NOIAA", "S+MOCK", "S+IAA"))

Na_content <- ggplot(data = ICP, mapping = aes(x = All.ID, y = Na.con.mg.mg.dry.weight, colour = All.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","blueviolet","cyan", "red", "deeppink", "magenta"))
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 = 120) + rremove("legend")
Na_content 

pdf("Na.accumulation.Iaa.shoot.spray.ICPMS.pdf", height = 5, width = 10)
plot(Na_content)
dev.off()
## png 
##   2
Na_content <- ggplot(data = ICP, mapping = aes(x = All.condition, y = Na.con.mg.mg.dry.weight, colour = All.condition)) 
Na_content <- Na_content + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

Na_content <- Na_content + facet_grid(~Tissue)

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","blueviolet","cyan", "red", "deeppink", "magenta"))
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")
Na_content

aov(K.con..mg.mg.dry.weight ~ All.ID, data = ICP)
## Call:
##    aov(formula = K.con..mg.mg.dry.weight ~ All.ID, data = ICP)
## 
## Terms:
##                   All.ID Residuals
## Sum of Squares  4309.607   724.785
## Deg. of Freedom       11        24
## 
## Residual standard error: 5.495394
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(K.con..mg.mg.dry.weight ~ All.ID, data = ICP))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = K.con..mg.mg.dry.weight ~ All.ID, data = ICP)
## 
## $All.ID
##                                     diff         lwr         upr     p adj
## LA_C+IAA_SH-LA_C+IAA_RO     -24.50743740 -40.6857900  -8.3290848 0.0006441
## LA_C+MOCK_RO-LA_C+IAA_RO     -8.66903058 -24.8473832   7.5093221 0.7299026
## LA_C+MOCK_SH-LA_C+IAA_RO    -27.52174932 -43.7001020 -11.3433967 0.0001284
## LA_C+NOIAA_RO-LA_C+IAA_RO    -7.54831162 -23.7266643   8.6300410 0.8593822
## LA_C+NOIAA_SH-LA_C+IAA_RO   -23.08795983 -39.2663125  -6.9096072 0.0013824
## LA_S+IAA_RO-LA_C+IAA_RO     -17.08791459 -33.2662672  -0.9095620 0.0320725
## LA_S+IAA_SH-LA_C+IAA_RO     -33.44049072 -49.6188434 -17.2621381 0.0000060
## LA_S+MOCK_RO-LA_C+IAA_RO    -15.40994170 -31.5882943   0.7684109 0.0718724
## LA_S+MOCK_SH-LA_C+IAA_RO    -34.48489472 -50.6632474 -18.3065421 0.0000036
## LA_S+NOIAA_RO-LA_C+IAA_RO   -11.73943951 -27.9177921   4.4389131 0.3238674
## LA_S+NOIAA_SH-LA_C+IAA_RO   -33.38786333 -49.5662160 -17.2095107 0.0000062
## LA_C+MOCK_RO-LA_C+IAA_SH     15.83840681  -0.3399458  32.0167594 0.0587959
## LA_C+MOCK_SH-LA_C+IAA_SH     -3.01431192 -19.1926646  13.1640407 0.9998983
## LA_C+NOIAA_RO-LA_C+IAA_SH    16.95912578   0.7807731  33.1374784 0.0341826
## LA_C+NOIAA_SH-LA_C+IAA_SH     1.41947757 -14.7588751  17.5978302 1.0000000
## LA_S+IAA_RO-LA_C+IAA_SH       7.41952281  -8.7588298  23.5978754 0.8716477
## LA_S+IAA_SH-LA_C+IAA_SH      -8.93305332 -25.1114060   7.2452993 0.6948573
## LA_S+MOCK_RO-LA_C+IAA_SH      9.09749570  -7.0808569  25.2758483 0.6724855
## LA_S+MOCK_SH-LA_C+IAA_SH     -9.97745732 -26.1558100   6.2008953 0.5496100
## LA_S+NOIAA_RO-LA_C+IAA_SH    12.76799789  -3.4103547  28.9463505 0.2225935
## LA_S+NOIAA_SH-LA_C+IAA_SH    -8.88042593 -25.0587786   7.2979267 0.7019367
## LA_C+MOCK_SH-LA_C+MOCK_RO   -18.85271874 -35.0310714  -2.6743661 0.0130948
## LA_C+NOIAA_RO-LA_C+MOCK_RO    1.12071897 -15.0576337  17.2990716 1.0000000
## LA_C+NOIAA_SH-LA_C+MOCK_RO  -14.41892924 -30.5972819   1.7594234 0.1124779
## LA_S+IAA_RO-LA_C+MOCK_RO     -8.41888400 -24.5972366   7.7594686 0.7618308
## LA_S+IAA_SH-LA_C+MOCK_RO    -24.77146014 -40.9498128  -8.5931075 0.0005588
## LA_S+MOCK_RO-LA_C+MOCK_RO    -6.74091111 -22.9192637   9.4374415 0.9258021
## LA_S+MOCK_SH-LA_C+MOCK_RO   -25.81586414 -41.9942168  -9.6375115 0.0003190
## LA_S+NOIAA_RO-LA_C+MOCK_RO   -3.07040893 -19.2487616  13.1079437 0.9998784
## LA_S+NOIAA_SH-LA_C+MOCK_RO  -24.71883275 -40.8971854  -8.5404801 0.0005749
## LA_C+NOIAA_RO-LA_C+MOCK_SH   19.97343770   3.7950851  36.1517903 0.0072906
## LA_C+NOIAA_SH-LA_C+MOCK_SH    4.43378949 -11.7445631  20.6121421 0.9965576
## LA_S+IAA_RO-LA_C+MOCK_SH     10.43383473  -5.7445179  26.6121874 0.4866167
## LA_S+IAA_SH-LA_C+MOCK_SH     -5.91874140 -22.0970940  10.2596112 0.9680895
## LA_S+MOCK_RO-LA_C+MOCK_SH    12.11180762  -4.0665450  28.2901603 0.2841899
## LA_S+MOCK_SH-LA_C+MOCK_SH    -6.96314540 -23.1414980   9.2152072 0.9100366
## LA_S+NOIAA_RO-LA_C+MOCK_SH   15.78230981  -0.3960428  31.9606624 0.0603758
## LA_S+NOIAA_SH-LA_C+MOCK_SH   -5.86611401 -22.0444666  10.3122386 0.9700054
## LA_C+NOIAA_SH-LA_C+NOIAA_RO -15.53964821 -31.7180008   0.6387044 0.0676618
## LA_S+IAA_RO-LA_C+NOIAA_RO    -9.53960297 -25.7179556   6.6387497 0.6110368
## LA_S+IAA_SH-LA_C+NOIAA_RO   -25.89217910 -42.0705317  -9.7138265 0.0003062
## LA_S+MOCK_RO-LA_C+NOIAA_RO   -7.86163008 -24.0399827   8.3167226 0.8270583
## LA_S+MOCK_SH-LA_C+NOIAA_RO  -26.93658310 -43.1149357 -10.7582305 0.0001753
## LA_S+NOIAA_RO-LA_C+NOIAA_RO  -4.19112789 -20.3694805  11.9872247 0.9978718
## LA_S+NOIAA_SH-LA_C+NOIAA_RO -25.83955171 -42.0179043  -9.6611991 0.0003150
## LA_S+IAA_RO-LA_C+NOIAA_SH     6.00004524 -10.1783074  22.1783979 0.9649573
## LA_S+IAA_SH-LA_C+NOIAA_SH   -10.35253089 -26.5308835   5.8258217 0.4976946
## LA_S+MOCK_RO-LA_C+NOIAA_SH    7.67801813  -8.5003345  23.8563708 0.8464180
## LA_S+MOCK_SH-LA_C+NOIAA_SH  -11.39693489 -27.5752875   4.7814177 0.3632513
## LA_S+NOIAA_RO-LA_C+NOIAA_SH  11.34852032  -4.8298323  27.5268730 0.3690306
## LA_S+NOIAA_SH-LA_C+NOIAA_SH -10.29990350 -26.4782561   5.8784491 0.5049043
## LA_S+IAA_SH-LA_S+IAA_RO     -16.35257613 -32.5309288  -0.1742235 0.0459752
## LA_S+MOCK_RO-LA_S+IAA_RO      1.67797289 -14.5003797  17.8563255 0.9999997
## LA_S+MOCK_SH-LA_S+IAA_RO    -17.39698013 -33.5753328  -1.2186275 0.0274965
## LA_S+NOIAA_RO-LA_S+IAA_RO     5.34847508 -10.8298776  21.5268277 0.9846245
## LA_S+NOIAA_SH-LA_S+IAA_RO   -16.29994874 -32.4783014  -0.1215961 0.0471584
## LA_S+MOCK_RO-LA_S+IAA_SH     18.03054902   1.8521964  34.2089017 0.0199717
## LA_S+MOCK_SH-LA_S+IAA_SH     -1.04440400 -17.2227566  15.1339486 1.0000000
## LA_S+NOIAA_RO-LA_S+IAA_SH    21.70105121   5.5226986  37.8794038 0.0029105
## LA_S+NOIAA_SH-LA_S+IAA_SH     0.05262739 -16.1257252  16.2309800 1.0000000
## LA_S+MOCK_SH-LA_S+MOCK_RO   -19.07495302 -35.2533057  -2.8966004 0.0116689
## LA_S+NOIAA_RO-LA_S+MOCK_RO    3.67050219 -12.5078504  19.8488548 0.9993473
## LA_S+NOIAA_SH-LA_S+MOCK_RO  -17.97792163 -34.1562743  -1.7995690 0.0205133
## LA_S+NOIAA_RO-LA_S+MOCK_SH   22.74545521   6.5671026  38.9238078 0.0016620
## LA_S+NOIAA_SH-LA_S+MOCK_SH    1.09703139 -15.0813212  17.2753840 1.0000000
## LA_S+NOIAA_SH-LA_S+NOIAA_RO -21.64842382 -37.8267765  -5.4700712 0.0029936
P8 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P8)
stat.test
##   LA_C+IAA_SH  LA_C+MOCK_RO  LA_C+MOCK_SH LA_C+NOIAA_RO LA_C+NOIAA_SH 
##         "abc"         "ade"          "bc"          "de"        "abcd" 
##   LA_S+IAA_RO   LA_S+IAA_SH  LA_S+MOCK_RO  LA_S+MOCK_SH LA_S+NOIAA_RO 
##         "abd"           "c"        "abde"           "c"        "abde" 
## LA_S+NOIAA_SH   LA_C+IAA_RO 
##           "c"           "e"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
ICP$All.condition<- factor(ICP$All.condition, levels=c("C+NOIAA", "C+MOCK", "C+IAA","S+NOIAA", "S+MOCK", "S+IAA"))

K_content <- ggplot(data = ICP, mapping = aes(x = All.ID, y = K.con..mg.mg.dry.weight, colour = All.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","blueviolet","cyan", "red", "deeppink", "magenta"))
K_content  <- K_content  + ylab("Na 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 = 120) + rremove("legend")
K_content 

pdf("K.accumulation.Iaa.shoot.spray.ICPMS.pdf", height = 5, width = 10)
plot(K_content)
dev.off()
## png 
##   2
K_content <- ggplot(data = ICP, mapping = aes(x = All.condition, y = K.con..mg.mg.dry.weight, colour = All.condition)) 
K_content <- K_content + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

K_content <- K_content + facet_grid(~Tissue)

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","blueviolet","cyan", "red", "deeppink", "magenta"))
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")
K_content

aov(Na.K.ratio ~ All.ID, data = ICP)
## Call:
##    aov(formula = Na.K.ratio ~ All.ID, data = ICP)
## 
## Terms:
##                    All.ID Residuals
## Sum of Squares  27.924935  3.188312
## Deg. of Freedom        11        24
## 
## Residual standard error: 0.3644809
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(Na.K.ratio ~ All.ID, data = ICP))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Na.K.ratio ~ All.ID, data = ICP)
## 
## $All.ID
##                                     diff         lwr          upr     p adj
## LA_C+IAA_SH-LA_C+IAA_RO      0.074945216 -0.99808075  1.147971185 1.0000000
## LA_C+MOCK_RO-LA_C+IAA_RO     0.007359543 -1.06566643  1.080385513 1.0000000
## LA_C+MOCK_SH-LA_C+IAA_RO     0.086552165 -0.98647380  1.159578134 1.0000000
## LA_C+NOIAA_RO-LA_C+IAA_RO    0.006086102 -1.06693987  1.079112072 1.0000000
## LA_C+NOIAA_SH-LA_C+IAA_RO    0.066031971 -1.00699400  1.139057941 1.0000000
## LA_S+IAA_RO-LA_C+IAA_RO      1.288407937  0.21538197  2.361433907 0.0097188
## LA_S+IAA_SH-LA_C+IAA_RO      2.329173693  1.25614772  3.402199662 0.0000026
## LA_S+MOCK_RO-LA_C+IAA_RO     1.262170552  0.18914458  2.335196522 0.0119438
## LA_S+MOCK_SH-LA_C+IAA_RO     2.080482684  1.00745671  3.153508653 0.0000172
## LA_S+NOIAA_RO-LA_C+IAA_RO    1.196934675  0.12390871  2.269960644 0.0198096
## LA_S+NOIAA_SH-LA_C+IAA_RO    1.937404461  0.86437849  3.010430430 0.0000527
## LA_C+MOCK_RO-LA_C+IAA_SH    -0.067585672 -1.14061164  1.005440297 1.0000000
## LA_C+MOCK_SH-LA_C+IAA_SH     0.011606949 -1.06141902  1.084632919 1.0000000
## LA_C+NOIAA_RO-LA_C+IAA_SH   -0.068859113 -1.14188508  1.004166856 1.0000000
## LA_C+NOIAA_SH-LA_C+IAA_SH   -0.008913244 -1.08193921  1.064112725 1.0000000
## LA_S+IAA_RO-LA_C+IAA_SH      1.213462722  0.14043675  2.286488691 0.0174435
## LA_S+IAA_SH-LA_C+IAA_SH      2.254228477  1.18120251  3.327254447 0.0000046
## LA_S+MOCK_RO-LA_C+IAA_SH     1.187225337  0.11419937  2.260251306 0.0213391
## LA_S+MOCK_SH-LA_C+IAA_SH     2.005537468  0.93251150  3.078563437 0.0000308
## LA_S+NOIAA_RO-LA_C+IAA_SH    1.121989459  0.04896349  2.195015428 0.0349076
## LA_S+NOIAA_SH-LA_C+IAA_SH    1.862459245  0.78943328  2.935485214 0.0000955
## LA_C+MOCK_SH-LA_C+MOCK_RO    0.079192622 -0.99383335  1.152218591 1.0000000
## LA_C+NOIAA_RO-LA_C+MOCK_RO  -0.001273441 -1.07429941  1.071752528 1.0000000
## LA_C+NOIAA_SH-LA_C+MOCK_RO   0.058672428 -1.01435354  1.131698397 1.0000000
## LA_S+IAA_RO-LA_C+MOCK_RO     1.281048394  0.20802242  2.354074363 0.0102987
## LA_S+IAA_SH-LA_C+MOCK_RO     2.321814150  1.24878818  3.394840119 0.0000028
## LA_S+MOCK_RO-LA_C+MOCK_RO    1.254811009  0.18178504  2.327836978 0.0126516
## LA_S+MOCK_SH-LA_C+MOCK_RO    2.073123140  1.00009717  3.146149110 0.0000182
## LA_S+NOIAA_RO-LA_C+MOCK_RO   1.189575131  0.11654916  2.262601101 0.0209589
## LA_S+NOIAA_SH-LA_C+MOCK_RO   1.930044917  0.85701895  3.003070887 0.0000558
## LA_C+NOIAA_RO-LA_C+MOCK_SH  -0.080466063 -1.15349203  0.992559907 1.0000000
## LA_C+NOIAA_SH-LA_C+MOCK_SH  -0.020520194 -1.09354616  1.052505776 1.0000000
## LA_S+IAA_RO-LA_C+MOCK_SH     1.201855772  0.12882980  2.274881742 0.0190748
## LA_S+IAA_SH-LA_C+MOCK_SH     2.242621528  1.16959556  3.315647497 0.0000050
## LA_S+MOCK_RO-LA_C+MOCK_SH    1.175618387  0.10259242  2.248644357 0.0233149
## LA_S+MOCK_SH-LA_C+MOCK_SH    1.993930519  0.92090455  3.066956488 0.0000338
## LA_S+NOIAA_RO-LA_C+MOCK_SH   1.110382510  0.03735654  2.183408479 0.0380438
## LA_S+NOIAA_SH-LA_C+MOCK_SH   1.850852296  0.77782633  2.923878265 0.0001048
## LA_C+NOIAA_SH-LA_C+NOIAA_RO  0.059945869 -1.01308010  1.132971838 1.0000000
## LA_S+IAA_RO-LA_C+NOIAA_RO    1.282321835  0.20929587  2.355347804 0.0101961
## LA_S+IAA_SH-LA_C+NOIAA_RO    2.323087591  1.25006162  3.396113560 0.0000027
## LA_S+MOCK_RO-LA_C+NOIAA_RO   1.256084450  0.18305848  2.329110419 0.0125263
## LA_S+MOCK_SH-LA_C+NOIAA_RO   2.074396581  1.00137061  3.147422551 0.0000180
## LA_S+NOIAA_RO-LA_C+NOIAA_RO  1.190848572  0.11782260  2.263874542 0.0207556
## LA_S+NOIAA_SH-LA_C+NOIAA_RO  1.931318358  0.85829239  3.004344328 0.0000553
## LA_S+IAA_RO-LA_C+NOIAA_SH    1.222375966  0.14935000  2.295401935 0.0162823
## LA_S+IAA_SH-LA_C+NOIAA_SH    2.263141722  1.19011575  3.336167691 0.0000043
## LA_S+MOCK_RO-LA_C+NOIAA_SH   1.196138581  0.12311261  2.269164550 0.0199309
## LA_S+MOCK_SH-LA_C+NOIAA_SH   2.014450712  0.94142474  3.087476682 0.0000287
## LA_S+NOIAA_RO-LA_C+NOIAA_SH  1.130902703  0.05787673  2.203928673 0.0326649
## LA_S+NOIAA_SH-LA_C+NOIAA_SH  1.871372489  0.79834652  2.944398459 0.0000889
## LA_S+IAA_SH-LA_S+IAA_RO      1.040765756 -0.03226021  2.113791725 0.0630015
## LA_S+MOCK_RO-LA_S+IAA_RO    -0.026237385 -1.09926335  1.046788584 1.0000000
## LA_S+MOCK_SH-LA_S+IAA_RO     0.792074746 -0.28095122  1.865100716 0.3018282
## LA_S+NOIAA_RO-LA_S+IAA_RO   -0.091473263 -1.16449923  0.981552707 1.0000000
## LA_S+NOIAA_SH-LA_S+IAA_RO    0.648996523 -0.42402945  1.722022493 0.5765604
## LA_S+MOCK_RO-LA_S+IAA_SH    -1.067003141 -2.14002911  0.006022829 0.0522236
## LA_S+MOCK_SH-LA_S+IAA_SH    -0.248691009 -1.32171698  0.824334960 0.9992074
## LA_S+NOIAA_RO-LA_S+IAA_SH   -1.132239018 -2.20526499 -0.059213049 0.0323405
## LA_S+NOIAA_SH-LA_S+IAA_SH   -0.391769232 -1.46479520  0.681256737 0.9685308
## LA_S+MOCK_SH-LA_S+MOCK_RO    0.818312131 -0.25471384  1.891338101 0.2617531
## LA_S+NOIAA_RO-LA_S+MOCK_RO  -0.065235878 -1.13826185  1.007790092 1.0000000
## LA_S+NOIAA_SH-LA_S+MOCK_RO   0.675233908 -0.39779206  1.748259878 0.5213333
## LA_S+NOIAA_RO-LA_S+MOCK_SH  -0.883548009 -1.95657398  0.189477960 0.1788509
## LA_S+NOIAA_SH-LA_S+MOCK_SH  -0.143078223 -1.21610419  0.929947746 0.9999965
## LA_S+NOIAA_SH-LA_S+NOIAA_RO  0.740469786 -0.33255618  1.813495755 0.3914757
P9 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P9)
stat.test
##   LA_C+IAA_SH  LA_C+MOCK_RO  LA_C+MOCK_SH LA_C+NOIAA_RO LA_C+NOIAA_SH 
##           "a"           "a"           "a"           "a"           "a" 
##   LA_S+IAA_RO   LA_S+IAA_SH  LA_S+MOCK_RO  LA_S+MOCK_SH LA_S+NOIAA_RO 
##          "bc"           "b"          "bc"          "bc"           "c" 
## LA_S+NOIAA_SH   LA_C+IAA_RO 
##          "bc"           "a"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
ICP$All.condition<- factor(ICP$All.condition, levels=c("C+NOIAA", "C+MOCK", "C+IAA","S+NOIAA", "S+MOCK", "S+IAA"))

NaK_ratio <- ggplot(data = ICP, mapping = aes(x = All.ID, y = Na.K.ratio, colour = All.condition)) 
NaK_ratio  <- NaK_ratio  + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
NaK_ratio  <- NaK_ratio  + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
NaK_ratio  <- NaK_ratio  + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
NaK_ratio  <- NaK_ratio  + ylab("Na/K ratio") + xlab("")
NaK_ratio  <- NaK_ratio  + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
NaK_ratio  <- NaK_ratio  + stat_pvalue_manual(test, label = "Tukey", y.position = 120) + rremove("legend")
NaK_ratio 

pdf("Na.K.ratio.Iaa.shoot.spray.ICPMS.pdf", height = 5, width = 10)
plot(NaK_ratio)
dev.off()
## png 
##   2
NaK_ratio <- ggplot(data = ICP, mapping = aes(x = All.condition, y = Na.K.ratio, colour = All.condition)) 
NaK_ratio <- NaK_ratio + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

NaK_ratio <- NaK_ratio + facet_grid(~Tissue)

NaK_ratio <- NaK_ratio + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
NaK_ratio <- NaK_ratio + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
NaK_ratio <- NaK_ratio + ylab("Na/K ratio") + xlab("")
NaK_ratio <- NaK_ratio + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
NaK_ratio <- NaK_ratio + rremove("legend")
NaK_ratio

aov(DW.mg ~ All.ID, data = ICP)
## Call:
##    aov(formula = DW.mg ~ All.ID, data = ICP)
## 
## Terms:
##                   All.ID Residuals
## Sum of Squares  4363.842   491.540
## Deg. of Freedom       11        24
## 
## Residual standard error: 4.525575
## Estimated effects may be unbalanced
Output <- TukeyHSD(aov(DW.mg ~ All.ID, data = ICP))
Output
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DW.mg ~ All.ID, data = ICP)
## 
## $All.ID
##                                    diff        lwr         upr     p adj
## LA_C+IAA_SH-LA_C+IAA_RO      23.2000000   9.876778  36.5232216 0.0000910
## LA_C+MOCK_RO-LA_C+IAA_RO      3.3333333  -9.989888  16.6565549 0.9984298
## LA_C+MOCK_SH-LA_C+IAA_RO     25.5666667  12.243445  38.8898882 0.0000203
## LA_C+NOIAA_RO-LA_C+IAA_RO    -2.9666667 -16.289888  10.3565549 0.9994503
## LA_C+NOIAA_SH-LA_C+IAA_RO     9.1000000  -4.223222  22.4232216 0.4057350
## LA_S+IAA_RO-LA_C+IAA_RO      -8.4000000 -21.723222   4.9232216 0.5186528
## LA_S+IAA_SH-LA_C+IAA_RO       0.2666667 -13.056555  13.5898882 1.0000000
## LA_S+MOCK_RO-LA_C+IAA_RO     -8.6333333 -21.956555   4.6898882 0.4798975
## LA_S+MOCK_SH-LA_C+IAA_RO     -2.5000000 -15.823222  10.8232216 0.9998911
## LA_S+NOIAA_RO-LA_C+IAA_RO    -8.5333333 -21.856555   4.7898882 0.4963999
## LA_S+NOIAA_SH-LA_C+IAA_RO    -1.9000000 -15.223222  11.4232216 0.9999929
## LA_C+MOCK_RO-LA_C+IAA_SH    -19.8666667 -33.189888  -6.5434451 0.0007916
## LA_C+MOCK_SH-LA_C+IAA_SH      2.3666667 -10.956555  15.6898882 0.9999361
## LA_C+NOIAA_RO-LA_C+IAA_SH   -26.1666667 -39.489888 -12.8434451 0.0000140
## LA_C+NOIAA_SH-LA_C+IAA_SH   -14.1000000 -27.423222  -0.7767784 0.0315412
## LA_S+IAA_RO-LA_C+IAA_SH     -31.6000000 -44.923222 -18.2767784 0.0000005
## LA_S+IAA_SH-LA_C+IAA_SH     -22.9333333 -36.256555  -9.6101118 0.0001080
## LA_S+MOCK_RO-LA_C+IAA_SH    -31.8333333 -45.156555 -18.5101118 0.0000005
## LA_S+MOCK_SH-LA_C+IAA_SH    -25.7000000 -39.023222 -12.3767784 0.0000187
## LA_S+NOIAA_RO-LA_C+IAA_SH   -31.7333333 -45.056555 -18.4101118 0.0000005
## LA_S+NOIAA_SH-LA_C+IAA_SH   -25.1000000 -38.423222 -11.7767784 0.0000272
## LA_C+MOCK_SH-LA_C+MOCK_RO    22.2333333   8.910112  35.5565549 0.0001697
## LA_C+NOIAA_RO-LA_C+MOCK_RO   -6.3000000 -19.623222   7.0232216 0.8492620
## LA_C+NOIAA_SH-LA_C+MOCK_RO    5.7666667  -7.556555  19.0898882 0.9070482
## LA_S+IAA_RO-LA_C+MOCK_RO    -11.7333333 -25.056555   1.5898882 0.1212064
## LA_S+IAA_SH-LA_C+MOCK_RO     -3.0666667 -16.389888  10.2565549 0.9992555
## LA_S+MOCK_RO-LA_C+MOCK_RO   -11.9666667 -25.289888   1.3565549 0.1070549
## LA_S+MOCK_SH-LA_C+MOCK_RO    -5.8333333 -19.156555   7.4898882 0.9007029
## LA_S+NOIAA_RO-LA_C+MOCK_RO  -11.8666667 -25.189888   1.4565549 0.1129364
## LA_S+NOIAA_SH-LA_C+MOCK_RO   -5.2333333 -18.556555   8.0898882 0.9486903
## LA_C+NOIAA_RO-LA_C+MOCK_SH  -28.5333333 -41.856555 -15.2101118 0.0000033
## LA_C+NOIAA_SH-LA_C+MOCK_SH  -16.4666667 -29.789888  -3.1434451 0.0072067
## LA_S+IAA_RO-LA_C+MOCK_SH    -33.9666667 -47.289888 -20.6434451 0.0000001
## LA_S+IAA_SH-LA_C+MOCK_SH    -25.3000000 -38.623222 -11.9767784 0.0000240
## LA_S+MOCK_RO-LA_C+MOCK_SH   -34.2000000 -47.523222 -20.8767784 0.0000001
## LA_S+MOCK_SH-LA_C+MOCK_SH   -28.0666667 -41.389888 -14.7434451 0.0000044
## LA_S+NOIAA_RO-LA_C+MOCK_SH  -34.1000000 -47.423222 -20.7767784 0.0000001
## LA_S+NOIAA_SH-LA_C+MOCK_SH  -27.4666667 -40.789888 -14.1434451 0.0000063
## LA_C+NOIAA_SH-LA_C+NOIAA_RO  12.0666667  -1.256555  25.3898882 0.1014390
## LA_S+IAA_RO-LA_C+NOIAA_RO    -5.4333333 -18.756555   7.8898882 0.9349502
## LA_S+IAA_SH-LA_C+NOIAA_RO     3.2333333 -10.089888  16.5565549 0.9988009
## LA_S+MOCK_RO-LA_C+NOIAA_RO   -5.6666667 -18.989888   7.6565549 0.9160868
## LA_S+MOCK_SH-LA_C+NOIAA_RO    0.4666667 -12.856555  13.7898882 1.0000000
## LA_S+NOIAA_RO-LA_C+NOIAA_RO  -5.5666667 -18.889888   7.7565549 0.9245509
## LA_S+NOIAA_SH-LA_C+NOIAA_RO   1.0666667 -12.256555  14.3898882 1.0000000
## LA_S+IAA_RO-LA_C+NOIAA_SH   -17.5000000 -30.823222  -4.1767784 0.0037035
## LA_S+IAA_SH-LA_C+NOIAA_SH    -8.8333333 -22.156555   4.4898882 0.4474810
## LA_S+MOCK_RO-LA_C+NOIAA_SH  -17.7333333 -31.056555  -4.4101118 0.0031833
## LA_S+MOCK_SH-LA_C+NOIAA_SH  -11.6000000 -24.923222   1.7232216 0.1299840
## LA_S+NOIAA_RO-LA_C+NOIAA_SH -17.6333333 -30.956555  -4.3101118 0.0033968
## LA_S+NOIAA_SH-LA_C+NOIAA_SH -11.0000000 -24.323222   2.3232216 0.1762767
## LA_S+IAA_SH-LA_S+IAA_RO       8.6666667  -4.656555  21.9898882 0.4744374
## LA_S+MOCK_RO-LA_S+IAA_RO     -0.2333333 -13.556555  13.0898882 1.0000000
## LA_S+MOCK_SH-LA_S+IAA_RO      5.9000000  -7.423222  19.2232216 0.8941026
## LA_S+NOIAA_RO-LA_S+IAA_RO    -0.1333333 -13.456555  13.1898882 1.0000000
## LA_S+NOIAA_SH-LA_S+IAA_RO     6.5000000  -6.823222  19.8232216 0.8236443
## LA_S+MOCK_RO-LA_S+IAA_SH     -8.9000000 -22.223222   4.4232216 0.4368744
## LA_S+MOCK_SH-LA_S+IAA_SH     -2.7666667 -16.089888  10.5565549 0.9997131
## LA_S+NOIAA_RO-LA_S+IAA_SH    -8.8000000 -22.123222   4.5232216 0.4528236
## LA_S+NOIAA_SH-LA_S+IAA_SH    -2.1666667 -15.489888  11.1565549 0.9999734
## LA_S+MOCK_SH-LA_S+MOCK_RO     6.1333333  -7.189888  19.4565549 0.8690177
## LA_S+NOIAA_RO-LA_S+MOCK_RO    0.1000000 -13.223222  13.4232216 1.0000000
## LA_S+NOIAA_SH-LA_S+MOCK_RO    6.7333333  -6.589888  20.0565549 0.7913345
## LA_S+NOIAA_RO-LA_S+MOCK_SH   -6.0333333 -19.356555   7.2898882 0.8801428
## LA_S+NOIAA_SH-LA_S+MOCK_SH    0.6000000 -12.723222  13.9232216 1.0000000
## LA_S+NOIAA_SH-LA_S+NOIAA_RO   6.6333333  -6.689888  19.9565549 0.8054831
P6 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P6)
stat.test
##   LA_C+IAA_SH  LA_C+MOCK_RO  LA_C+MOCK_SH LA_C+NOIAA_RO LA_C+NOIAA_SH 
##           "a"          "bc"           "a"          "bc"           "b" 
##   LA_S+IAA_RO   LA_S+IAA_SH  LA_S+MOCK_RO  LA_S+MOCK_SH LA_S+NOIAA_RO 
##           "c"          "bc"           "c"          "bc"           "c" 
## LA_S+NOIAA_SH   LA_C+IAA_RO 
##          "bc"          "bc"
test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
test
ICP$All.condition<- factor(ICP$All.condition, levels=c("C+NOIAA", "C+MOCK", "C+IAA","S+NOIAA", "S+MOCK", "S+IAA"))

DW_IAA <- ggplot(data = ICP, mapping = aes(x = All.ID, y = DW.mg, colour = All.condition)) 
DW_IAA  <- DW_IAA  + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
DW_IAA  <- DW_IAA  + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
DW_IAA  <- DW_IAA  + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
DW_IAA  <- DW_IAA  + ylab("Dry weight, mg") + xlab("")
DW_IAA  <- DW_IAA  + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
DW_IAA  <- DW_IAA  + stat_pvalue_manual(test, label = "Tukey", y.position = 120) + rremove("legend")
DW_IAA 

pdf("DW.Iaa.shoot.spray.pdf", height = 5, width = 10)
plot(DW_IAA)
dev.off()
## png 
##   2
DW_IAA <- ggplot(data = ICP, mapping = aes(x = All.condition, y = DW.mg, colour = All.condition)) 
DW_IAA <- NaK_ratio + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)

DW_IAA <- DW_IAA + facet_grid(~Tissue)

DW_IAA <- DW_IAA + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
DW_IAA <- DW_IAA + scale_color_manual(values = c("blue","blueviolet","cyan", "red", "deeppink", "magenta"))
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
DW_IAA <- DW_IAA + ylab("Dry weight, mg") + xlab("")
DW_IAA <- DW_IAA + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
DW_IAA <- DW_IAA + rremove("legend")
DW_IAA