202406_ICP_analysis_tomato_soil_salt_ACC_IAA_shoot_spray_m248_la1511
This is a ICP-AES analysis for two tomato accessions grown in soil with and without salt stress, plus with different spraying regime for two hormones, ACC and IAA for almost 3 weeks.For each hormone, there is 10 d and 4 weeks data.
getwd()## [1] "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/202406_ICP_analysis_tomato_ACC_IAA_soil_salt_spary_experiment_10dand4weeks_tissues"list.files(pattern=".csv")## [1] "ICP-ACC-SOIL-SPAR-SALT-10d.csv" "ICP-ACC-SOIL-SPAR-SALT-4weeks.csv"
## [3] "ICP-IAA-SOIL-SPAR-SALT-10d.csv" "ICP-IAA-SOIL-SPAR-SALT-4weeks.csv"#lets start with 10 of ACC treatment.
ICP_ACC_10d <- read.csv("ICP-ACC-SOIL-SPAR-SALT-10d.csv")
ICP_ACC_10dICP_ACC_10d$All.condition<-paste(ICP_ACC_10d$Condition1,ICP_ACC_10d$Condition2, sep="+")
ICP_ACC_10dICP_ACC_10d$All.ID2<-paste(ICP_ACC_10d$Accession,ICP_ACC_10d$All.condition, ICP_ACC_10d$Tissue,sep="_")
ICP_ACC_10dlibrary(ggplot2)
library(ggpubr)
library(multcompView)## Warning: package 'multcompView' was built under R version 4.3.2aov(Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_10d)## Call:
## aov(formula = Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_10d)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 505.7821 889.4427
## Deg. of Freedom 11 47
##
## Residual standard error: 4.350208
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_10d))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_10d)
##
## $All.ID2
## diff lwr upr p adj
## la_s+1acc_sh-la_s+1acc_ro 3.3415727 -6.6892520 13.372397 0.9906268
## la_s+mock_ro-la_s+1acc_ro -0.1864787 -10.2173034 9.844346 1.0000000
## la_s+mock_sh-la_s+1acc_ro 4.6130970 -5.4177277 14.643922 0.9074126
## la_s+noacc_ro-la_s+1acc_ro 3.6525100 -6.3783147 13.683335 0.9812262
## la_s+noacc_sh-la_s+1acc_ro 2.0669659 -7.9638588 12.097791 0.9998761
## M248_s+1acc_ro-la_s+1acc_ro 3.1382347 -6.8925900 13.169059 0.9943965
## M248_s+1acc_sh-la_s+1acc_ro 6.5394492 -3.4913755 16.570274 0.5301969
## M248_s+mock_ro-la_s+1acc_ro 2.4587829 -7.5720418 12.489608 0.9993522
## M248_s+mock_sh-la_s+1acc_ro 7.1848517 -2.8459731 17.215676 0.3876162
## M248_s+noacc_ro-la_s+1acc_ro 9.6033941 -0.4274306 19.634219 0.0720382
## M248_s+noacc_sh-la_s+1acc_ro 7.9905264 -2.0402983 18.021351 0.2402611
## la_s+mock_ro-la_s+1acc_sh -3.5280514 -12.9852037 5.929101 0.9774893
## la_s+mock_sh-la_s+1acc_sh 1.2715243 -8.1856280 10.728677 0.9999984
## la_s+noacc_ro-la_s+1acc_sh 0.3109373 -9.1462149 9.768090 1.0000000
## la_s+noacc_sh-la_s+1acc_sh -1.2746068 -10.7317590 8.182545 0.9999983
## M248_s+1acc_ro-la_s+1acc_sh -0.2033380 -9.6604902 9.253814 1.0000000
## M248_s+1acc_sh-la_s+1acc_sh 3.1978764 -6.2592758 12.655029 0.9894367
## M248_s+mock_ro-la_s+1acc_sh -0.8827898 -10.3399420 8.574362 1.0000000
## M248_s+mock_sh-la_s+1acc_sh 3.8432789 -5.6138733 13.300431 0.9584408
## M248_s+noacc_ro-la_s+1acc_sh 6.2618213 -3.1953309 15.718974 0.5067511
## M248_s+noacc_sh-la_s+1acc_sh 4.6489537 -4.8081985 14.106106 0.8638194
## la_s+mock_sh-la_s+mock_ro 4.7995757 -4.6575765 14.256728 0.8381710
## la_s+noacc_ro-la_s+mock_ro 3.8389887 -5.6181635 13.296141 0.9587599
## la_s+noacc_sh-la_s+mock_ro 2.2534446 -7.2037076 11.710597 0.9995026
## M248_s+1acc_ro-la_s+mock_ro 3.3247135 -6.1324388 12.781866 0.9856576
## M248_s+1acc_sh-la_s+mock_ro 6.7259279 -2.7312243 16.183080 0.3982495
## M248_s+mock_ro-la_s+mock_ro 2.6452616 -6.8118906 12.102414 0.9978585
## M248_s+mock_sh-la_s+mock_ro 7.3713304 -2.0858219 16.828483 0.2681410
## M248_s+noacc_ro-la_s+mock_ro 9.7898728 0.3327205 19.247025 0.0365353
## M248_s+noacc_sh-la_s+mock_ro 8.1770052 -1.2801471 17.634157 0.1496984
## la_s+noacc_ro-la_s+mock_sh -0.9605870 -10.4177392 8.496565 0.9999999
## la_s+noacc_sh-la_s+mock_sh -2.5461311 -12.0032833 6.911021 0.9984751
## M248_s+1acc_ro-la_s+mock_sh -1.4748623 -10.9320145 7.982290 0.9999924
## M248_s+1acc_sh-la_s+mock_sh 1.9263522 -7.5308001 11.383504 0.9998894
## M248_s+mock_ro-la_s+mock_sh -2.1543141 -11.6114663 7.302838 0.9996746
## M248_s+mock_sh-la_s+mock_sh 2.5717546 -6.8853976 12.028907 0.9983323
## M248_s+noacc_ro-la_s+mock_sh 4.9902971 -4.4668552 14.447449 0.8023543
## M248_s+noacc_sh-la_s+mock_sh 3.3774294 -6.0797228 12.834582 0.9838066
## la_s+noacc_sh-la_s+noacc_ro -1.5855441 -11.0426963 7.871608 0.9999842
## M248_s+1acc_ro-la_s+noacc_ro -0.5142753 -9.9714275 8.942877 1.0000000
## M248_s+1acc_sh-la_s+noacc_ro 2.8869391 -6.5702131 12.344091 0.9954415
## M248_s+mock_ro-la_s+noacc_ro -1.1937271 -10.6508794 8.263425 0.9999992
## M248_s+mock_sh-la_s+noacc_ro 3.5323416 -5.9248106 12.989494 0.9772854
## M248_s+noacc_ro-la_s+noacc_ro 5.9508840 -3.5062682 15.408036 0.5828190
## M248_s+noacc_sh-la_s+noacc_ro 4.3380164 -5.1191358 13.795169 0.9088523
## M248_s+1acc_ro-la_s+noacc_sh 1.0712688 -8.3858834 10.528421 0.9999997
## M248_s+1acc_sh-la_s+noacc_sh 4.4724833 -4.9846690 13.929635 0.8907121
## M248_s+mock_ro-la_s+noacc_sh 0.3918170 -9.0653352 9.848969 1.0000000
## M248_s+mock_sh-la_s+noacc_sh 5.1178857 -4.3392665 14.575038 0.7764907
## M248_s+noacc_ro-la_s+noacc_sh 7.5364281 -1.9207241 16.993580 0.2397834
## M248_s+noacc_sh-la_s+noacc_sh 5.9235605 -3.5335917 15.380713 0.5895271
## M248_s+1acc_sh-M248_s+1acc_ro 3.4012144 -6.0559378 12.858367 0.9829132
## M248_s+mock_ro-M248_s+1acc_ro -0.6794518 -10.1366041 8.777700 1.0000000
## M248_s+mock_sh-M248_s+1acc_ro 4.0466169 -5.4105353 13.503769 0.9412172
## M248_s+noacc_ro-M248_s+1acc_ro 6.4651593 -2.9919929 15.922312 0.4581152
## M248_s+noacc_sh-M248_s+1acc_ro 4.8522917 -4.6048605 14.309444 0.8286321
## M248_s+mock_ro-M248_s+1acc_sh -4.0806663 -13.5378185 5.376486 0.9379149
## M248_s+mock_sh-M248_s+1acc_sh 0.6454025 -8.8117498 10.102555 1.0000000
## M248_s+noacc_ro-M248_s+1acc_sh 3.0639449 -6.3932073 12.521097 0.9925216
## M248_s+noacc_sh-M248_s+1acc_sh 1.4510773 -8.0060750 10.908230 0.9999936
## M248_s+mock_sh-M248_s+mock_ro 4.7260687 -4.7310835 14.183221 0.8509905
## M248_s+noacc_ro-M248_s+mock_ro 7.1446112 -2.3125411 16.601763 0.3104882
## M248_s+noacc_sh-M248_s+mock_ro 5.5317435 -3.9254087 14.988896 0.6841929
## M248_s+noacc_ro-M248_s+mock_sh 2.4185424 -7.0386098 11.875695 0.9990427
## M248_s+noacc_sh-M248_s+mock_sh 0.8056748 -8.6514774 10.262827 1.0000000
## M248_s+noacc_sh-M248_s+noacc_ro -1.6128676 -11.0700199 7.844285 0.9999812P10 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P10)
stat.test## la_s+1acc_sh la_s+mock_ro la_s+mock_sh la_s+noacc_ro la_s+noacc_sh
## "ab" "a" "ab" "ab" "ab"
## M248_s+1acc_ro M248_s+1acc_sh M248_s+mock_ro M248_s+mock_sh M248_s+noacc_ro
## "ab" "ab" "ab" "ab" "b"
## M248_s+noacc_sh la_s+1acc_ro
## "ab" "ab"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+1acc"test$info[[1]][3]## [1] "sh"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_ACC_10dICP_ACC_10d$All.condition <- factor(ICP_ACC_10d$All.condition, levels = c("s+noacc", "s+mock", "s+1acc"))
ICP_ACC_10d <- subset(ICP_ACC_10d, ICP_ACC_10d$Na.con.mg.mg.dry.weight < 30)
Na_content_ACC_10d <- ggplot(data = ICP_ACC_10d, mapping = aes(x = All.condition, y = Na.con.mg.mg.dry.weight, colour = All.condition))
Na_content_ACC_10d <- Na_content_ACC_10d + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Na_content_ACC_10d <- Na_content_ACC_10d + facet_grid(Tissue ~ Accession, scales = "free_y")
Na_content_ACC_10d <- Na_content_ACC_10d + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Na_content_ACC_10d <- Na_content_ACC_10d + scale_color_manual(values = c("red","blueviolet","cyan"))
Na_content_ACC_10d <- Na_content_ACC_10d + ylab("Na content, mg/mg dry weight") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 25)
Na_content_ACC_10d <- Na_content_ACC_10d + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Na_content_ACC_10d <- Na_content_ACC_10d + rremove("legend")
Na_content_ACC_10d
aov(K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_10d)## Call:
## aov(formula = K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_10d)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 15129.63 28071.42
## Deg. of Freedom 11 46
##
## Residual standard error: 24.7032
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_10d))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_10d)
##
## $All.ID2
## diff lwr upr p adj
## la_s+1acc_sh-la_s+1acc_ro -25.9832294 -83.006674 31.040215 0.9117071
## la_s+mock_ro-la_s+1acc_ro -0.2612648 -57.284709 56.762180 1.0000000
## la_s+mock_sh-la_s+1acc_ro -20.0170183 -77.040463 37.006426 0.9856414
## la_s+noacc_ro-la_s+1acc_ro 28.8052493 -28.218195 85.828694 0.8410205
## la_s+noacc_sh-la_s+1acc_ro -18.8401673 -75.863612 38.183277 0.9911104
## M248_s+1acc_ro-la_s+1acc_ro -11.4288257 -68.452270 45.594619 0.9999038
## M248_s+1acc_sh-la_s+1acc_ro -29.6638507 -86.687295 27.359594 0.8148412
## M248_s+mock_ro-la_s+1acc_ro -17.2537020 -74.277147 39.769743 0.9957089
## M248_s+mock_sh-la_s+1acc_ro -27.1392191 -84.162664 29.884226 0.8857043
## M248_s+noacc_ro-la_s+1acc_ro -13.9122620 -74.020250 46.195726 0.9996150
## M248_s+noacc_sh-la_s+1acc_ro -28.4825150 -85.505960 28.540930 0.8503225
## la_s+mock_ro-la_s+1acc_sh 25.7219647 -28.040255 79.484184 0.8821982
## la_s+mock_sh-la_s+1acc_sh 5.9662111 -47.796008 59.728430 0.9999998
## la_s+noacc_ro-la_s+1acc_sh 54.7884787 1.026260 108.550698 0.0422501
## la_s+noacc_sh-la_s+1acc_sh 7.1430622 -46.619157 60.905281 0.9999985
## M248_s+1acc_ro-la_s+1acc_sh 14.5544037 -39.207816 68.316623 0.9983723
## M248_s+1acc_sh-la_s+1acc_sh -3.6806213 -57.442841 50.081598 1.0000000
## M248_s+mock_ro-la_s+1acc_sh 8.7295274 -45.032692 62.491747 0.9999883
## M248_s+mock_sh-la_s+1acc_sh -1.1559896 -54.918209 52.606230 1.0000000
## M248_s+noacc_ro-la_s+1acc_sh 12.0709674 -44.952477 69.094412 0.9998361
## M248_s+noacc_sh-la_s+1acc_sh -2.4992856 -56.261505 51.262934 1.0000000
## la_s+mock_sh-la_s+mock_ro -19.7557535 -73.517973 34.006466 0.9796460
## la_s+noacc_ro-la_s+mock_ro 29.0665141 -24.695705 82.828733 0.7762019
## la_s+noacc_sh-la_s+mock_ro -18.5789025 -72.341122 35.183317 0.9872919
## M248_s+1acc_ro-la_s+mock_ro -11.1675609 -64.929780 42.594658 0.9998636
## M248_s+1acc_sh-la_s+mock_ro -29.4025860 -83.164805 24.359633 0.7637440
## M248_s+mock_ro-la_s+mock_ro -16.9924373 -70.754657 36.769782 0.9938135
## M248_s+mock_sh-la_s+mock_ro -26.8779543 -80.640174 26.884265 0.8495883
## M248_s+noacc_ro-la_s+mock_ro -13.6509972 -70.674442 43.372447 0.9994714
## M248_s+noacc_sh-la_s+mock_ro -28.2212502 -81.983469 25.540969 0.8062189
## la_s+noacc_ro-la_s+mock_sh 48.8222676 -4.939952 102.584487 0.1072784
## la_s+noacc_sh-la_s+mock_sh 1.1768510 -52.585368 54.939070 1.0000000
## M248_s+1acc_ro-la_s+mock_sh 8.5881926 -45.174027 62.350412 0.9999901
## M248_s+1acc_sh-la_s+mock_sh -9.6468325 -63.409052 44.115387 0.9999679
## M248_s+mock_ro-la_s+mock_sh 2.7633162 -50.998903 56.525535 1.0000000
## M248_s+mock_sh-la_s+mock_sh -7.1222008 -60.884420 46.640018 0.9999986
## M248_s+noacc_ro-la_s+mock_sh 6.1047563 -50.918688 63.128201 0.9999999
## M248_s+noacc_sh-la_s+mock_sh -8.4654967 -62.227716 45.296723 0.9999915
## la_s+noacc_sh-la_s+noacc_ro -47.6454166 -101.407636 6.116803 0.1270525
## M248_s+1acc_ro-la_s+noacc_ro -40.2340750 -93.996294 13.528144 0.3227769
## M248_s+1acc_sh-la_s+noacc_ro -58.4691001 -112.231319 -4.706881 0.0225388
## M248_s+mock_ro-la_s+noacc_ro -46.0589514 -99.821171 7.703268 0.1582429
## M248_s+mock_sh-la_s+noacc_ro -55.9444684 -109.706688 -2.182249 0.0348213
## M248_s+noacc_ro-la_s+noacc_ro -42.7175113 -99.740956 14.305933 0.3213617
## M248_s+noacc_sh-la_s+noacc_ro -57.2877643 -111.049984 -3.525545 0.0276844
## M248_s+1acc_ro-la_s+noacc_sh 7.4113416 -46.350878 61.173561 0.9999978
## M248_s+1acc_sh-la_s+noacc_sh -10.8236835 -64.585903 42.938536 0.9998995
## M248_s+mock_ro-la_s+noacc_sh 1.5864652 -52.175754 55.348684 1.0000000
## M248_s+mock_sh-la_s+noacc_sh -8.2990518 -62.061271 45.463167 0.9999931
## M248_s+noacc_ro-la_s+noacc_sh 4.9279053 -52.095539 61.951350 1.0000000
## M248_s+noacc_sh-la_s+noacc_sh -9.6423477 -63.404567 44.119871 0.9999681
## M248_s+1acc_sh-M248_s+1acc_ro -18.2350250 -71.997244 35.527194 0.9890329
## M248_s+mock_ro-M248_s+1acc_ro -5.8248763 -59.587096 47.937343 0.9999998
## M248_s+mock_sh-M248_s+1acc_ro -15.7103934 -69.472613 38.051826 0.9968144
## M248_s+noacc_ro-M248_s+1acc_ro -2.4834363 -59.506881 54.540008 1.0000000
## M248_s+noacc_sh-M248_s+1acc_ro -17.0536893 -70.815909 36.708530 0.9936265
## M248_s+mock_ro-M248_s+1acc_sh 12.4101487 -41.352071 66.172368 0.9996246
## M248_s+mock_sh-M248_s+1acc_sh 2.5246317 -51.237588 56.286851 1.0000000
## M248_s+noacc_ro-M248_s+1acc_sh 15.7515888 -41.271856 72.775033 0.9980526
## M248_s+noacc_sh-M248_s+1acc_sh 1.1813358 -52.580883 54.943555 1.0000000
## M248_s+mock_sh-M248_s+mock_ro -9.8855170 -63.647736 43.876702 0.9999590
## M248_s+noacc_ro-M248_s+mock_ro 3.3414401 -53.682005 60.364885 1.0000000
## M248_s+noacc_sh-M248_s+mock_ro -11.2288129 -64.991032 42.533406 0.9998562
## M248_s+noacc_ro-M248_s+mock_sh 13.2269571 -43.796488 70.250402 0.9996070
## M248_s+noacc_sh-M248_s+mock_sh -1.3432959 -55.105515 52.418923 1.0000000
## M248_s+noacc_sh-M248_s+noacc_ro -14.5702530 -71.593698 42.453192 0.9990345P9 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P9)
stat.test## la_s+1acc_sh la_s+mock_ro la_s+mock_sh la_s+noacc_ro la_s+noacc_sh
## "a" "ab" "ab" "b" "ab"
## M248_s+1acc_ro M248_s+1acc_sh M248_s+mock_ro M248_s+mock_sh M248_s+noacc_ro
## "ab" "a" "ab" "a" "ab"
## M248_s+noacc_sh la_s+1acc_ro
## "a" "ab"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+1acc"test$info[[1]][3]## [1] "sh"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_ACC_10dICP_ACC_10d$All.condition <- factor(ICP_ACC_10d$All.condition, levels = c("s+noacc", "s+mock", "s+1acc"))
ICP_ACC_10d <- subset(ICP_ACC_10d, ICP_ACC_10d$K.con..mg.mg.dry.weight < 65)
k_content_ACC_10d <- ggplot(data = ICP_ACC_10d, mapping = aes(x = All.condition, y = K.con..mg.mg.dry.weight, colour = All.condition))
k_content_ACC_10d <- k_content_ACC_10d + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
k_content_ACC_10d <- k_content_ACC_10d + facet_grid(Tissue ~ Accession, scales = "free_y")
k_content_ACC_10d <- k_content_ACC_10d + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
k_content_ACC_10d <- k_content_ACC_10d + scale_color_manual(values = c("red","blueviolet","cyan"))
k_content_ACC_10d <- k_content_ACC_10d + ylab("K content, mg/mg dry weight") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 70)
k_content_ACC_10d <- k_content_ACC_10d + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
k_content_ACC_10d <- k_content_ACC_10d + rremove("legend")
k_content_ACC_10d
aov(Na.K.ratio ~ All.ID2, data = ICP_ACC_10d)## Call:
## aov(formula = Na.K.ratio ~ All.ID2, data = ICP_ACC_10d)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 2.216001 1.234887
## Deg. of Freedom 11 45
##
## Residual standard error: 0.1656561
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(Na.K.ratio~ All.ID2, data = ICP_ACC_10d))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.K.ratio ~ All.ID2, data = ICP_ACC_10d)
##
## $All.ID2
## diff lwr upr
## la_s+1acc_sh-la_s+1acc_ro 0.363914602 -0.018911967 0.746741171
## la_s+mock_ro-la_s+1acc_ro -0.004321774 -0.387148343 0.378504795
## la_s+mock_sh-la_s+1acc_ro 0.254684088 -0.128142481 0.637510657
## la_s+noacc_ro-la_s+1acc_ro 0.077525745 -0.326008891 0.481060380
## la_s+noacc_sh-la_s+1acc_ro 0.167372321 -0.215454248 0.550198890
## M248_s+1acc_ro-la_s+1acc_ro 0.125075937 -0.257750631 0.507902506
## M248_s+1acc_sh-la_s+1acc_ro 0.557953315 0.175126746 0.940779883
## M248_s+mock_ro-la_s+1acc_ro 0.164321868 -0.218504701 0.547148437
## M248_s+mock_sh-la_s+1acc_ro 0.498132545 0.115305976 0.880959114
## M248_s+noacc_ro-la_s+1acc_ro 0.195913728 -0.207620908 0.599448363
## M248_s+noacc_sh-la_s+1acc_ro 0.571881433 0.189054865 0.954708002
## la_s+mock_ro-la_s+1acc_sh -0.368236376 -0.729168726 -0.007304025
## la_s+mock_sh-la_s+1acc_sh -0.109230514 -0.470162864 0.251701837
## la_s+noacc_ro-la_s+1acc_sh -0.286388857 -0.669215426 0.096437711
## la_s+noacc_sh-la_s+1acc_sh -0.196542281 -0.557474631 0.164390069
## M248_s+1acc_ro-la_s+1acc_sh -0.238838664 -0.599771015 0.122093686
## M248_s+1acc_sh-la_s+1acc_sh 0.194038713 -0.166893638 0.554971063
## M248_s+mock_ro-la_s+1acc_sh -0.199592734 -0.560525084 0.161339617
## M248_s+mock_sh-la_s+1acc_sh 0.134217943 -0.226714407 0.495150294
## M248_s+noacc_ro-la_s+1acc_sh -0.168000874 -0.550827443 0.214825695
## M248_s+noacc_sh-la_s+1acc_sh 0.207966832 -0.152965519 0.568899182
## la_s+mock_sh-la_s+mock_ro 0.259005862 -0.101926488 0.619938212
## la_s+noacc_ro-la_s+mock_ro 0.081847518 -0.300979050 0.464674087
## la_s+noacc_sh-la_s+mock_ro 0.171694095 -0.189238256 0.532626445
## M248_s+1acc_ro-la_s+mock_ro 0.129397711 -0.231534639 0.490330062
## M248_s+1acc_sh-la_s+mock_ro 0.562275089 0.201342738 0.923207439
## M248_s+mock_ro-la_s+mock_ro 0.168643642 -0.192288708 0.529575992
## M248_s+mock_sh-la_s+mock_ro 0.502454319 0.141521969 0.863386670
## M248_s+noacc_ro-la_s+mock_ro 0.200235502 -0.182591067 0.583062070
## M248_s+noacc_sh-la_s+mock_ro 0.576203207 0.215270857 0.937135558
## la_s+noacc_ro-la_s+mock_sh -0.177158344 -0.559984912 0.205668225
## la_s+noacc_sh-la_s+mock_sh -0.087311767 -0.448244118 0.273620583
## M248_s+1acc_ro-la_s+mock_sh -0.129608151 -0.490540501 0.231324200
## M248_s+1acc_sh-la_s+mock_sh 0.303269227 -0.057663124 0.664201577
## M248_s+mock_ro-la_s+mock_sh -0.090362220 -0.451294570 0.270570130
## M248_s+mock_sh-la_s+mock_sh 0.243448457 -0.117483893 0.604380808
## M248_s+noacc_ro-la_s+mock_sh -0.058770360 -0.441596929 0.324056208
## M248_s+noacc_sh-la_s+mock_sh 0.317197345 -0.043735005 0.678129696
## la_s+noacc_sh-la_s+noacc_ro 0.089846576 -0.292979992 0.472673145
## M248_s+1acc_ro-la_s+noacc_ro 0.047550193 -0.335276376 0.430376762
## M248_s+1acc_sh-la_s+noacc_ro 0.480427570 0.097601001 0.863254139
## M248_s+mock_ro-la_s+noacc_ro 0.086796124 -0.296030445 0.469622692
## M248_s+mock_sh-la_s+noacc_ro 0.420606801 0.037780232 0.803433370
## M248_s+noacc_ro-la_s+noacc_ro 0.118387983 -0.285146652 0.521922619
## M248_s+noacc_sh-la_s+noacc_ro 0.494355689 0.111529120 0.877182258
## M248_s+1acc_ro-la_s+noacc_sh -0.042296383 -0.403228734 0.318635967
## M248_s+1acc_sh-la_s+noacc_sh 0.390580994 0.029648643 0.751513344
## M248_s+mock_ro-la_s+noacc_sh -0.003050453 -0.363982803 0.357881898
## M248_s+mock_sh-la_s+noacc_sh 0.330760224 -0.030172126 0.691692575
## M248_s+noacc_ro-la_s+noacc_sh 0.028541407 -0.354285162 0.411367976
## M248_s+noacc_sh-la_s+noacc_sh 0.404509113 0.043576762 0.765441463
## M248_s+1acc_sh-M248_s+1acc_ro 0.432877377 0.071945027 0.793809728
## M248_s+mock_ro-M248_s+1acc_ro 0.039245931 -0.321686420 0.400178281
## M248_s+mock_sh-M248_s+1acc_ro 0.373056608 0.012124257 0.733988958
## M248_s+noacc_ro-M248_s+1acc_ro 0.070837790 -0.311988778 0.453664359
## M248_s+noacc_sh-M248_s+1acc_ro 0.446805496 0.085873146 0.807737846
## M248_s+mock_ro-M248_s+1acc_sh -0.393631447 -0.754563797 -0.032699096
## M248_s+mock_sh-M248_s+1acc_sh -0.059820769 -0.420753120 0.301111581
## M248_s+noacc_ro-M248_s+1acc_sh -0.362039587 -0.744866156 0.020786982
## M248_s+noacc_sh-M248_s+1acc_sh 0.013928119 -0.347004232 0.374860469
## M248_s+mock_sh-M248_s+mock_ro 0.333810677 -0.027121673 0.694743028
## M248_s+noacc_ro-M248_s+mock_ro 0.031591860 -0.351234709 0.414418428
## M248_s+noacc_sh-M248_s+mock_ro 0.407559565 0.046627215 0.768491916
## M248_s+noacc_ro-M248_s+mock_sh -0.302218818 -0.685045386 0.080607751
## M248_s+noacc_sh-M248_s+mock_sh 0.073748888 -0.287183462 0.434681239
## M248_s+noacc_sh-M248_s+noacc_ro 0.375967706 -0.006858863 0.758794274
## p adj
## la_s+1acc_sh-la_s+1acc_ro 0.0760597
## la_s+mock_ro-la_s+1acc_ro 1.0000000
## la_s+mock_sh-la_s+1acc_ro 0.4967157
## la_s+noacc_ro-la_s+1acc_ro 0.9999354
## la_s+noacc_sh-la_s+1acc_ro 0.9311191
## M248_s+1acc_ro-la_s+1acc_ro 0.9917639
## M248_s+1acc_sh-la_s+1acc_ro 0.0004828
## M248_s+mock_ro-la_s+1acc_ro 0.9388064
## M248_s+mock_sh-la_s+1acc_ro 0.0026412
## M248_s+noacc_ro-la_s+1acc_ro 0.8709826
## M248_s+noacc_sh-la_s+1acc_ro 0.0003216
## la_s+mock_ro-la_s+1acc_sh 0.0418433
## la_s+mock_sh-la_s+1acc_sh 0.9956354
## la_s+noacc_ro-la_s+1acc_sh 0.3222796
## la_s+noacc_sh-la_s+1acc_sh 0.7670985
## M248_s+1acc_ro-la_s+1acc_sh 0.5048044
## M248_s+1acc_sh-la_s+1acc_sh 0.7808194
## M248_s+mock_ro-la_s+1acc_sh 0.7499212
## M248_s+mock_sh-la_s+1acc_sh 0.9774791
## M248_s+noacc_ro-la_s+1acc_sh 0.9294583
## M248_s+noacc_sh-la_s+1acc_sh 0.7005201
## la_s+mock_sh-la_s+mock_ro 0.3825014
## la_s+noacc_ro-la_s+mock_ro 0.9998162
## la_s+noacc_sh-la_s+mock_ro 0.8850824
## M248_s+1acc_ro-la_s+mock_ro 0.9828932
## M248_s+1acc_sh-la_s+mock_ro 0.0001562
## M248_s+mock_ro-la_s+mock_ro 0.8964703
## M248_s+mock_sh-la_s+mock_ro 0.0009926
## M248_s+noacc_ro-la_s+mock_ro 0.8084111
## M248_s+noacc_sh-la_s+mock_ro 0.0001006
## la_s+noacc_ro-la_s+mock_sh 0.9022145
## la_s+noacc_sh-la_s+mock_sh 0.9994069
## M248_s+1acc_ro-la_s+mock_sh 0.9826801
## M248_s+1acc_sh-la_s+mock_sh 0.1775906
## M248_s+mock_ro-la_s+mock_sh 0.9991852
## M248_s+mock_sh-la_s+mock_sh 0.4758254
## M248_s+noacc_ro-la_s+mock_sh 0.9999933
## M248_s+noacc_sh-la_s+mock_sh 0.1340498
## la_s+noacc_sh-la_s+noacc_ro 0.9995532
## M248_s+1acc_ro-la_s+noacc_ro 0.9999993
## M248_s+1acc_sh-la_s+noacc_ro 0.0042902
## M248_s+mock_ro-la_s+noacc_ro 0.9996776
## M248_s+mock_sh-la_s+noacc_ro 0.0203441
## M248_s+noacc_ro-la_s+noacc_ro 0.9966520
## M248_s+noacc_sh-la_s+noacc_ro 0.0029315
## M248_s+1acc_ro-la_s+noacc_sh 0.9999996
## M248_s+1acc_sh-la_s+noacc_sh 0.0237760
## M248_s+mock_ro-la_s+noacc_sh 1.0000000
## M248_s+mock_sh-la_s+noacc_sh 0.1003043
## M248_s+noacc_ro-la_s+noacc_sh 1.0000000
## M248_s+noacc_sh-la_s+noacc_sh 0.0164794
## M248_s+1acc_sh-M248_s+1acc_ro 0.0075847
## M248_s+mock_ro-M248_s+1acc_ro 0.9999998
## M248_s+mock_sh-M248_s+1acc_ro 0.0371341
## M248_s+noacc_ro-M248_s+1acc_ro 0.9999555
## M248_s+noacc_sh-M248_s+1acc_ro 0.0051163
## M248_s+mock_ro-M248_s+1acc_sh 0.0219612
## M248_s+mock_sh-M248_s+1acc_sh 0.9999853
## M248_s+noacc_ro-M248_s+1acc_sh 0.0791911
## M248_s+noacc_sh-M248_s+1acc_sh 1.0000000
## M248_s+mock_sh-M248_s+mock_ro 0.0937769
## M248_s+noacc_ro-M248_s+mock_ro 1.0000000
## M248_s+noacc_sh-M248_s+mock_ro 0.0151878
## M248_s+noacc_ro-M248_s+mock_sh 0.2501203
## M248_s+noacc_sh-M248_s+mock_sh 0.9998816
## M248_s+noacc_sh-M248_s+noacc_ro 0.0583661P11 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P11)
stat.test## la_s+1acc_sh la_s+mock_ro la_s+mock_sh la_s+noacc_ro la_s+noacc_sh
## "abc" "d" "abcd" "ad" "abd"
## M248_s+1acc_ro M248_s+1acc_sh M248_s+mock_ro M248_s+mock_sh M248_s+noacc_ro
## "ad" "c" "abd" "bc" "abcd"
## M248_s+noacc_sh la_s+1acc_ro
## "c" "ad"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+1acc"test$info[[1]][3]## [1] "sh"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_ACC_10dICP_ACC_10d$All.condition <- factor(ICP_ACC_10d$All.condition, levels = c("s+noacc", "s+mock", "s+1acc"))
ICP_ACC_10d <- subset(ICP_ACC_10d, ICP_ACC_10d$Na.K.ratio < 1)
ratio_content_ACC_10d <- ggplot(data = ICP_ACC_10d, mapping = aes(x = All.condition, y = Na.K.ratio, colour = All.condition))
ratio_content_ACC_10d <- ratio_content_ACC_10d + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
ratio_content_ACC_10d <- ratio_content_ACC_10d + facet_grid(Tissue ~ Accession, scales = "free_y")
ratio_content_ACC_10d <- ratio_content_ACC_10d + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
ratio_content_ACC_10d <- ratio_content_ACC_10d + scale_color_manual(values = c("red","blueviolet","cyan"))
ratio_content_ACC_10d <- ratio_content_ACC_10d + ylab("Na/K ratio") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 1.3)
ratio_content_ACC_10d <- ratio_content_ACC_10d + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
ratio_content_ACC_10d <- ratio_content_ACC_10d + rremove("legend")
ratio_content_ACC_10d
#I want to graph Na/K ratio for each developmental stage compared to
only one control group, noacc / and or noiaa…the rest can go to sup.
ICP_ACC_10dnoacc_sub <- subset(ICP_ACC_10d, ICP_ACC_10d$Condition2 %in% c("1acc", "noacc"))
noacc_subOutput <- TukeyHSD(aov(Na.K.ratio ~ All.ID2, data = noacc_sub))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.K.ratio ~ All.ID2, data = noacc_sub)
##
## $All.ID2
## diff lwr upr p adj
## la_s+1acc_sh-la_s+1acc_ro 0.18428606 0.0652638379 0.30330829 0.0005748
## la_s+noacc_ro-la_s+1acc_ro 0.07752574 -0.0414964803 0.19654797 0.4187326
## la_s+noacc_sh-la_s+1acc_ro 0.16737232 0.0544579241 0.28028672 0.0010102
## M248_s+1acc_ro-la_s+1acc_ro 0.12507594 0.0121615407 0.23799033 0.0221558
## M248_s+1acc_sh-la_s+1acc_ro 0.41210474 0.2930825137 0.53112696 0.0000000
## M248_s+noacc_ro-la_s+1acc_ro 0.19591373 0.0768915029 0.31493595 0.0002490
## M248_s+noacc_sh-la_s+1acc_ro 0.57188143 0.4589670367 0.68479583 0.0000000
## la_s+noacc_ro-la_s+1acc_sh -0.10676032 -0.2257825431 0.01226191 0.1030777
## la_s+noacc_sh-la_s+1acc_sh -0.01691374 -0.1298281387 0.09600065 0.9996134
## M248_s+1acc_ro-la_s+1acc_sh -0.05921013 -0.1721245221 0.05370427 0.6752002
## M248_s+1acc_sh-la_s+1acc_sh 0.22781868 0.1087964509 0.34684090 0.0000254
## M248_s+noacc_ro-la_s+1acc_sh 0.01162766 -0.1073945598 0.13064989 0.9999780
## M248_s+noacc_sh-la_s+1acc_sh 0.38759537 0.2746809739 0.50050977 0.0000000
## la_s+noacc_sh-la_s+noacc_ro 0.08984658 -0.0230678204 0.20276097 0.1958903
## M248_s+1acc_ro-la_s+noacc_ro 0.04755019 -0.0653642038 0.16046459 0.8578627
## M248_s+1acc_sh-la_s+noacc_ro 0.33457899 0.2155567692 0.45360122 0.0000000
## M248_s+noacc_ro-la_s+noacc_ro 0.11838798 -0.0006342416 0.23741021 0.0519823
## M248_s+noacc_sh-la_s+noacc_ro 0.49435569 0.3814412922 0.60727009 0.0000000
## M248_s+1acc_ro-la_s+noacc_sh -0.04229638 -0.1487530976 0.06416033 0.8898718
## M248_s+1acc_sh-la_s+noacc_sh 0.24473242 0.1318180209 0.35764681 0.0000031
## M248_s+noacc_ro-la_s+noacc_sh 0.02854141 -0.0843729899 0.14145580 0.9897405
## M248_s+noacc_sh-la_s+noacc_sh 0.40450911 0.2980523984 0.51096583 0.0000000
## M248_s+1acc_sh-M248_s+1acc_ro 0.28702880 0.1741144043 0.39994320 0.0000002
## M248_s+noacc_ro-M248_s+1acc_ro 0.07083779 -0.0420766065 0.18375219 0.4651490
## M248_s+noacc_sh-M248_s+1acc_ro 0.44680550 0.3403487818 0.55326221 0.0000000
## M248_s+noacc_ro-M248_s+1acc_sh -0.21619101 -0.3352132356 -0.09716879 0.0000581
## M248_s+noacc_sh-M248_s+1acc_sh 0.15977669 0.0468622982 0.27269109 0.0017888
## M248_s+noacc_sh-M248_s+noacc_ro 0.37596771 0.2630533089 0.48888210 0.0000000P11 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P11)
stat.test## la_s+1acc_sh la_s+noacc_ro la_s+noacc_sh M248_s+1acc_ro M248_s+1acc_sh
## "a" "ab" "a" "a" "c"
## M248_s+noacc_ro M248_s+noacc_sh la_s+1acc_ro
## "a" "d" "b"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+1acc"test$info[[1]][3]## [1] "sh"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ratio_noacc_ACC_10d <- ggplot(data = noacc_sub, mapping = aes(x = All.condition, y = Na.K.ratio, colour = All.condition))
ratio_noacc_ACC_10d <- ratio_noacc_ACC_10d + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
ratio_noacc_ACC_10d <- ratio_noacc_ACC_10d + facet_grid(Tissue ~ Accession, scales = "free_y")
ratio_noacc_ACC_10d <- ratio_noacc_ACC_10d + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
ratio_noacc_ACC_10d <- ratio_noacc_ACC_10d + scale_color_manual(values = c("red","blueviolet","cyan"))
ratio_noacc_ACC_10d <- ratio_noacc_ACC_10d + ylab("Na/K ratio") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 1.3)
ratio_noacc_ACC_10d <- ratio_noacc_ACC_10d + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
ratio_noacc_ACC_10d <- ratio_noacc_ACC_10d + rremove("legend")
ratio_noacc_ACC_10d
#now, lets calculate Na, K , and the ratio for ACC -4weeks spray.
ICP_ACC_4w <- read.csv("ICP-ACC-SOIL-SPAR-SALT-4weeks.csv")
ICP_ACC_4wICP_ACC_4w$All.condition<-paste(ICP_ACC_4w$Condition1,ICP_ACC_4w$Condition2, sep="+")
ICP_ACC_4wICP_ACC_4w$All.ID2<-paste(ICP_ACC_4w$Accession,ICP_ACC_4w$All.condition, ICP_ACC_4w$Tissue,sep="_")
ICP_ACC_4waov(Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_4w)## Call:
## aov(formula = Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_4w)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 210.36375 33.10972
## Deg. of Freedom 11 48
##
## Residual standard error: 0.8305335
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_4w))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_4w)
##
## $All.ID2
## diff lwr upr p adj
## la_s+1acc_tip-la_s+1acc_ro -1.74132554 -3.544980426 0.06232935 0.0673737
## la_s+mock_ro-la_s+1acc_ro -0.77211847 -2.575773359 1.03153642 0.9415882
## la_s+mock_tip-la_s+1acc_ro -1.67410810 -3.477762985 0.12954679 0.0917335
## la_s+noacc_ro-la_s+1acc_ro 1.34564591 -0.458008977 3.14930080 0.3291041
## la_s+noacc_tip-la_s+1acc_ro 0.28614793 -1.517506961 2.08980282 0.9999911
## M248_s+1acc_ro-la_s+1acc_ro 3.68154342 1.877888534 5.48519831 0.0000004
## M248_s+1acc_tip-la_s+1acc_ro 1.79977523 -0.003879655 3.60343012 0.0509532
## M248_s+mock_ro-la_s+1acc_ro 2.81507930 1.011424411 4.61873419 0.0001377
## M248_s+mock_tip-la_s+1acc_ro 0.52101449 -1.282640401 2.32466937 0.9972019
## M248_s+noacc_ro-la_s+1acc_ro 4.27207005 2.468415161 6.07572494 0.0000000
## M248_s+noacc_tip-la_s+1acc_ro 1.76438083 -0.039274054 3.56803572 0.0604154
## la_s+mock_ro-la_s+1acc_tip 0.96920707 -0.834447820 2.77286196 0.7851692
## la_s+mock_tip-la_s+1acc_tip 0.06721744 -1.736437447 1.87087233 1.0000000
## la_s+noacc_ro-la_s+1acc_tip 3.08697145 1.283316561 4.89062634 0.0000234
## la_s+noacc_tip-la_s+1acc_tip 2.02747347 0.223818578 3.83112835 0.0157662
## M248_s+1acc_ro-la_s+1acc_tip 5.42286896 3.619214072 7.22652385 0.0000000
## M248_s+1acc_tip-la_s+1acc_tip 3.54110077 1.737445883 5.34475566 0.0000011
## M248_s+mock_ro-la_s+1acc_tip 4.55640484 2.752749949 6.36005973 0.0000000
## M248_s+mock_tip-la_s+1acc_tip 2.26234003 0.458685138 4.06599491 0.0041922
## M248_s+noacc_ro-la_s+1acc_tip 6.01339559 4.209740699 7.81705047 0.0000000
## M248_s+noacc_tip-la_s+1acc_tip 3.50570637 1.702051484 5.30936126 0.0000015
## la_s+mock_tip-la_s+mock_ro -0.90198963 -2.705644514 0.90166526 0.8514406
## la_s+noacc_ro-la_s+mock_ro 2.11776438 0.314109494 3.92141927 0.0095891
## la_s+noacc_tip-la_s+mock_ro 1.05826640 -0.745388490 2.86192129 0.6815655
## M248_s+1acc_ro-la_s+mock_ro 4.45366189 2.650007005 6.25731678 0.0000000
## M248_s+1acc_tip-la_s+mock_ro 2.57189370 0.768238816 4.37554859 0.0006437
## M248_s+mock_ro-la_s+mock_ro 3.58719777 1.783542882 5.39085266 0.0000008
## M248_s+mock_tip-la_s+mock_ro 1.29313296 -0.510521930 3.09678785 0.3873325
## M248_s+noacc_ro-la_s+mock_ro 5.04418852 3.240533632 6.84784341 0.0000000
## M248_s+noacc_tip-la_s+mock_ro 2.53649930 0.732844417 4.34015419 0.0008022
## la_s+noacc_ro-la_s+mock_tip 3.01975401 1.216099120 4.82340890 0.0000364
## la_s+noacc_tip-la_s+mock_tip 1.96025602 0.156601137 3.76391091 0.0225784
## M248_s+1acc_ro-la_s+mock_tip 5.35565152 3.551996631 7.15930641 0.0000000
## M248_s+1acc_tip-la_s+mock_tip 3.47388333 1.670228442 5.27753822 0.0000018
## M248_s+mock_ro-la_s+mock_tip 4.48918740 2.685532508 6.29284228 0.0000000
## M248_s+mock_tip-la_s+mock_tip 2.19512258 0.391467697 3.99877747 0.0061864
## M248_s+noacc_ro-la_s+mock_tip 5.94617815 4.142523258 7.74983303 0.0000000
## M248_s+noacc_tip-la_s+mock_tip 3.43848893 1.634834043 5.24214382 0.0000023
## la_s+noacc_tip-la_s+noacc_ro -1.05949798 -2.863152871 0.74415690 0.6800419
## M248_s+1acc_ro-la_s+noacc_ro 2.33589751 0.532242623 4.13955240 0.0027166
## M248_s+1acc_tip-la_s+noacc_ro 0.45412932 -1.349525566 2.25778421 0.9991833
## M248_s+mock_ro-la_s+noacc_ro 1.46943339 -0.334221500 3.27308828 0.2136521
## M248_s+mock_tip-la_s+noacc_ro -0.82463142 -2.628286311 0.97902346 0.9113872
## M248_s+noacc_ro-la_s+noacc_ro 2.92642414 1.122769250 4.73007903 0.0000670
## M248_s+noacc_tip-la_s+noacc_ro 0.41873492 -1.384919965 2.22238981 0.9996170
## M248_s+1acc_ro-la_s+noacc_tip 3.39539549 1.591740607 5.19905038 0.0000030
## M248_s+1acc_tip-la_s+noacc_tip 1.51362731 -0.290027582 3.31728219 0.1803400
## M248_s+mock_ro-la_s+noacc_tip 2.52893137 0.725276484 4.33258626 0.0008407
## M248_s+mock_tip-la_s+noacc_tip 0.23486656 -1.568788328 2.03852145 0.9999989
## M248_s+noacc_ro-la_s+noacc_tip 3.98592212 2.182267233 5.78957701 0.0000001
## M248_s+noacc_tip-la_s+noacc_tip 1.47823291 -0.325421981 3.28188779 0.2066883
## M248_s+1acc_tip-M248_s+1acc_ro -1.88176819 -3.685423077 -0.07811330 0.0338922
## M248_s+mock_ro-M248_s+1acc_ro -0.86646412 -2.670119011 0.93719076 0.8812544
## M248_s+mock_tip-M248_s+1acc_ro -3.16052893 -4.964183823 -1.35687405 0.0000144
## M248_s+noacc_ro-M248_s+1acc_ro 0.59052663 -1.213128261 2.39418151 0.9919587
## M248_s+noacc_tip-M248_s+1acc_ro -1.91716259 -3.720817476 -0.11350770 0.0282713
## M248_s+mock_ro-M248_s+1acc_tip 1.01530407 -0.788350822 2.81895895 0.7333585
## M248_s+mock_tip-M248_s+1acc_tip -1.27876075 -3.082415634 0.52489414 0.4040919
## M248_s+noacc_ro-M248_s+1acc_tip 2.47229482 0.668639928 4.27594970 0.0011916
## M248_s+noacc_tip-M248_s+1acc_tip -0.03539440 -1.839049287 1.76826049 1.0000000
## M248_s+mock_tip-M248_s+mock_ro -2.29406481 -4.097719700 -0.49040992 0.0034802
## M248_s+noacc_ro-M248_s+mock_ro 1.45699075 -0.346664138 3.26064564 0.2237827
## M248_s+noacc_tip-M248_s+mock_ro -1.05069847 -2.854353353 0.75295642 0.6908858
## M248_s+noacc_ro-M248_s+mock_tip 3.75105556 1.947400674 5.55471045 0.0000003
## M248_s+noacc_tip-M248_s+mock_tip 1.24336635 -0.560288541 3.04702123 0.4466500
## M248_s+noacc_tip-M248_s+noacc_ro -2.50768921 -4.311344103 -0.70403433 0.0009586P10= Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P10)
stat.test## la_s+1acc_tip la_s+mock_ro la_s+mock_tip la_s+noacc_ro
## "a" "ab" "a" "cd"
## la_s+noacc_tip M248_s+1acc_ro M248_s+1acc_tip M248_s+mock_ro
## "bc" "e" "cd" "de"
## M248_s+mock_tip M248_s+noacc_ro M248_s+noacc_tip la_s+1acc_ro
## "bc" "e" "cd" "abc"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+1acc"test$info[[1]][3]## [1] "tip"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_ACC_4wICP_ACC_4w$All.condition <- factor(ICP_ACC_4w$All.condition, levels = c("s+noacc", "s+mock", "s+1acc"))
Na_content_ACC_4w <- ggplot(data = ICP_ACC_4w, mapping = aes(x = All.condition, y = Na.con.mg.mg.dry.weight, colour = All.condition))
Na_content_ACC_4w <- Na_content_ACC_4w + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Na_content_ACC_4w <- Na_content_ACC_4w + facet_grid(Tissue ~ Accession, scales = "free_y")
Na_content_ACC_4w <- Na_content_ACC_4w + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Na_content_ACC_4w <- Na_content_ACC_4w+ scale_color_manual(values = c("red","blueviolet","cyan"))
Na_content_ACC_4w<- Na_content_ACC_4w + ylab("Na content, mg/mg dry weight") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 10)
Na_content_ACC_4w <- Na_content_ACC_4w + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Na_content_ACC_4w <- Na_content_ACC_4w + rremove("legend")
Na_content_ACC_4w
aov(K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_4w)## Call:
## aov(formula = K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_4w)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 252.8220 842.1601
## Deg. of Freedom 11 48
##
## Residual standard error: 4.188675
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(K.con..mg.mg.dry.weight~ All.ID2, data = ICP_ACC_4w))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_ACC_4w)
##
## $All.ID2
## diff lwr upr p adj
## la_s+1acc_tip-la_s+1acc_ro 3.14243354 -5.954039 12.2389060 0.9876437
## la_s+mock_ro-la_s+1acc_ro 1.87786380 -7.218609 10.9743363 0.9998761
## la_s+mock_tip-la_s+1acc_ro 0.99649450 -8.099978 10.0929670 0.9999998
## la_s+noacc_ro-la_s+1acc_ro 3.05045777 -6.046015 12.1469302 0.9902409
## la_s+noacc_tip-la_s+1acc_ro 6.27996128 -2.816511 15.3764337 0.4444107
## M248_s+1acc_ro-la_s+1acc_ro 1.67475517 -7.421717 10.7712276 0.9999600
## M248_s+1acc_tip-la_s+1acc_ro -0.02430253 -9.120775 9.0721699 1.0000000
## M248_s+mock_ro-la_s+1acc_ro 2.29399905 -6.802473 11.3904715 0.9991712
## M248_s+mock_tip-la_s+1acc_ro -0.22752525 -9.323998 8.8689472 1.0000000
## M248_s+noacc_ro-la_s+1acc_ro -0.24283164 -9.339304 8.8536408 1.0000000
## M248_s+noacc_tip-la_s+1acc_ro -1.82355057 -10.920023 7.2729219 0.9999070
## la_s+mock_ro-la_s+1acc_tip -1.26456974 -10.361042 7.8319027 0.9999977
## la_s+mock_tip-la_s+1acc_tip -2.14593904 -11.242412 6.9505334 0.9995545
## la_s+noacc_ro-la_s+1acc_tip -0.09197577 -9.188448 9.0044967 1.0000000
## la_s+noacc_tip-la_s+1acc_tip 3.13752774 -5.958945 12.2340002 0.9877947
## M248_s+1acc_ro-la_s+1acc_tip -1.46767837 -10.564151 7.6287941 0.9999895
## M248_s+1acc_tip-la_s+1acc_tip -3.16673606 -12.263209 5.9297364 0.9868738
## M248_s+mock_ro-la_s+1acc_tip -0.84843449 -9.944907 8.2480380 1.0000000
## M248_s+mock_tip-la_s+1acc_tip -3.36995879 -12.466431 5.7265137 0.9788699
## M248_s+noacc_ro-la_s+1acc_tip -3.38526518 -12.481738 5.7112073 0.9781428
## M248_s+noacc_tip-la_s+1acc_tip -4.96598411 -14.062457 4.1304884 0.7683100
## la_s+mock_tip-la_s+mock_ro -0.88136930 -9.977842 8.2151032 1.0000000
## la_s+noacc_ro-la_s+mock_ro 1.17259397 -7.923879 10.2690664 0.9999990
## la_s+noacc_tip-la_s+mock_ro 4.40209747 -4.694375 13.4985699 0.8761769
## M248_s+1acc_ro-la_s+mock_ro -0.20310863 -9.299581 8.8933638 1.0000000
## M248_s+1acc_tip-la_s+mock_ro -1.90216633 -10.998639 7.1943061 0.9998596
## M248_s+mock_ro-la_s+mock_ro 0.41613524 -8.680337 9.5126077 1.0000000
## M248_s+mock_tip-la_s+mock_ro -2.10538906 -11.201862 6.9910834 0.9996279
## M248_s+noacc_ro-la_s+mock_ro -2.12069545 -11.217168 6.9757770 0.9996015
## M248_s+noacc_tip-la_s+mock_ro -3.70141437 -12.797887 5.3950581 0.9584960
## la_s+noacc_ro-la_s+mock_tip 2.05396327 -7.042509 11.1504357 0.9997058
## la_s+noacc_tip-la_s+mock_tip 5.28346678 -3.813006 14.3799392 0.6946683
## M248_s+1acc_ro-la_s+mock_tip 0.67826067 -8.418212 9.7747331 1.0000000
## M248_s+1acc_tip-la_s+mock_tip -1.02079703 -10.117269 8.0756754 0.9999998
## M248_s+mock_ro-la_s+mock_tip 1.29750455 -7.798968 10.3939770 0.9999970
## M248_s+mock_tip-la_s+mock_tip -1.22401975 -10.320492 7.8724527 0.9999984
## M248_s+noacc_ro-la_s+mock_tip -1.23932614 -10.335799 7.8571463 0.9999982
## M248_s+noacc_tip-la_s+mock_tip -2.82004507 -11.916518 6.2764274 0.9948797
## la_s+noacc_tip-la_s+noacc_ro 3.22950351 -5.866969 12.3259760 0.9847107
## M248_s+1acc_ro-la_s+noacc_ro -1.37570260 -10.472175 7.7207699 0.9999946
## M248_s+1acc_tip-la_s+noacc_ro -3.07476029 -12.171233 6.0217122 0.9896013
## M248_s+mock_ro-la_s+noacc_ro -0.75645872 -9.852931 8.3400138 1.0000000
## M248_s+mock_tip-la_s+noacc_ro -3.27798302 -12.374455 5.8184895 0.9828577
## M248_s+noacc_ro-la_s+noacc_ro -3.29328941 -12.389762 5.8031831 0.9822379
## M248_s+noacc_tip-la_s+noacc_ro -4.87400834 -13.970481 4.2224641 0.7881490
## M248_s+1acc_ro-la_s+noacc_tip -4.60520611 -13.701679 4.4912664 0.8413236
## M248_s+1acc_tip-la_s+noacc_tip -6.30426380 -15.400736 2.7922087 0.4385297
## M248_s+mock_ro-la_s+noacc_tip -3.98596223 -13.082435 5.1105102 0.9320516
## M248_s+mock_tip-la_s+noacc_tip -6.50748653 -15.603959 2.5889859 0.3905997
## M248_s+noacc_ro-la_s+noacc_tip -6.52279292 -15.619265 2.5736796 0.3870890
## M248_s+noacc_tip-la_s+noacc_tip -8.10351185 -17.199984 0.9929606 0.1231622
## M248_s+1acc_tip-M248_s+1acc_ro -1.69905769 -10.795530 7.3974148 0.9999538
## M248_s+mock_ro-M248_s+1acc_ro 0.61924388 -8.477229 9.7157164 1.0000000
## M248_s+mock_tip-M248_s+1acc_ro -1.90228042 -10.998753 7.1941921 0.9998595
## M248_s+noacc_ro-M248_s+1acc_ro -1.91758681 -11.014059 7.1788857 0.9998481
## M248_s+noacc_tip-M248_s+1acc_ro -3.49830574 -12.594778 5.5981667 0.9721697
## M248_s+mock_ro-M248_s+1acc_tip 2.31830157 -6.778171 11.4147740 0.9990870
## M248_s+mock_tip-M248_s+1acc_tip -0.20322273 -9.299695 8.8932497 1.0000000
## M248_s+noacc_ro-M248_s+1acc_tip -0.21852912 -9.315002 8.8779434 1.0000000
## M248_s+noacc_tip-M248_s+1acc_tip -1.79924804 -10.895721 7.2972244 0.9999185
## M248_s+mock_tip-M248_s+mock_ro -2.52152430 -11.617997 6.5749482 0.9980520
## M248_s+noacc_ro-M248_s+mock_ro -2.53683069 -11.633303 6.5596418 0.9979448
## M248_s+noacc_tip-M248_s+mock_ro -4.11754962 -13.214022 4.9789229 0.9166573
## M248_s+noacc_ro-M248_s+mock_tip -0.01530639 -9.111779 9.0811661 1.0000000
## M248_s+noacc_tip-M248_s+mock_tip -1.59602532 -10.692498 7.5004472 0.9999754
## M248_s+noacc_tip-M248_s+noacc_ro -1.58071893 -10.677191 7.5157535 0.9999776P9 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P9)
stat.test## $Letters
## la_s+1acc_tip la_s+mock_ro la_s+mock_tip la_s+noacc_ro
## "a" "a" "a" "a"
## la_s+noacc_tip M248_s+1acc_ro M248_s+1acc_tip M248_s+mock_ro
## "a" "a" "a" "a"
## M248_s+mock_tip M248_s+noacc_ro M248_s+noacc_tip la_s+1acc_ro
## "a" "a" "a" "a"
##
## $LetterMatrix
## a
## la_s+1acc_tip TRUE
## la_s+mock_ro TRUE
## la_s+mock_tip TRUE
## la_s+noacc_ro TRUE
## la_s+noacc_tip TRUE
## M248_s+1acc_ro TRUE
## M248_s+1acc_tip TRUE
## M248_s+mock_ro TRUE
## M248_s+mock_tip TRUE
## M248_s+noacc_ro TRUE
## M248_s+noacc_tip TRUE
## la_s+1acc_ro TRUEtest <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+1acc"test$info[[1]][3]## [1] "tip"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_ACC_4wICP_ACC_4w$All.condition <- factor(ICP_ACC_4w$All.condition, levels = c("s+noacc", "s+mock", "s+1acc"))
k_content_ACC_4w <- ggplot(data = ICP_ACC_4w, mapping = aes(x = All.condition, y = K.con..mg.mg.dry.weight, colour = All.condition))
k_content_ACC_4w <- k_content_ACC_4w + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
k_content_ACC_4w <- k_content_ACC_4w + facet_grid(Tissue ~ Accession, scales = "free_y")
k_content_ACC_4w <- k_content_ACC_4w + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
k_content_ACC_4w <- k_content_ACC_4w+ scale_color_manual(values = c("red","blueviolet","cyan"))
k_content_ACC_4w<- k_content_ACC_4w + ylab("k content, mg/mg dry weight") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 30)
k_content_ACC_4w <- k_content_ACC_4w + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
k_content_ACC_4w <- k_content_ACC_4w + rremove("legend")
k_content_ACC_4w
aov(Na.K.ratio ~ All.ID2, data = ICP_ACC_4w)## Call:
## aov(formula = Na.K.ratio ~ All.ID2, data = ICP_ACC_4w)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 1.4147199 0.6019891
## Deg. of Freedom 11 48
##
## Residual standard error: 0.1119886
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(Na.K.ratio~ All.ID2, data = ICP_ACC_4w))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.K.ratio ~ All.ID2, data = ICP_ACC_4w)
##
## $All.ID2
## diff lwr upr p adj
## la_s+1acc_tip-la_s+1acc_ro -0.15577251 -0.398976110 0.08743109 0.5580748
## la_s+mock_ro-la_s+1acc_ro -0.06338500 -0.306588596 0.17981860 0.9988801
## la_s+mock_tip-la_s+1acc_ro -0.13127798 -0.374481576 0.11192562 0.7804449
## la_s+noacc_ro-la_s+1acc_ro 0.04105933 -0.202144270 0.28426293 0.9999833
## la_s+noacc_tip-la_s+1acc_ro -0.03716826 -0.280371860 0.20603534 0.9999940
## M248_s+1acc_ro-la_s+1acc_ro 0.25904560 0.015842003 0.50224920 0.0276953
## M248_s+1acc_tip-la_s+1acc_ro 0.15658837 -0.086615227 0.39979197 0.5502858
## M248_s+mock_ro-la_s+1acc_ro 0.15798336 -0.085220237 0.40118696 0.5369895
## M248_s+mock_tip-la_s+1acc_ro 0.07894988 -0.164253719 0.32215348 0.9924994
## M248_s+noacc_ro-la_s+1acc_ro 0.35915748 0.115953875 0.60236108 0.0003620
## M248_s+noacc_tip-la_s+1acc_ro 0.21809052 -0.025113082 0.46129412 0.1176795
## la_s+mock_ro-la_s+1acc_tip 0.09238751 -0.150816085 0.33559111 0.9745577
## la_s+mock_tip-la_s+1acc_tip 0.02449453 -0.218709065 0.26769813 0.9999999
## la_s+noacc_ro-la_s+1acc_tip 0.19683184 -0.046371759 0.44003544 0.2215065
## la_s+noacc_tip-la_s+1acc_tip 0.11860425 -0.124599349 0.36180785 0.8706783
## M248_s+1acc_ro-la_s+1acc_tip 0.41481811 0.171614514 0.65802171 0.0000251
## M248_s+1acc_tip-la_s+1acc_tip 0.31236088 0.069157284 0.55556448 0.0030475
## M248_s+mock_ro-la_s+1acc_tip 0.31375587 0.070552274 0.55695947 0.0028661
## M248_s+mock_tip-la_s+1acc_tip 0.23472239 -0.008481209 0.47792599 0.0675540
## M248_s+noacc_ro-la_s+1acc_tip 0.51492999 0.271726386 0.75813359 0.0000002
## M248_s+noacc_tip-la_s+1acc_tip 0.37386303 0.130659428 0.61706663 0.0001808
## la_s+mock_tip-la_s+mock_ro -0.06789298 -0.311096580 0.17531062 0.9979265
## la_s+noacc_ro-la_s+mock_ro 0.10444433 -0.138759274 0.34764793 0.9403551
## la_s+noacc_tip-la_s+mock_ro 0.02621674 -0.216986864 0.26942034 0.9999998
## M248_s+1acc_ro-la_s+mock_ro 0.32243060 0.079226999 0.56563420 0.0019504
## M248_s+1acc_tip-la_s+mock_ro 0.21997337 -0.023230231 0.46317697 0.1107879
## M248_s+mock_ro-la_s+mock_ro 0.22136836 -0.021835241 0.46457196 0.1058986
## M248_s+mock_tip-la_s+mock_ro 0.14233488 -0.100868723 0.38553848 0.6848715
## M248_s+noacc_ro-la_s+mock_ro 0.42254247 0.179338871 0.66574607 0.0000172
## M248_s+noacc_tip-la_s+mock_ro 0.28147551 0.038271914 0.52467911 0.0113493
## la_s+noacc_ro-la_s+mock_tip 0.17233731 -0.070866294 0.41554091 0.4048774
## la_s+noacc_tip-la_s+mock_tip 0.09410972 -0.149093884 0.33731332 0.9708986
## M248_s+1acc_ro-la_s+mock_tip 0.39032358 0.147119979 0.63352718 0.0000823
## M248_s+1acc_tip-la_s+mock_tip 0.28786635 0.044662749 0.53106995 0.0087108
## M248_s+mock_ro-la_s+mock_tip 0.28926134 0.046057739 0.53246494 0.0082174
## M248_s+mock_tip-la_s+mock_tip 0.21022786 -0.032975743 0.45343146 0.1502996
## M248_s+noacc_ro-la_s+mock_tip 0.49043545 0.247231851 0.73363905 0.0000006
## M248_s+noacc_tip-la_s+mock_tip 0.34936849 0.106164894 0.59257209 0.0005714
## la_s+noacc_tip-la_s+noacc_ro -0.07822759 -0.321431190 0.16497601 0.9930441
## M248_s+1acc_ro-la_s+noacc_ro 0.21798627 -0.025217327 0.46118987 0.1180711
## M248_s+1acc_tip-la_s+noacc_ro 0.11552904 -0.127674557 0.35873264 0.8887054
## M248_s+mock_ro-la_s+noacc_ro 0.11692403 -0.126279567 0.36012763 0.8807262
## M248_s+mock_tip-la_s+noacc_ro 0.03789055 -0.205313049 0.28109415 0.9999927
## M248_s+noacc_ro-la_s+noacc_ro 0.31809815 0.074894545 0.56130174 0.0023654
## M248_s+noacc_tip-la_s+noacc_ro 0.17703119 -0.066172412 0.42023479 0.3648307
## M248_s+1acc_ro-la_s+noacc_tip 0.29621386 0.053010263 0.53941746 0.0061275
## M248_s+1acc_tip-la_s+noacc_tip 0.19375663 -0.049446967 0.43696023 0.2408048
## M248_s+mock_ro-la_s+noacc_tip 0.19515162 -0.048051977 0.43835522 0.2319118
## M248_s+mock_tip-la_s+noacc_tip 0.11611814 -0.127085459 0.35932174 0.8853762
## M248_s+noacc_ro-la_s+noacc_tip 0.39632574 0.153122135 0.63952933 0.0000616
## M248_s+noacc_tip-la_s+noacc_tip 0.25525878 0.012055178 0.49846238 0.0319980
## M248_s+1acc_tip-M248_s+1acc_ro -0.10245723 -0.345660830 0.14074637 0.9474651
## M248_s+mock_ro-M248_s+1acc_ro -0.10106224 -0.344265840 0.14214136 0.9520879
## M248_s+mock_tip-M248_s+1acc_ro -0.18009572 -0.423299322 0.06310788 0.3398185
## M248_s+noacc_ro-M248_s+1acc_ro 0.10011187 -0.143091728 0.34331547 0.9550677
## M248_s+noacc_tip-M248_s+1acc_ro -0.04095509 -0.284158685 0.20224851 0.9999837
## M248_s+mock_ro-M248_s+1acc_tip 0.00139499 -0.241808610 0.24459859 1.0000000
## M248_s+mock_tip-M248_s+1acc_tip -0.07763849 -0.320842092 0.16556511 0.9934644
## M248_s+noacc_ro-M248_s+1acc_tip 0.20256910 -0.040634498 0.44577270 0.1884847
## M248_s+noacc_tip-M248_s+1acc_tip 0.06150214 -0.181701455 0.30470574 0.9991499
## M248_s+mock_tip-M248_s+mock_ro -0.07903348 -0.322237082 0.16417012 0.9924342
## M248_s+noacc_ro-M248_s+mock_ro 0.20117411 -0.042029488 0.44437771 0.1961592
## M248_s+noacc_tip-M248_s+mock_ro 0.06010715 -0.183096445 0.30331075 0.9993121
## M248_s+noacc_ro-M248_s+mock_tip 0.28020759 0.037003994 0.52341119 0.0119548
## M248_s+noacc_tip-M248_s+mock_tip 0.13914064 -0.104062963 0.38234424 0.7136924
## M248_s+noacc_tip-M248_s+noacc_ro -0.14106696 -0.384270557 0.10213664 0.6964074P11 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P11)
stat.test## la_s+1acc_tip la_s+mock_ro la_s+mock_tip la_s+noacc_ro
## "a" "ab" "a" "abcd"
## la_s+noacc_tip M248_s+1acc_ro M248_s+1acc_tip M248_s+mock_ro
## "ab" "ce" "bcde" "bcde"
## M248_s+mock_tip M248_s+noacc_ro M248_s+noacc_tip la_s+1acc_ro
## "abcd" "e" "cde" "abd"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+1acc"test$info[[1]][3]## [1] "tip"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_ACC_4wICP_ACC_4w$All.condition <- factor(ICP_ACC_4w$All.condition, levels = c("s+noacc", "s+mock", "s+1acc"))
ratio_content_ACC_4w <- ggplot(data = ICP_ACC_4w, mapping = aes(x = All.condition, y = Na.K.ratio, colour = All.condition))
ratio_content_ACC_4w <- ratio_content_ACC_4w + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
ratio_content_ACC_4w <- ratio_content_ACC_4w + facet_grid(Tissue ~ Accession, scales = "free_y")
ratio_content_ACC_4w <-ratio_content_ACC_4w + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
ratio_content_ACC_4w <- ratio_content_ACC_4w+ scale_color_manual(values = c("red","blueviolet","cyan"))
ratio_content_ACC_4w<- ratio_content_ACC_4w + ylab("Na/K ratio") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 1)
ratio_content_ACC_4w <- ratio_content_ACC_4w + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
ratio_content_ACC_4w <- ratio_content_ACC_4w + rremove("legend")
ratio_content_ACC_4w
ICP_ACC_4wnoacc_sub <- subset(ICP_ACC_4w, ICP_ACC_4w$Condition2 %in% c("1acc", "noacc"))
noacc_subOutput <- TukeyHSD(aov(Na.K.ratio ~ All.ID2, data = noacc_sub))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.K.ratio ~ All.ID2, data = noacc_sub)
##
## $All.ID2
## diff lwr upr p adj
## la_s+1acc_tip-la_s+1acc_ro -0.15577251 -0.38557269 0.07402767 0.3806147
## la_s+noacc_ro-la_s+1acc_ro 0.04105933 -0.18874085 0.27085951 0.9989173
## la_s+noacc_tip-la_s+1acc_ro -0.03716826 -0.26696844 0.19263192 0.9994321
## M248_s+1acc_ro-la_s+1acc_ro 0.25904560 0.02924542 0.48884578 0.0183837
## M248_s+1acc_tip-la_s+1acc_ro 0.15658837 -0.07321181 0.38638855 0.3741805
## M248_s+noacc_ro-la_s+1acc_ro 0.35915748 0.12935730 0.58895766 0.0003985
## M248_s+noacc_tip-la_s+1acc_ro 0.21809052 -0.01170966 0.44789070 0.0727791
## la_s+noacc_ro-la_s+1acc_tip 0.19683184 -0.03296834 0.42663202 0.1374715
## la_s+noacc_tip-la_s+1acc_tip 0.11860425 -0.11119593 0.34840443 0.7043267
## M248_s+1acc_ro-la_s+1acc_tip 0.41481811 0.18501793 0.64461829 0.0000425
## M248_s+1acc_tip-la_s+1acc_tip 0.31236088 0.08256070 0.54216106 0.0025184
## M248_s+noacc_ro-la_s+1acc_tip 0.51492999 0.28512981 0.74473017 0.0000008
## M248_s+noacc_tip-la_s+1acc_tip 0.37386303 0.14406285 0.60366321 0.0002212
## la_s+noacc_tip-la_s+noacc_ro -0.07822759 -0.30802777 0.15157259 0.9515308
## M248_s+1acc_ro-la_s+noacc_ro 0.21798627 -0.01181391 0.44778645 0.0730173
## M248_s+1acc_tip-la_s+noacc_ro 0.11552904 -0.11427114 0.34532922 0.7302063
## M248_s+noacc_ro-la_s+noacc_ro 0.31809815 0.08829796 0.54789833 0.0020160
## M248_s+noacc_tip-la_s+noacc_ro 0.17703119 -0.05276899 0.40683137 0.2335164
## M248_s+1acc_ro-la_s+noacc_tip 0.29621386 0.06641368 0.52601404 0.0046759
## M248_s+1acc_tip-la_s+noacc_tip 0.19375663 -0.03604355 0.42355681 0.1498981
## M248_s+noacc_ro-la_s+noacc_tip 0.39632574 0.16652555 0.62612592 0.0000896
## M248_s+noacc_tip-la_s+noacc_tip 0.25525878 0.02545860 0.48505896 0.0210193
## M248_s+1acc_tip-M248_s+1acc_ro -0.10245723 -0.33225741 0.12734295 0.8298130
## M248_s+noacc_ro-M248_s+1acc_ro 0.10011187 -0.12968831 0.32991205 0.8454570
## M248_s+noacc_tip-M248_s+1acc_ro -0.04095509 -0.27075527 0.18884509 0.9989349
## M248_s+noacc_ro-M248_s+1acc_tip 0.20256910 -0.02723108 0.43236928 0.1165237
## M248_s+noacc_tip-M248_s+1acc_tip 0.06150214 -0.16829804 0.29130232 0.9870356
## M248_s+noacc_tip-M248_s+noacc_ro -0.14106696 -0.37086714 0.08873322 0.5046877P11 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P11)
stat.test## la_s+1acc_tip la_s+noacc_ro la_s+noacc_tip M248_s+1acc_ro
## "a" "abcd" "ac" "be"
## M248_s+1acc_tip M248_s+noacc_ro M248_s+noacc_tip la_s+1acc_ro
## "bcde" "e" "bde" "acd"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+1acc"test$info[[1]][3]## [1] "tip"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ratio_noacc_ACC_4w <- ggplot(data = noacc_sub, mapping = aes(x = All.condition, y = Na.K.ratio, colour = All.condition))
ratio_noacc_ACC_4w <- ratio_noacc_ACC_4w + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
ratio_noacc_ACC_4w <- ratio_noacc_ACC_4w + facet_grid(Tissue ~ Accession, scales = "free_y")
ratio_noacc_ACC_4w <- ratio_noacc_ACC_4w + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
ratio_noacc_ACC_4w <- ratio_noacc_ACC_4w + scale_color_manual(values = c("red","blueviolet","cyan"))
ratio_noacc_ACC_4w <- ratio_noacc_ACC_4w + ylab("Na/K ratio") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 1.3)
ratio_noacc_ACC_4w <- ratio_noacc_ACC_4w + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
ratio_noacc_ACC_4w <- ratio_noacc_ACC_4w + rremove("legend")
ratio_noacc_ACC_4w
now lets calculate Na, K and the ratio for IAA-10d of spray.
list.files(pattern=".csv")## [1] "ICP-ACC-SOIL-SPAR-SALT-10d.csv" "ICP-ACC-SOIL-SPAR-SALT-4weeks.csv"
## [3] "ICP-IAA-SOIL-SPAR-SALT-10d.csv" "ICP-IAA-SOIL-SPAR-SALT-4weeks.csv"#lets start with 10 of ACC treatment.
ICP_IAA_10d <- read.csv("ICP-IAA-SOIL-SPAR-SALT-10d.csv")
ICP_IAA_10dICP_IAA_10d$All.condition<-paste(ICP_IAA_10d$Condition1,ICP_IAA_10d$Condition2, sep="+")
ICP_IAA_10dICP_IAA_10d$All.ID2<-paste(ICP_IAA_10d$Accession,ICP_IAA_10d$All.condition, ICP_IAA_10d$Tissue,sep="_")
ICP_IAA_10daov(Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_10d)## Call:
## aov(formula = Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_10d)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 662.5470 611.4302
## Deg. of Freedom 11 48
##
## Residual standard error: 3.569052
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_10d))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_10d)
##
## $All.ID2
## diff lwr upr
## la_s+iaa_sh-la_s+iaa_ro 0.62015013 -7.6008639 8.8411642
## la_s+mockiaa_ro-la_s+iaa_ro -3.71247158 -11.4633179 4.0383748
## la_s+mockiaa_sh-la_s+iaa_ro -3.92731034 -11.6781567 3.8235360
## la_s+noiaa_ro-la_s+iaa_ro -5.20462773 -12.9554741 2.5462186
## la_s+noiaa_sh-la_s+iaa_ro -0.75158470 -8.5024311 6.9992617
## M248_s+iaa_ro-la_s+iaa_ro -2.43517148 -9.8560419 4.9856989
## M248_s+iaa_sh-la_s+iaa_ro 5.83017288 -1.9206735 13.5810192
## M248_s+mockiaa_ro-la_s+iaa_ro -0.30067617 -8.0515225 7.4501702
## M248_s+mockiaa_sh-la_s+iaa_ro 3.29733722 -4.4535091 11.0481836
## M248_s+noiaa_ro-la_s+iaa_ro -4.21231504 -11.9631614 3.5385313
## M248_s+noiaa_sh-la_s+iaa_ro 3.31466278 -4.4361836 11.0655091
## la_s+mockiaa_ro-la_s+iaa_sh -4.33262172 -12.5536357 3.8883923
## la_s+mockiaa_sh-la_s+iaa_sh -4.54746048 -12.7684745 3.6735536
## la_s+noiaa_ro-la_s+iaa_sh -5.82477786 -14.0457919 2.3962362
## la_s+noiaa_sh-la_s+iaa_sh -1.37173484 -9.5927489 6.8492792
## M248_s+iaa_ro-la_s+iaa_sh -3.05532162 -10.9659961 4.8553528
## M248_s+iaa_sh-la_s+iaa_sh 5.21002275 -3.0109913 13.4310368
## M248_s+mockiaa_ro-la_s+iaa_sh -0.92082631 -9.1418403 7.3001877
## M248_s+mockiaa_sh-la_s+iaa_sh 2.67718709 -5.5438269 10.8982011
## M248_s+noiaa_ro-la_s+iaa_sh -4.83246517 -13.0534792 3.3885489
## M248_s+noiaa_sh-la_s+iaa_sh 2.69451265 -5.5265014 10.9155267
## la_s+mockiaa_sh-la_s+mockiaa_ro -0.21483876 -7.9656851 7.5360076
## la_s+noiaa_ro-la_s+mockiaa_ro -1.49215615 -9.2430025 6.2586902
## la_s+noiaa_sh-la_s+mockiaa_ro 2.96088688 -4.7899595 10.7117332
## M248_s+iaa_ro-la_s+mockiaa_ro 1.27730010 -6.1435703 8.6981705
## M248_s+iaa_sh-la_s+mockiaa_ro 9.54264447 1.7917981 17.2934908
## M248_s+mockiaa_ro-la_s+mockiaa_ro 3.41179541 -4.3390509 11.1626418
## M248_s+mockiaa_sh-la_s+mockiaa_ro 7.00980880 -0.7410376 14.7606552
## M248_s+noiaa_ro-la_s+mockiaa_ro -0.49984345 -8.2506898 7.2510029
## M248_s+noiaa_sh-la_s+mockiaa_ro 7.02713436 -0.7237120 14.7779807
## la_s+noiaa_ro-la_s+mockiaa_sh -1.27731739 -9.0281637 6.4735290
## la_s+noiaa_sh-la_s+mockiaa_sh 3.17572564 -4.5751207 10.9265720
## M248_s+iaa_ro-la_s+mockiaa_sh 1.49213886 -5.9287316 8.9130093
## M248_s+iaa_sh-la_s+mockiaa_sh 9.75748322 2.0066369 17.5083296
## M248_s+mockiaa_ro-la_s+mockiaa_sh 3.62663417 -4.1242122 11.3774805
## M248_s+mockiaa_sh-la_s+mockiaa_sh 7.22464756 -0.5261988 14.9754939
## M248_s+noiaa_ro-la_s+mockiaa_sh -0.28500469 -8.0358511 7.4658417
## M248_s+noiaa_sh-la_s+mockiaa_sh 7.24197312 -0.5088732 14.9928195
## la_s+noiaa_sh-la_s+noiaa_ro 4.45304303 -3.2978033 12.2038894
## M248_s+iaa_ro-la_s+noiaa_ro 2.76945625 -4.6514142 10.1903267
## M248_s+iaa_sh-la_s+noiaa_ro 11.03480061 3.2839543 18.7856470
## M248_s+mockiaa_ro-la_s+noiaa_ro 4.90395156 -2.8468948 12.6547979
## M248_s+mockiaa_sh-la_s+noiaa_ro 8.50196495 0.7511186 16.2528113
## M248_s+noiaa_ro-la_s+noiaa_ro 0.99231270 -6.7585337 8.7431591
## M248_s+noiaa_sh-la_s+noiaa_ro 8.51929051 0.7684442 16.2701369
## M248_s+iaa_ro-la_s+noiaa_sh -1.68358678 -9.1044572 5.7372836
## M248_s+iaa_sh-la_s+noiaa_sh 6.58175758 -1.1690888 14.3326039
## M248_s+mockiaa_ro-la_s+noiaa_sh 0.45090853 -7.2999378 8.2017549
## M248_s+mockiaa_sh-la_s+noiaa_sh 4.04892192 -3.7019244 11.7997683
## M248_s+noiaa_ro-la_s+noiaa_sh -3.46073033 -11.2115767 4.2901160
## M248_s+noiaa_sh-la_s+noiaa_sh 4.06624748 -3.6845989 11.8170938
## M248_s+iaa_sh-M248_s+iaa_ro 8.26534437 0.8444740 15.6862148
## M248_s+mockiaa_ro-M248_s+iaa_ro 2.13449531 -5.2863751 9.5553657
## M248_s+mockiaa_sh-M248_s+iaa_ro 5.73250870 -1.6883617 13.1533791
## M248_s+noiaa_ro-M248_s+iaa_ro -1.77714355 -9.1980140 5.6437269
## M248_s+noiaa_sh-M248_s+iaa_ro 5.74983426 -1.6710361 13.1707047
## M248_s+mockiaa_ro-M248_s+iaa_sh -6.13084906 -13.8816954 1.6199973
## M248_s+mockiaa_sh-M248_s+iaa_sh -2.53283566 -10.2836820 5.2180107
## M248_s+noiaa_ro-M248_s+iaa_sh -10.04248792 -17.7933343 -2.2916416
## M248_s+noiaa_sh-M248_s+iaa_sh -2.51551010 -10.2663565 5.2353363
## M248_s+mockiaa_sh-M248_s+mockiaa_ro 3.59801339 -4.1528330 11.3488598
## M248_s+noiaa_ro-M248_s+mockiaa_ro -3.91163886 -11.6624852 3.8392075
## M248_s+noiaa_sh-M248_s+mockiaa_ro 3.61533895 -4.1355074 11.3661853
## M248_s+noiaa_ro-M248_s+mockiaa_sh -7.50965225 -15.2604986 0.2411941
## M248_s+noiaa_sh-M248_s+mockiaa_sh 0.01732556 -7.7335208 7.7681719
## M248_s+noiaa_sh-M248_s+noiaa_ro 7.52697781 -0.2238685 15.2778242
## p adj
## la_s+iaa_sh-la_s+iaa_ro 1.0000000
## la_s+mockiaa_ro-la_s+iaa_ro 0.8832514
## la_s+mockiaa_sh-la_s+iaa_ro 0.8406029
## la_s+noiaa_ro-la_s+iaa_ro 0.4867160
## la_s+noiaa_sh-la_s+iaa_ro 1.0000000
## M248_s+iaa_ro-la_s+iaa_ro 0.9918084
## M248_s+iaa_sh-la_s+iaa_ro 0.3174962
## M248_s+mockiaa_ro-la_s+iaa_ro 1.0000000
## M248_s+mockiaa_sh-la_s+iaa_ro 0.9439452
## M248_s+noiaa_ro-la_s+iaa_ro 0.7732050
## M248_s+noiaa_sh-la_s+iaa_ro 0.9419758
## la_s+mockiaa_ro-la_s+iaa_sh 0.8047566
## la_s+mockiaa_sh-la_s+iaa_sh 0.7536771
## la_s+noiaa_ro-la_s+iaa_sh 0.4050692
## la_s+noiaa_sh-la_s+iaa_sh 0.9999852
## M248_s+iaa_ro-la_s+iaa_sh 0.9712934
## M248_s+iaa_sh-la_s+iaa_sh 0.5737801
## M248_s+mockiaa_ro-la_s+iaa_sh 0.9999998
## M248_s+mockiaa_sh-la_s+iaa_sh 0.9923032
## M248_s+noiaa_ro-la_s+iaa_sh 0.6791455
## M248_s+noiaa_sh-la_s+iaa_sh 0.9918878
## la_s+mockiaa_sh-la_s+mockiaa_ro 1.0000000
## la_s+noiaa_ro-la_s+mockiaa_ro 0.9999377
## la_s+noiaa_sh-la_s+mockiaa_ro 0.9734951
## M248_s+iaa_ro-la_s+mockiaa_ro 0.9999797
## M248_s+iaa_sh-la_s+mockiaa_ro 0.0053423
## M248_s+mockiaa_ro-la_s+mockiaa_ro 0.9300506
## M248_s+mockiaa_sh-la_s+mockiaa_ro 0.1108658
## M248_s+noiaa_ro-la_s+mockiaa_ro 1.0000000
## M248_s+noiaa_sh-la_s+mockiaa_ro 0.1089377
## la_s+noiaa_ro-la_s+mockiaa_sh 0.9999869
## la_s+noiaa_sh-la_s+mockiaa_sh 0.9564764
## M248_s+iaa_ro-la_s+mockiaa_sh 0.9999043
## M248_s+iaa_sh-la_s+mockiaa_sh 0.0039943
## M248_s+mockiaa_ro-la_s+mockiaa_sh 0.8981595
## M248_s+mockiaa_sh-la_s+mockiaa_sh 0.0888513
## M248_s+noiaa_ro-la_s+mockiaa_sh 1.0000000
## M248_s+noiaa_sh-la_s+mockiaa_sh 0.0872484
## la_s+noiaa_sh-la_s+noiaa_ro 0.7084711
## M248_s+iaa_ro-la_s+noiaa_ro 0.9776810
## M248_s+iaa_sh-la_s+noiaa_ro 0.0006602
## M248_s+mockiaa_ro-la_s+noiaa_ro 0.5762107
## M248_s+mockiaa_sh-la_s+noiaa_ro 0.0205079
## M248_s+noiaa_ro-la_s+noiaa_ro 0.9999990
## M248_s+noiaa_sh-la_s+noiaa_ro 0.0200732
## M248_s+iaa_ro-la_s+noiaa_sh 0.9996922
## M248_s+iaa_sh-la_s+noiaa_sh 0.1679621
## M248_s+mockiaa_ro-la_s+noiaa_sh 1.0000000
## M248_s+mockiaa_sh-la_s+noiaa_sh 0.8132484
## M248_s+noiaa_ro-la_s+noiaa_sh 0.9234627
## M248_s+noiaa_sh-la_s+noiaa_sh 0.8091744
## M248_s+iaa_sh-M248_s+iaa_ro 0.0174276
## M248_s+mockiaa_ro-M248_s+iaa_ro 0.9973041
## M248_s+mockiaa_sh-M248_s+iaa_ro 0.2808502
## M248_s+noiaa_ro-M248_s+iaa_ro 0.9994871
## M248_s+noiaa_sh-M248_s+iaa_ro 0.2768104
## M248_s+mockiaa_ro-M248_s+iaa_sh 0.2498606
## M248_s+mockiaa_sh-M248_s+iaa_sh 0.9920826
## M248_s+noiaa_ro-M248_s+iaa_sh 0.0027000
## M248_s+noiaa_sh-M248_s+iaa_sh 0.9925142
## M248_s+mockiaa_sh-M248_s+mockiaa_ro 0.9028547
## M248_s+noiaa_ro-M248_s+mockiaa_ro 0.8439645
## M248_s+noiaa_sh-M248_s+mockiaa_ro 0.9000289
## M248_s+noiaa_ro-M248_s+mockiaa_sh 0.0654366
## M248_s+noiaa_sh-M248_s+mockiaa_sh 1.0000000
## M248_s+noiaa_sh-M248_s+noiaa_ro 0.0642035P10 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P10)
stat.test## la_s+iaa_sh la_s+mockiaa_ro la_s+mockiaa_sh la_s+noiaa_ro
## "abc" "ab" "ab" "a"
## la_s+noiaa_sh M248_s+iaa_ro M248_s+iaa_sh M248_s+mockiaa_ro
## "abc" "ab" "c" "abc"
## M248_s+mockiaa_sh M248_s+noiaa_ro M248_s+noiaa_sh la_s+iaa_ro
## "bc" "ab" "bc" "abc"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+iaa"test$info[[1]][3]## [1] "sh"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_IAA_10dICP_IAA_10d$All.condition <- factor(ICP_IAA_10d$All.condition, levels = c("s+noiaa", "s+mockiaa", "s+iaa"))
ICP_IAA_10d <- subset(ICP_IAA_10d, ICP_IAA_10d$Na.con.mg.mg.dry.weight < 25)
Na_content_IAA_10d <- ggplot(data = ICP_IAA_10d, mapping = aes(x = All.condition, y = Na.con.mg.mg.dry.weight, colour = All.condition))
Na_content_IAA_10d <- Na_content_IAA_10d + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Na_content_IAA_10d <- Na_content_IAA_10d + facet_grid(Tissue ~ Accession, scales = "free_y")
Na_content_IAA_10d <- Na_content_IAA_10d + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Na_content_IAA_10d <- Na_content_IAA_10d + scale_color_manual(values = c("red","blueviolet","cyan"))
Na_content_IAA_10d <- Na_content_IAA_10d + ylab("Na content, mg/mg dry weight") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 25)
Na_content_IAA_10d <- Na_content_IAA_10d + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Na_content_IAA_10d <- Na_content_IAA_10d + rremove("legend")
Na_content_IAA_10d
aov(K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_10d)## Call:
## aov(formula = K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_10d)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 5327.205 1815.188
## Deg. of Freedom 11 47
##
## Residual standard error: 6.214582
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_10d))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_10d)
##
## $All.ID2
## diff lwr upr
## la_s+iaa_sh-la_s+iaa_ro -22.935398367 -38.040277 -7.8305198
## la_s+mockiaa_ro-la_s+iaa_ro 0.900355972 -13.429390 15.2301020
## la_s+mockiaa_sh-la_s+iaa_ro -21.923940769 -36.253687 -7.5941947
## la_s+noiaa_ro-la_s+iaa_ro -3.643595420 -17.973341 10.6861506
## la_s+noiaa_sh-la_s+iaa_ro -15.481338999 -29.811085 -1.1515930
## M248_s+iaa_ro-la_s+iaa_ro -15.476678418 -29.265483 -1.6878739
## M248_s+iaa_sh-la_s+iaa_ro -28.234461523 -42.564208 -13.9047155
## M248_s+mockiaa_ro-la_s+iaa_ro -10.662527963 -24.992274 3.6672181
## M248_s+mockiaa_sh-la_s+iaa_ro -24.945523436 -39.275269 -10.6157774
## M248_s+noiaa_ro-la_s+iaa_ro -23.010439355 -37.340185 -8.6806933
## M248_s+noiaa_sh-la_s+iaa_ro -22.429350958 -36.759097 -8.0996049
## la_s+mockiaa_ro-la_s+iaa_sh 23.835754339 9.506008 38.1655004
## la_s+mockiaa_sh-la_s+iaa_sh 1.011457599 -13.318288 15.3412036
## la_s+noiaa_ro-la_s+iaa_sh 19.291802947 4.962057 33.6215490
## la_s+noiaa_sh-la_s+iaa_sh 7.454059369 -6.875687 21.7838054
## M248_s+iaa_ro-la_s+iaa_sh 7.458719950 -6.330085 21.2475245
## M248_s+iaa_sh-la_s+iaa_sh -5.299063155 -19.628809 9.0306829
## M248_s+mockiaa_ro-la_s+iaa_sh 12.272870405 -2.056876 26.6026164
## M248_s+mockiaa_sh-la_s+iaa_sh -2.010125069 -16.339871 12.3196210
## M248_s+noiaa_ro-la_s+iaa_sh -0.075040987 -14.404787 14.2547051
## M248_s+noiaa_sh-la_s+iaa_sh 0.506047409 -13.823699 14.8357934
## la_s+mockiaa_sh-la_s+mockiaa_ro -22.824296740 -36.334511 -9.3140826
## la_s+noiaa_ro-la_s+mockiaa_ro -4.543951392 -18.054166 8.9662627
## la_s+noiaa_sh-la_s+mockiaa_ro -16.381694970 -29.891909 -2.8714808
## M248_s+iaa_ro-la_s+mockiaa_ro -16.377034389 -29.312080 -3.4419892
## M248_s+iaa_sh-la_s+mockiaa_ro -29.134817494 -42.645032 -15.6246034
## M248_s+mockiaa_ro-la_s+mockiaa_ro -11.562883934 -25.073098 1.9473302
## M248_s+mockiaa_sh-la_s+mockiaa_ro -25.845879408 -39.356094 -12.3356653
## M248_s+noiaa_ro-la_s+mockiaa_ro -23.910795326 -37.421009 -10.4005812
## M248_s+noiaa_sh-la_s+mockiaa_ro -23.329706930 -36.839921 -9.8194928
## la_s+noiaa_ro-la_s+mockiaa_sh 18.280345348 4.770131 31.7905595
## la_s+noiaa_sh-la_s+mockiaa_sh 6.442601770 -7.067612 19.9528159
## M248_s+iaa_ro-la_s+mockiaa_sh 6.447262351 -6.487783 19.3823076
## M248_s+iaa_sh-la_s+mockiaa_sh -6.310520754 -19.820735 7.1996934
## M248_s+mockiaa_ro-la_s+mockiaa_sh 11.261412806 -2.248801 24.7716269
## M248_s+mockiaa_sh-la_s+mockiaa_sh -3.021582668 -16.531797 10.4886315
## M248_s+noiaa_ro-la_s+mockiaa_sh -1.086498586 -14.596713 12.4237155
## M248_s+noiaa_sh-la_s+mockiaa_sh -0.505410190 -14.015624 13.0048039
## la_s+noiaa_sh-la_s+noiaa_ro -11.837743578 -25.347958 1.6724706
## M248_s+iaa_ro-la_s+noiaa_ro -11.833082997 -24.768128 1.1019622
## M248_s+iaa_sh-la_s+noiaa_ro -24.590866102 -38.101080 -11.0806520
## M248_s+mockiaa_ro-la_s+noiaa_ro -7.018932542 -20.529147 6.4912816
## M248_s+mockiaa_sh-la_s+noiaa_ro -21.301928016 -34.812142 -7.7917139
## M248_s+noiaa_ro-la_s+noiaa_ro -19.366843934 -32.877058 -5.8566298
## M248_s+noiaa_sh-la_s+noiaa_ro -18.785755538 -32.295970 -5.2755414
## M248_s+iaa_ro-la_s+noiaa_sh 0.004660581 -12.930385 12.9397058
## M248_s+iaa_sh-la_s+noiaa_sh -12.753122524 -26.263337 0.7570916
## M248_s+mockiaa_ro-la_s+noiaa_sh 4.818811036 -8.691403 18.3290252
## M248_s+mockiaa_sh-la_s+noiaa_sh -9.464184438 -22.974399 4.0460297
## M248_s+noiaa_ro-la_s+noiaa_sh -7.529100356 -21.039314 5.9811138
## M248_s+noiaa_sh-la_s+noiaa_sh -6.948011960 -20.458226 6.5622022
## M248_s+iaa_sh-M248_s+iaa_ro -12.757783105 -25.692828 0.1772621
## M248_s+mockiaa_ro-M248_s+iaa_ro 4.814150455 -8.120895 17.7491957
## M248_s+mockiaa_sh-M248_s+iaa_ro -9.468845019 -22.403890 3.4662002
## M248_s+noiaa_ro-M248_s+iaa_ro -7.533760937 -20.468806 5.4012843
## M248_s+noiaa_sh-M248_s+iaa_ro -6.952672541 -19.887718 5.9823727
## M248_s+mockiaa_ro-M248_s+iaa_sh 17.571933560 4.061719 31.0821477
## M248_s+mockiaa_sh-M248_s+iaa_sh 3.288938086 -10.221276 16.7991522
## M248_s+noiaa_ro-M248_s+iaa_sh 5.224022168 -8.286192 18.7342363
## M248_s+noiaa_sh-M248_s+iaa_sh 5.805110564 -7.705104 19.3153247
## M248_s+mockiaa_sh-M248_s+mockiaa_ro -14.282995474 -27.793210 -0.7727813
## M248_s+noiaa_ro-M248_s+mockiaa_ro -12.347911392 -25.858126 1.1623027
## M248_s+noiaa_sh-M248_s+mockiaa_ro -11.766822996 -25.277037 1.7433911
## M248_s+noiaa_ro-M248_s+mockiaa_sh 1.935084082 -11.575130 15.4452982
## M248_s+noiaa_sh-M248_s+mockiaa_sh 2.516172478 -10.994042 16.0263866
## M248_s+noiaa_sh-M248_s+noiaa_ro 0.581088396 -12.929126 14.0913025
## p adj
## la_s+iaa_sh-la_s+iaa_ro 0.0002307
## la_s+mockiaa_ro-la_s+iaa_ro 1.0000000
## la_s+mockiaa_sh-la_s+iaa_ro 0.0002021
## la_s+noiaa_ro-la_s+iaa_ro 0.9990919
## la_s+noiaa_sh-la_s+iaa_ro 0.0240590
## M248_s+iaa_ro-la_s+iaa_ro 0.0160875
## M248_s+iaa_sh-la_s+iaa_ro 0.0000011
## M248_s+mockiaa_ro-la_s+iaa_ro 0.3319345
## M248_s+mockiaa_sh-la_s+iaa_ro 0.0000173
## M248_s+noiaa_ro-la_s+iaa_ro 0.0000842
## M248_s+noiaa_sh-la_s+iaa_ro 0.0001347
## la_s+mockiaa_ro-la_s+iaa_sh 0.0000430
## la_s+mockiaa_sh-la_s+iaa_sh 1.0000000
## la_s+noiaa_ro-la_s+iaa_sh 0.0015822
## la_s+noiaa_sh-la_s+iaa_sh 0.8160789
## M248_s+iaa_ro-la_s+iaa_sh 0.7769628
## M248_s+iaa_sh-la_s+iaa_sh 0.9789148
## M248_s+mockiaa_ro-la_s+iaa_sh 0.1589358
## M248_s+mockiaa_sh-la_s+iaa_sh 0.9999975
## M248_s+noiaa_ro-la_s+iaa_sh 1.0000000
## M248_s+noiaa_sh-la_s+iaa_sh 1.0000000
## la_s+mockiaa_sh-la_s+mockiaa_ro 0.0000317
## la_s+noiaa_ro-la_s+mockiaa_ro 0.9898793
## la_s+noiaa_sh-la_s+mockiaa_ro 0.0065297
## M248_s+iaa_ro-la_s+mockiaa_ro 0.0037390
## M248_s+iaa_sh-la_s+mockiaa_ro 0.0000001
## M248_s+mockiaa_ro-la_s+mockiaa_ro 0.1596293
## M248_s+mockiaa_sh-la_s+mockiaa_ro 0.0000022
## M248_s+noiaa_ro-la_s+mockiaa_ro 0.0000123
## M248_s+noiaa_sh-la_s+mockiaa_ro 0.0000204
## la_s+noiaa_ro-la_s+mockiaa_sh 0.0014691
## la_s+noiaa_sh-la_s+mockiaa_sh 0.8853124
## M248_s+iaa_ro-la_s+mockiaa_sh 0.8530806
## M248_s+iaa_sh-la_s+mockiaa_sh 0.8983951
## M248_s+mockiaa_ro-la_s+mockiaa_sh 0.1871673
## M248_s+mockiaa_sh-la_s+mockiaa_sh 0.9997268
## M248_s+noiaa_ro-la_s+mockiaa_sh 1.0000000
## M248_s+noiaa_sh-la_s+mockiaa_sh 1.0000000
## la_s+noiaa_sh-la_s+noiaa_ro 0.1373521
## M248_s+iaa_ro-la_s+noiaa_ro 0.1019436
## M248_s+iaa_sh-la_s+noiaa_ro 0.0000068
## M248_s+mockiaa_ro-la_s+noiaa_ro 0.8172521
## M248_s+mockiaa_sh-la_s+noiaa_ro 0.0001180
## M248_s+noiaa_ro-la_s+noiaa_ro 0.0006034
## M248_s+noiaa_sh-la_s+noiaa_ro 0.0009739
## M248_s+iaa_ro-la_s+noiaa_sh 1.0000000
## M248_s+iaa_sh-la_s+noiaa_sh 0.0805332
## M248_s+mockiaa_ro-la_s+noiaa_sh 0.9839628
## M248_s+mockiaa_sh-la_s+noiaa_sh 0.4210353
## M248_s+noiaa_ro-la_s+noiaa_sh 0.7438195
## M248_s+noiaa_sh-la_s+noiaa_sh 0.8265473
## M248_s+iaa_sh-M248_s+iaa_ro 0.0563340
## M248_s+mockiaa_ro-M248_s+iaa_ro 0.9778798
## M248_s+mockiaa_sh-M248_s+iaa_ro 0.3555161
## M248_s+noiaa_ro-M248_s+iaa_ro 0.6897339
## M248_s+noiaa_sh-M248_s+iaa_ro 0.7836457
## M248_s+mockiaa_ro-M248_s+iaa_sh 0.0025903
## M248_s+mockiaa_sh-M248_s+iaa_sh 0.9993924
## M248_s+noiaa_ro-M248_s+iaa_sh 0.9707377
## M248_s+noiaa_sh-M248_s+iaa_sh 0.9395876
## M248_s+mockiaa_sh-M248_s+mockiaa_ro 0.0298651
## M248_s+noiaa_ro-M248_s+mockiaa_ro 0.1026245
## M248_s+noiaa_sh-M248_s+mockiaa_ro 0.1428499
## M248_s+noiaa_ro-M248_s+mockiaa_sh 0.9999969
## M248_s+noiaa_sh-M248_s+mockiaa_sh 0.9999543
## M248_s+noiaa_sh-M248_s+noiaa_ro 1.0000000P9 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P9)
stat.test## la_s+iaa_sh la_s+mockiaa_ro la_s+mockiaa_sh la_s+noiaa_ro
## "ab" "c" "ab" "cd"
## la_s+noiaa_sh M248_s+iaa_ro M248_s+iaa_sh M248_s+mockiaa_ro
## "abd" "abd" "a" "bcd"
## M248_s+mockiaa_sh M248_s+noiaa_ro M248_s+noiaa_sh la_s+iaa_ro
## "a" "ab" "ab" "c"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+iaa"test$info[[1]][3]## [1] "sh"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_IAA_10dICP_IAA_10d$All.condition <- factor(ICP_IAA_10d$All.condition, levels = c("s+noiaa", "s+mockiaa", "s+iaa"))
ICP_IAA_10d <- subset(ICP_IAA_10d, ICP_IAA_10d$K.con..mg.mg.dry.weight < 60)
K_content_IAA_10d <- ggplot(data = ICP_IAA_10d, mapping = aes(x = All.condition, y = K.con..mg.mg.dry.weight, colour = All.condition))
K_content_IAA_10d <- K_content_IAA_10d + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
K_content_IAA_10d <- K_content_IAA_10d + facet_grid(Tissue ~ Accession, scales = "free_y")
K_content_IAA_10d <- K_content_IAA_10d + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
K_content_IAA_10d <- K_content_IAA_10d + scale_color_manual(values = c("red","blueviolet","cyan"))
K_content_IAA_10d <- K_content_IAA_10d + ylab("K content, mg/mg dry weight") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 60)
K_content_IAA_10d <- K_content_IAA_10d + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
K_content_IAA_10d <- K_content_IAA_10d + rremove("legend")
K_content_IAA_10d
aov(Na.K.ratio ~ All.ID2, data = ICP_IAA_10d)## Call:
## aov(formula = Na.K.ratio ~ All.ID2, data = ICP_IAA_10d)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 3.0313796 0.2607962
## Deg. of Freedom 11 46
##
## Residual standard error: 0.07529597
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(Na.K.ratio ~ All.ID2, data = ICP_IAA_10d))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.K.ratio ~ All.ID2, data = ICP_IAA_10d)
##
## $All.ID2
## diff lwr upr
## la_s+iaa_sh-la_s+iaa_ro 0.371869969 0.1739798767 0.569760060
## la_s+mockiaa_ro-la_s+iaa_ro 0.005604174 -0.1836149453 0.194823294
## la_s+mockiaa_sh-la_s+iaa_ro 0.166441243 -0.0227778761 0.355660363
## la_s+noiaa_ro-la_s+iaa_ro -0.007033778 -0.1962528979 0.182185341
## la_s+noiaa_sh-la_s+iaa_ro 0.189773322 0.0005542029 0.378992442
## M248_s+iaa_ro-la_s+iaa_ro 0.145335395 -0.0378752292 0.328546020
## M248_s+iaa_sh-la_s+iaa_ro 0.773619114 0.5843999949 0.962838234
## M248_s+mockiaa_ro-la_s+iaa_ro 0.156130534 -0.0330885853 0.345349654
## M248_s+mockiaa_sh-la_s+iaa_ro 0.524500256 0.3352811361 0.713719375
## M248_s+noiaa_ro-la_s+iaa_ro 0.175046700 -0.0141724199 0.364265819
## M248_s+noiaa_sh-la_s+iaa_ro 0.458336973 0.2691178537 0.647556093
## la_s+mockiaa_ro-la_s+iaa_sh -0.366265794 -0.5400746540 -0.192456935
## la_s+mockiaa_sh-la_s+iaa_sh -0.205428725 -0.3792375848 -0.031619866
## la_s+noiaa_ro-la_s+iaa_sh -0.378903747 -0.5527126066 -0.205094887
## la_s+noiaa_sh-la_s+iaa_sh -0.182096646 -0.3559055058 -0.008287787
## M248_s+iaa_ro-la_s+iaa_sh -0.226534573 -0.3937822263 -0.059286920
## M248_s+iaa_sh-la_s+iaa_sh 0.401749146 0.2279402862 0.575558005
## M248_s+mockiaa_ro-la_s+iaa_sh -0.215739434 -0.3895482940 -0.041930575
## M248_s+mockiaa_sh-la_s+iaa_sh 0.152630287 -0.0211785726 0.326439147
## M248_s+noiaa_ro-la_s+iaa_sh -0.196823269 -0.3706321286 -0.023014409
## M248_s+noiaa_sh-la_s+iaa_sh 0.086467005 -0.0873418550 0.260275864
## la_s+mockiaa_sh-la_s+mockiaa_ro 0.160837069 -0.0030314951 0.324705634
## la_s+noiaa_ro-la_s+mockiaa_ro -0.012637953 -0.1765065169 0.151230612
## la_s+noiaa_sh-la_s+mockiaa_ro 0.184169148 0.0203005839 0.348037713
## M248_s+iaa_ro-la_s+mockiaa_ro 0.139731221 -0.0171609844 0.296623427
## M248_s+iaa_sh-la_s+mockiaa_ro 0.768014940 0.6041463759 0.931883505
## M248_s+mockiaa_ro-la_s+mockiaa_ro 0.150526360 -0.0133422043 0.314394924
## M248_s+mockiaa_sh-la_s+mockiaa_ro 0.518896081 0.3550275171 0.682764646
## M248_s+noiaa_ro-la_s+mockiaa_ro 0.169442525 0.0055739611 0.333311090
## M248_s+noiaa_sh-la_s+mockiaa_ro 0.452732799 0.2888642347 0.616601363
## la_s+noiaa_ro-la_s+mockiaa_sh -0.173475022 -0.3373435861 -0.009606457
## la_s+noiaa_sh-la_s+mockiaa_sh 0.023332079 -0.1405364853 0.187200643
## M248_s+iaa_ro-la_s+mockiaa_sh -0.021105848 -0.1779980536 0.135786358
## M248_s+iaa_sh-la_s+mockiaa_sh 0.607177871 0.4433093067 0.771046435
## M248_s+mockiaa_ro-la_s+mockiaa_sh -0.010310709 -0.1741792735 0.153557855
## M248_s+mockiaa_sh-la_s+mockiaa_sh 0.358059012 0.1941904479 0.521927577
## M248_s+noiaa_ro-la_s+mockiaa_sh 0.008605456 -0.1552631081 0.172474021
## M248_s+noiaa_sh-la_s+mockiaa_sh 0.291895730 0.1280271655 0.455764294
## la_s+noiaa_sh-la_s+noiaa_ro 0.196807101 0.0329385365 0.360675665
## M248_s+iaa_ro-la_s+noiaa_ro 0.152369174 -0.0045230318 0.309261379
## M248_s+iaa_sh-la_s+noiaa_ro 0.780652893 0.6167843285 0.944521457
## M248_s+mockiaa_ro-la_s+noiaa_ro 0.163164313 -0.0007042517 0.327032877
## M248_s+mockiaa_sh-la_s+noiaa_ro 0.531534034 0.3676654697 0.695402598
## M248_s+noiaa_ro-la_s+noiaa_ro 0.182080478 0.0182119137 0.345949042
## M248_s+noiaa_sh-la_s+noiaa_ro 0.465370752 0.3015021873 0.629239316
## M248_s+iaa_ro-la_s+noiaa_sh -0.044437927 -0.2013301326 0.112454279
## M248_s+iaa_sh-la_s+noiaa_sh 0.583845792 0.4199772277 0.747714356
## M248_s+mockiaa_ro-la_s+noiaa_sh -0.033642788 -0.1975113525 0.130225776
## M248_s+mockiaa_sh-la_s+noiaa_sh 0.334726933 0.1708583689 0.498595498
## M248_s+noiaa_ro-la_s+noiaa_sh -0.014726623 -0.1785951871 0.149141942
## M248_s+noiaa_sh-la_s+noiaa_sh 0.268563651 0.1046950865 0.432432215
## M248_s+iaa_sh-M248_s+iaa_ro 0.628283719 0.4713915134 0.785175925
## M248_s+mockiaa_ro-M248_s+iaa_ro 0.010795139 -0.1460970668 0.167687344
## M248_s+mockiaa_sh-M248_s+iaa_ro 0.379164860 0.2222726546 0.536057066
## M248_s+noiaa_ro-M248_s+iaa_ro 0.029711304 -0.1271809014 0.186603510
## M248_s+noiaa_sh-M248_s+iaa_ro 0.313001578 0.1561093722 0.469893783
## M248_s+mockiaa_ro-M248_s+iaa_sh -0.617488580 -0.7813571445 -0.453620016
## M248_s+mockiaa_sh-M248_s+iaa_sh -0.249118859 -0.4129874231 -0.085250294
## M248_s+noiaa_ro-M248_s+iaa_sh -0.598572415 -0.7624409791 -0.434703850
## M248_s+noiaa_sh-M248_s+iaa_sh -0.315282141 -0.4791507055 -0.151413577
## M248_s+mockiaa_sh-M248_s+mockiaa_ro 0.368369721 0.2045011571 0.532238286
## M248_s+noiaa_ro-M248_s+mockiaa_ro 0.018916165 -0.1449523989 0.182784730
## M248_s+noiaa_sh-M248_s+mockiaa_ro 0.302206439 0.1383378747 0.466075003
## M248_s+noiaa_ro-M248_s+mockiaa_sh -0.349453556 -0.5133221203 -0.185584992
## M248_s+noiaa_sh-M248_s+mockiaa_sh -0.066163282 -0.2300318467 0.097705282
## M248_s+noiaa_sh-M248_s+noiaa_ro 0.283290274 0.1194217093 0.447158838
## p adj
## la_s+iaa_sh-la_s+iaa_ro 0.0000036
## la_s+mockiaa_ro-la_s+iaa_ro 1.0000000
## la_s+mockiaa_sh-la_s+iaa_ro 0.1335654
## la_s+noiaa_ro-la_s+iaa_ro 1.0000000
## la_s+noiaa_sh-la_s+iaa_ro 0.0487345
## M248_s+iaa_ro-la_s+iaa_ro 0.2447839
## M248_s+iaa_sh-la_s+iaa_ro 0.0000000
## M248_s+mockiaa_ro-la_s+iaa_ro 0.1978771
## M248_s+mockiaa_sh-la_s+iaa_ro 0.0000000
## M248_s+noiaa_ro-la_s+iaa_ro 0.0937482
## M248_s+noiaa_sh-la_s+iaa_ro 0.0000000
## la_s+mockiaa_ro-la_s+iaa_sh 0.0000002
## la_s+mockiaa_sh-la_s+iaa_sh 0.0089758
## la_s+noiaa_ro-la_s+iaa_sh 0.0000001
## la_s+noiaa_sh-la_s+iaa_sh 0.0326427
## M248_s+iaa_ro-la_s+iaa_sh 0.0014696
## M248_s+iaa_sh-la_s+iaa_sh 0.0000000
## M248_s+mockiaa_ro-la_s+iaa_sh 0.0048969
## M248_s+mockiaa_sh-la_s+iaa_sh 0.1350526
## M248_s+noiaa_ro-la_s+iaa_sh 0.0146548
## M248_s+noiaa_sh-la_s+iaa_sh 0.8535442
## la_s+mockiaa_sh-la_s+mockiaa_ro 0.0586851
## la_s+noiaa_ro-la_s+mockiaa_ro 1.0000000
## la_s+noiaa_sh-la_s+mockiaa_ro 0.0159212
## M248_s+iaa_ro-la_s+mockiaa_ro 0.1228619
## M248_s+iaa_sh-la_s+mockiaa_ro 0.0000000
## M248_s+mockiaa_ro-la_s+mockiaa_ro 0.0987632
## M248_s+mockiaa_sh-la_s+mockiaa_ro 0.0000000
## M248_s+noiaa_ro-la_s+mockiaa_ro 0.0369576
## M248_s+noiaa_sh-la_s+mockiaa_ro 0.0000000
## la_s+noiaa_ro-la_s+mockiaa_sh 0.0295236
## la_s+noiaa_sh-la_s+mockiaa_sh 0.9999970
## M248_s+iaa_ro-la_s+mockiaa_sh 0.9999983
## M248_s+iaa_sh-la_s+mockiaa_sh 0.0000000
## M248_s+mockiaa_ro-la_s+mockiaa_sh 1.0000000
## M248_s+mockiaa_sh-la_s+mockiaa_sh 0.0000001
## M248_s+noiaa_ro-la_s+mockiaa_sh 1.0000000
## M248_s+noiaa_sh-la_s+mockiaa_sh 0.0000113
## la_s+noiaa_sh-la_s+noiaa_ro 0.0074016
## M248_s+iaa_ro-la_s+noiaa_ro 0.0640883
## M248_s+iaa_sh-la_s+noiaa_ro 0.0000000
## M248_s+mockiaa_ro-la_s+noiaa_ro 0.0519096
## M248_s+mockiaa_sh-la_s+noiaa_ro 0.0000000
## M248_s+noiaa_ro-la_s+noiaa_ro 0.0180046
## M248_s+noiaa_sh-la_s+noiaa_ro 0.0000000
## M248_s+iaa_ro-la_s+noiaa_sh 0.9975717
## M248_s+iaa_sh-la_s+noiaa_sh 0.0000000
## M248_s+mockiaa_ro-la_s+noiaa_sh 0.9998783
## M248_s+mockiaa_sh-la_s+noiaa_sh 0.0000005
## M248_s+noiaa_ro-la_s+noiaa_sh 1.0000000
## M248_s+noiaa_sh-la_s+noiaa_sh 0.0000595
## M248_s+iaa_sh-M248_s+iaa_ro 0.0000000
## M248_s+mockiaa_ro-M248_s+iaa_ro 1.0000000
## M248_s+mockiaa_sh-M248_s+iaa_ro 0.0000000
## M248_s+noiaa_ro-M248_s+iaa_ro 0.9999451
## M248_s+noiaa_sh-M248_s+iaa_ro 0.0000009
## M248_s+mockiaa_ro-M248_s+iaa_sh 0.0000000
## M248_s+mockiaa_sh-M248_s+iaa_sh 0.0002323
## M248_s+noiaa_ro-M248_s+iaa_sh 0.0000000
## M248_s+noiaa_sh-M248_s+iaa_sh 0.0000021
## M248_s+mockiaa_sh-M248_s+mockiaa_ro 0.0000000
## M248_s+noiaa_ro-M248_s+mockiaa_ro 0.9999997
## M248_s+noiaa_sh-M248_s+mockiaa_ro 0.0000054
## M248_s+noiaa_ro-M248_s+mockiaa_sh 0.0000002
## M248_s+noiaa_sh-M248_s+mockiaa_sh 0.9598417
## M248_s+noiaa_sh-M248_s+noiaa_ro 0.0000209P11 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P11)
stat.test## la_s+iaa_sh la_s+mockiaa_ro la_s+mockiaa_sh la_s+noiaa_ro
## "a" "bc" "bde" "c"
## la_s+noiaa_sh M248_s+iaa_ro M248_s+iaa_sh M248_s+mockiaa_ro
## "d" "bcde" "f" "bcde"
## M248_s+mockiaa_sh M248_s+noiaa_ro M248_s+noiaa_sh la_s+iaa_ro
## "a" "de" "a" "bce"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+iaa"test$info[[1]][3]## [1] "sh"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_IAA_10dICP_IAA_10d$All.condition <- factor(ICP_IAA_10d$All.condition, levels = c("s+noiaa", "s+mockiaa", "s+iaa"))
#ICP_IAA_10d <- subset(ICP_IAA_10d, ICP_IAA_10d$K.con..mg.mg.dry.weight < 60)
ratio_content_IAA_10d <- ggplot(data = ICP_IAA_10d, mapping = aes(x = All.condition, y = Na.K.ratio, colour = All.condition))
ratio_content_IAA_10d <- ratio_content_IAA_10d + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
ratio_content_IAA_10d <- ratio_content_IAA_10d + facet_grid(Tissue ~ Accession, scales = "free_y")
ratio_content_IAA_10d <-ratio_content_IAA_10d + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
ratio_content_IAA_10d <- ratio_content_IAA_10d + scale_color_manual(values = c("red","blueviolet","cyan"))
ratio_content_IAA_10d <- ratio_content_IAA_10d + ylab("Na/K ratio") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 1.3)
ratio_content_IAA_10d <- ratio_content_IAA_10d + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
ratio_content_IAA_10d <- ratio_content_IAA_10d + rremove("legend")
ratio_content_IAA_10d
ICP_IAA_10dnoiaa_sub <- subset(ICP_IAA_10d, ICP_IAA_10d$Condition2 %in% c("iaa", "noiaa"))
noiaa_subOutput <- TukeyHSD(aov(Na.K.ratio ~ All.ID2, data = noiaa_sub))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.K.ratio ~ All.ID2, data = noiaa_sub)
##
## $All.ID2
## diff lwr upr
## la_s+iaa_sh-la_s+iaa_ro 0.371869969 0.153498122 0.590241816
## la_s+noiaa_ro-la_s+iaa_ro -0.007033778 -0.215837202 0.201769645
## la_s+noiaa_sh-la_s+iaa_ro 0.189773322 -0.019030101 0.398576746
## M248_s+iaa_ro-la_s+iaa_ro 0.145335395 -0.056837650 0.347508441
## M248_s+iaa_sh-la_s+iaa_ro 0.773619114 0.564815691 0.982422538
## M248_s+noiaa_ro-la_s+iaa_ro 0.175046700 -0.033756724 0.383850123
## M248_s+noiaa_sh-la_s+iaa_ro 0.458336973 0.249533550 0.667140396
## la_s+noiaa_ro-la_s+iaa_sh -0.378903747 -0.570701938 -0.187105556
## la_s+noiaa_sh-la_s+iaa_sh -0.182096646 -0.373894838 0.009701545
## M248_s+iaa_ro-la_s+iaa_sh -0.226534573 -0.411092469 -0.041976678
## M248_s+iaa_sh-la_s+iaa_sh 0.401749146 0.209950954 0.593547337
## M248_s+noiaa_ro-la_s+iaa_sh -0.196823269 -0.388621460 -0.005025078
## M248_s+noiaa_sh-la_s+iaa_sh 0.086467005 -0.105331187 0.278265196
## la_s+noiaa_sh-la_s+noiaa_ro 0.196807101 0.015978032 0.377636170
## M248_s+iaa_ro-la_s+noiaa_ro 0.152369174 -0.020761479 0.325499826
## M248_s+iaa_sh-la_s+noiaa_ro 0.780652893 0.599823824 0.961481962
## M248_s+noiaa_ro-la_s+noiaa_ro 0.182080478 0.001251409 0.362909547
## M248_s+noiaa_sh-la_s+noiaa_ro 0.465370752 0.284541683 0.646199821
## M248_s+iaa_ro-la_s+noiaa_sh -0.044437927 -0.217568579 0.128692725
## M248_s+iaa_sh-la_s+noiaa_sh 0.583845792 0.403016723 0.764674861
## M248_s+noiaa_ro-la_s+noiaa_sh -0.014726623 -0.195555692 0.166102446
## M248_s+noiaa_sh-la_s+noiaa_sh 0.268563651 0.087734582 0.449392720
## M248_s+iaa_sh-M248_s+iaa_ro 0.628283719 0.455153067 0.801414371
## M248_s+noiaa_ro-M248_s+iaa_ro 0.029711304 -0.143419348 0.202841957
## M248_s+noiaa_sh-M248_s+iaa_ro 0.313001578 0.139870925 0.486132230
## M248_s+noiaa_ro-M248_s+iaa_sh -0.598572415 -0.779401484 -0.417743346
## M248_s+noiaa_sh-M248_s+iaa_sh -0.315282141 -0.496111210 -0.134453072
## M248_s+noiaa_sh-M248_s+noiaa_ro 0.283290274 0.102461205 0.464119343
## p adj
## la_s+iaa_sh-la_s+iaa_ro 0.0001241
## la_s+noiaa_ro-la_s+iaa_ro 1.0000000
## la_s+noiaa_sh-la_s+iaa_ro 0.0961141
## M248_s+iaa_ro-la_s+iaa_ro 0.3067316
## M248_s+iaa_sh-la_s+iaa_ro 0.0000000
## M248_s+noiaa_ro-la_s+iaa_ro 0.1532470
## M248_s+noiaa_sh-la_s+iaa_ro 0.0000016
## la_s+noiaa_ro-la_s+iaa_sh 0.0000108
## la_s+noiaa_sh-la_s+iaa_sh 0.0723251
## M248_s+iaa_ro-la_s+iaa_sh 0.0081614
## M248_s+iaa_sh-la_s+iaa_sh 0.0000038
## M248_s+noiaa_ro-la_s+iaa_sh 0.0410618
## M248_s+noiaa_sh-la_s+iaa_sh 0.8183078
## la_s+noiaa_sh-la_s+noiaa_ro 0.0253788
## M248_s+iaa_ro-la_s+noiaa_ro 0.1167952
## M248_s+iaa_sh-la_s+noiaa_ro 0.0000000
## M248_s+noiaa_ro-la_s+noiaa_ro 0.0474827
## M248_s+noiaa_sh-la_s+noiaa_ro 0.0000001
## M248_s+iaa_ro-la_s+noiaa_sh 0.9894688
## M248_s+iaa_sh-la_s+noiaa_sh 0.0000000
## M248_s+noiaa_ro-la_s+noiaa_sh 0.9999942
## M248_s+noiaa_sh-la_s+noiaa_sh 0.0008718
## M248_s+iaa_sh-M248_s+iaa_ro 0.0000000
## M248_s+noiaa_ro-M248_s+iaa_ro 0.9991297
## M248_s+noiaa_sh-M248_s+iaa_ro 0.0000483
## M248_s+noiaa_ro-M248_s+iaa_sh 0.0000000
## M248_s+noiaa_sh-M248_s+iaa_sh 0.0000862
## M248_s+noiaa_sh-M248_s+noiaa_ro 0.0004217P11 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P11)
stat.test## la_s+iaa_sh la_s+noiaa_ro la_s+noiaa_sh M248_s+iaa_ro M248_s+iaa_sh
## "ab" "c" "ad" "cd" "e"
## M248_s+noiaa_ro M248_s+noiaa_sh la_s+iaa_ro
## "d" "b" "cd"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+iaa"test$info[[1]][3]## [1] "sh"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ratio_noiaa_IAA_10d <- ggplot(data = noiaa_sub, mapping = aes(x = All.condition, y = Na.K.ratio, colour = All.condition))
ratio_noiaa_IAA_10d <- ratio_noiaa_IAA_10d + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
ratio_noiaa_IAA_10d <- ratio_noiaa_IAA_10d + facet_grid(Tissue ~ Accession, scales = "free_y")
ratio_noiaa_IAA_10d <- ratio_noiaa_IAA_10d + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
ratio_noiaa_IAA_10d <- ratio_noiaa_IAA_10d + scale_color_manual(values = c("red","blueviolet","cyan"))
ratio_noiaa_IAA_10d <- ratio_noiaa_IAA_10d + ylab("Na/K ratio") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 1.3)
ratio_noiaa_IAA_10d <- ratio_noiaa_IAA_10d + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
ratio_noiaa_IAA_10d <- ratio_noiaa_IAA_10d + rremove("legend")
ratio_noiaa_IAA_10d
now lets calculate parameters for the 4weeks-IAA spray.
list.files(pattern=".csv")## [1] "ICP-ACC-SOIL-SPAR-SALT-10d.csv" "ICP-ACC-SOIL-SPAR-SALT-4weeks.csv"
## [3] "ICP-IAA-SOIL-SPAR-SALT-10d.csv" "ICP-IAA-SOIL-SPAR-SALT-4weeks.csv"ICP_IAA_4w <- read.csv("ICP-IAA-SOIL-SPAR-SALT-4weeks.csv")
ICP_IAA_4wICP_IAA_4w$All.condition<-paste(ICP_IAA_4w$Condition1,ICP_IAA_4w$Condition2, sep="+")
ICP_IAA_4wICP_IAA_4w$All.ID2<-paste(ICP_IAA_4w$Accession,ICP_IAA_4w$All.condition, ICP_IAA_4w$Tissue,sep="_")
ICP_IAA_4waov(Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_4w)## Call:
## aov(formula = Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_4w)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 198.2856 182.1736
## Deg. of Freedom 11 48
##
## Residual standard error: 1.948149
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_4w))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.con.mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_4w)
##
## $All.ID2
## diff lwr upr
## la_s+iaa_tip-la_s+iaa_ro -0.74775849 -4.79840332 3.3028863
## la_s+mockiaa_ro-la_s+iaa_ro 0.91854601 -3.31221452 5.1493065
## la_s+mockiaa_tip-la_s+iaa_ro -1.84574473 -6.07650527 2.3850158
## la_s+noiaa_ro-la_s+iaa_ro -0.29569520 -4.52645574 3.9350653
## la_s+noiaa_tip-la_s+iaa_ro -1.97225529 -6.20301582 2.2585052
## M248_s+iaa_ro-la_s+iaa_ro 2.44021729 -2.04718191 6.9276165
## M248_s+iaa_tip-la_s+iaa_ro -0.67819241 -4.90895294 3.5525681
## M248_s+mockiaa_ro-la_s+iaa_ro 3.09487625 -1.13588429 7.3256368
## M248_s+mockiaa_tip-la_s+iaa_ro -1.65472930 -5.88548983 2.5760312
## M248_s+noiaa_ro-la_s+iaa_ro 3.11248204 -1.11827850 7.3432426
## M248_s+noiaa_tip-la_s+iaa_ro -1.87951224 -6.11027278 2.3512483
## la_s+mockiaa_ro-la_s+iaa_tip 1.66630451 -2.38434032 5.7169493
## la_s+mockiaa_tip-la_s+iaa_tip -1.09798624 -5.14863106 2.9526586
## la_s+noiaa_ro-la_s+iaa_tip 0.45206329 -3.59858153 4.5027081
## la_s+noiaa_tip-la_s+iaa_tip -1.22449679 -5.27514162 2.8261480
## M248_s+iaa_ro-la_s+iaa_tip 3.18797578 -1.13002611 7.5059777
## M248_s+iaa_tip-la_s+iaa_tip 0.06956608 -3.98107874 4.1202109
## M248_s+mockiaa_ro-la_s+iaa_tip 3.84263474 -0.20801008 7.8932796
## M248_s+mockiaa_tip-la_s+iaa_tip -0.90697080 -4.95761563 3.1436740
## M248_s+noiaa_ro-la_s+iaa_tip 3.86024053 -0.19040429 7.9108854
## M248_s+noiaa_tip-la_s+iaa_tip -1.13175375 -5.18239857 2.9188911
## la_s+mockiaa_tip-la_s+mockiaa_ro -2.76429074 -6.99505128 1.4664698
## la_s+noiaa_ro-la_s+mockiaa_ro -1.21424121 -5.44500175 3.0165193
## la_s+noiaa_tip-la_s+mockiaa_ro -2.89080130 -7.12156183 1.3399592
## M248_s+iaa_ro-la_s+mockiaa_ro 1.52167127 -2.96572792 6.0090705
## M248_s+iaa_tip-la_s+mockiaa_ro -1.59673842 -5.82749896 2.6340221
## M248_s+mockiaa_ro-la_s+mockiaa_ro 2.17633023 -2.05443030 6.4070908
## M248_s+mockiaa_tip-la_s+mockiaa_ro -2.57327531 -6.80403584 1.6574852
## M248_s+noiaa_ro-la_s+mockiaa_ro 2.19393603 -2.03682451 6.4246966
## M248_s+noiaa_tip-la_s+mockiaa_ro -2.79805825 -7.02881879 1.4327023
## la_s+noiaa_ro-la_s+mockiaa_tip 1.55004953 -2.68071100 5.7808101
## la_s+noiaa_tip-la_s+mockiaa_tip -0.12651055 -4.35727109 4.1042500
## M248_s+iaa_ro-la_s+mockiaa_tip 4.28596202 -0.20143718 8.7733612
## M248_s+iaa_tip-la_s+mockiaa_tip 1.16755232 -3.06320821 5.3983129
## M248_s+mockiaa_ro-la_s+mockiaa_tip 4.94062098 0.70986044 9.1713815
## M248_s+mockiaa_tip-la_s+mockiaa_tip 0.19101544 -4.03974510 4.4217760
## M248_s+noiaa_ro-la_s+mockiaa_tip 4.95822677 0.72746624 9.1889873
## M248_s+noiaa_tip-la_s+mockiaa_tip -0.03376751 -4.26452804 4.1969930
## la_s+noiaa_tip-la_s+noiaa_ro -1.67656008 -5.90732062 2.5542005
## M248_s+iaa_ro-la_s+noiaa_ro 2.73591249 -1.75148671 7.2233117
## M248_s+iaa_tip-la_s+noiaa_ro -0.38249721 -4.61325774 3.8482633
## M248_s+mockiaa_ro-la_s+noiaa_ro 3.39057145 -0.84018909 7.6213320
## M248_s+mockiaa_tip-la_s+noiaa_ro -1.35903409 -5.58979463 2.8717264
## M248_s+noiaa_ro-la_s+noiaa_ro 3.40817724 -0.82258330 7.6389378
## M248_s+noiaa_tip-la_s+noiaa_ro -1.58381704 -5.81457757 2.6469435
## M248_s+iaa_ro-la_s+noiaa_tip 4.41247257 -0.07492662 8.8998718
## M248_s+iaa_tip-la_s+noiaa_tip 1.29406288 -2.93669766 5.5248234
## M248_s+mockiaa_ro-la_s+noiaa_tip 5.06713153 0.83637100 9.2978921
## M248_s+mockiaa_tip-la_s+noiaa_tip 0.31752599 -3.91323454 4.5482865
## M248_s+noiaa_ro-la_s+noiaa_tip 5.08473732 0.85397679 9.3154979
## M248_s+noiaa_tip-la_s+noiaa_tip 0.09274305 -4.13801749 4.3235036
## M248_s+iaa_tip-M248_s+iaa_ro -3.11840970 -7.60580889 1.3689895
## M248_s+mockiaa_ro-M248_s+iaa_ro 0.65465896 -3.83274024 5.1420582
## M248_s+mockiaa_tip-M248_s+iaa_ro -4.09494658 -8.58234578 0.3924526
## M248_s+noiaa_ro-M248_s+iaa_ro 0.67226475 -3.81513444 5.1596639
## M248_s+noiaa_tip-M248_s+iaa_ro -4.31972953 -8.80712872 0.1676697
## M248_s+mockiaa_ro-M248_s+iaa_tip 3.77306865 -0.45769188 8.0038292
## M248_s+mockiaa_tip-M248_s+iaa_tip -0.97653689 -5.20729742 3.2542236
## M248_s+noiaa_ro-M248_s+iaa_tip 3.79067445 -0.44008609 8.0214350
## M248_s+noiaa_tip-M248_s+iaa_tip -1.20131983 -5.43208037 3.0294407
## M248_s+mockiaa_tip-M248_s+mockiaa_ro -4.74960554 -8.98036608 -0.5188450
## M248_s+noiaa_ro-M248_s+mockiaa_ro 0.01760579 -4.21315474 4.2483663
## M248_s+noiaa_tip-M248_s+mockiaa_ro -4.97438849 -9.20514902 -0.7436280
## M248_s+noiaa_ro-M248_s+mockiaa_tip 4.76721133 0.53645080 8.9979719
## M248_s+noiaa_tip-M248_s+mockiaa_tip -0.22478295 -4.45554348 4.0059776
## M248_s+noiaa_tip-M248_s+noiaa_ro -4.99199428 -9.22275481 -0.7612337
## p adj
## la_s+iaa_tip-la_s+iaa_ro 0.9999589
## la_s+mockiaa_ro-la_s+iaa_ro 0.9997980
## la_s+mockiaa_tip-la_s+iaa_ro 0.9339398
## la_s+noiaa_ro-la_s+iaa_ro 1.0000000
## la_s+noiaa_tip-la_s+iaa_ro 0.9003781
## M248_s+iaa_ro-la_s+iaa_ro 0.7725545
## M248_s+iaa_tip-la_s+iaa_ro 0.9999902
## M248_s+mockiaa_ro-la_s+iaa_ro 0.3575804
## M248_s+mockiaa_tip-la_s+iaa_ro 0.9685741
## M248_s+noiaa_ro-la_s+iaa_ro 0.3493041
## M248_s+noiaa_tip-la_s+iaa_ro 0.9258534
## la_s+mockiaa_ro-la_s+iaa_tip 0.9552689
## la_s+mockiaa_tip-la_s+iaa_tip 0.9984040
## la_s+noiaa_ro-la_s+iaa_tip 0.9999998
## la_s+noiaa_tip-la_s+iaa_tip 0.9958625
## M248_s+iaa_ro-la_s+iaa_tip 0.3441470
## M248_s+iaa_tip-la_s+iaa_tip 1.0000000
## M248_s+mockiaa_ro-la_s+iaa_tip 0.0775617
## M248_s+mockiaa_tip-la_s+iaa_tip 0.9997285
## M248_s+noiaa_ro-la_s+iaa_tip 0.0748060
## M248_s+noiaa_tip-la_s+iaa_tip 0.9979107
## la_s+mockiaa_tip-la_s+mockiaa_ro 0.5282340
## la_s+noiaa_ro-la_s+mockiaa_ro 0.9973553
## la_s+noiaa_tip-la_s+mockiaa_ro 0.4601555
## M248_s+iaa_ro-la_s+mockiaa_ro 0.9893330
## M248_s+iaa_tip-la_s+mockiaa_ro 0.9757467
## M248_s+mockiaa_ro-la_s+mockiaa_ro 0.8274342
## M248_s+mockiaa_tip-la_s+mockiaa_ro 0.6328465
## M248_s+noiaa_ro-la_s+mockiaa_ro 0.8201048
## M248_s+noiaa_tip-la_s+mockiaa_ro 0.5098523
## la_s+noiaa_ro-la_s+mockiaa_tip 0.9805614
## la_s+noiaa_tip-la_s+mockiaa_tip 1.0000000
## M248_s+iaa_ro-la_s+mockiaa_tip 0.0734949
## M248_s+iaa_tip-la_s+mockiaa_tip 0.9981274
## M248_s+mockiaa_ro-la_s+mockiaa_tip 0.0102241
## M248_s+mockiaa_tip-la_s+mockiaa_tip 1.0000000
## M248_s+noiaa_ro-la_s+mockiaa_tip 0.0098047
## M248_s+noiaa_tip-la_s+mockiaa_tip 1.0000000
## la_s+noiaa_tip-la_s+noiaa_ro 0.9655035
## M248_s+iaa_ro-la_s+noiaa_ro 0.6294964
## M248_s+iaa_tip-la_s+noiaa_ro 1.0000000
## M248_s+mockiaa_ro-la_s+noiaa_ro 0.2334630
## M248_s+mockiaa_tip-la_s+noiaa_ro 0.9931199
## M248_s+noiaa_ro-la_s+noiaa_ro 0.2271260
## M248_s+noiaa_tip-la_s+noiaa_ro 0.9771609
## M248_s+iaa_ro-la_s+noiaa_tip 0.0578303
## M248_s+iaa_tip-la_s+noiaa_tip 0.9954288
## M248_s+mockiaa_ro-la_s+noiaa_tip 0.0075492
## M248_s+mockiaa_tip-la_s+noiaa_tip 1.0000000
## M248_s+noiaa_ro-la_s+noiaa_tip 0.0072341
## M248_s+noiaa_tip-la_s+noiaa_tip 1.0000000
## M248_s+iaa_tip-M248_s+iaa_ro 0.4344068
## M248_s+mockiaa_ro-M248_s+iaa_ro 0.9999963
## M248_s+mockiaa_tip-M248_s+iaa_ro 0.1039680
## M248_s+noiaa_ro-M248_s+iaa_ro 0.9999951
## M248_s+noiaa_tip-M248_s+iaa_ro 0.0689917
## M248_s+mockiaa_ro-M248_s+iaa_tip 0.1222402
## M248_s+mockiaa_tip-M248_s+iaa_tip 0.9996374
## M248_s+noiaa_ro-M248_s+iaa_tip 0.1183755
## M248_s+noiaa_tip-M248_s+iaa_tip 0.9975916
## M248_s+mockiaa_tip-M248_s+mockiaa_ro 0.0159918
## M248_s+noiaa_ro-M248_s+mockiaa_ro 1.0000000
## M248_s+noiaa_tip-M248_s+mockiaa_ro 0.0094340
## M248_s+noiaa_ro-M248_s+mockiaa_tip 0.0153545
## M248_s+noiaa_tip-M248_s+mockiaa_tip 1.0000000
## M248_s+noiaa_tip-M248_s+noiaa_ro 0.0090451P10 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P10)
stat.test## la_s+iaa_tip la_s+mockiaa_ro la_s+mockiaa_tip la_s+noiaa_ro
## "ab" "ab" "a" "ab"
## la_s+noiaa_tip M248_s+iaa_ro M248_s+iaa_tip M248_s+mockiaa_ro
## "a" "ab" "ab" "b"
## M248_s+mockiaa_tip M248_s+noiaa_ro M248_s+noiaa_tip la_s+iaa_ro
## "a" "b" "a" "ab"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+iaa"test$info[[1]][3]## [1] "tip"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_IAA_4wICP_IAA_4w$All.condition <- factor(ICP_IAA_4w$All.condition, levels = c("s+noiaa", "s+mockiaa", "s+iaa"))
ICP_IAA_4w <- subset(ICP_IAA_4w, ICP_IAA_4w$Na.con.mg.mg.dry.weight < 15)
Na_content_IAA_4w <- ggplot(data = ICP_IAA_4w, mapping = aes(x = All.condition, y = Na.con.mg.mg.dry.weight, colour = All.condition))
Na_content_IAA_4w <- Na_content_IAA_4w + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
Na_content_IAA_4w <- Na_content_IAA_4w + facet_grid(Tissue ~ Accession, scales = "free_y")
Na_content_IAA_4w <- Na_content_IAA_4w + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
Na_content_IAA_4w<- Na_content_IAA_4w + scale_color_manual(values = c("red","blueviolet","cyan"))
Na_content_IAA_4w <- Na_content_IAA_4w + ylab("Na content, mg/mg dry weight") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 15)
Na_content_IAA_4w <- Na_content_IAA_4w + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
Na_content_IAA_4w <- Na_content_IAA_4w + rremove("legend")
Na_content_IAA_4w
unique(ICP_IAA_4w$All.condition)## [1] s+noiaa s+mockiaa s+iaa
## Levels: s+noiaa s+mockiaa s+iaaaov(K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_4w)## Call:
## aov(formula = K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_4w)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 1969.381 1777.963
## Deg. of Freedom 11 48
##
## Residual standard error: 6.086123
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_4w))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K.con..mg.mg.dry.weight ~ All.ID2, data = ICP_IAA_4w)
##
## $All.ID2
## diff lwr upr
## la_s+iaa_tip-la_s+iaa_ro -9.40526108 -22.059695 3.2491726
## la_s+mockiaa_ro-la_s+iaa_ro -0.27480440 -13.491929 12.9423205
## la_s+mockiaa_tip-la_s+iaa_ro -11.90007468 -25.117200 1.3170502
## la_s+noiaa_ro-la_s+iaa_ro -4.97969844 -18.196823 8.2374265
## la_s+noiaa_tip-la_s+iaa_ro -8.24828724 -21.465412 4.9688377
## M248_s+iaa_ro-la_s+iaa_ro -5.18709544 -19.205973 8.8317825
## M248_s+iaa_tip-la_s+iaa_ro -15.53065078 -28.747776 -2.3135259
## M248_s+mockiaa_ro-la_s+iaa_ro -1.77888084 -14.996006 11.4382441
## M248_s+mockiaa_tip-la_s+iaa_ro -14.79151480 -28.008640 -1.5743899
## M248_s+noiaa_ro-la_s+iaa_ro -0.20963164 -13.426757 13.0074933
## M248_s+noiaa_tip-la_s+iaa_ro -14.39062340 -27.607748 -1.1734985
## la_s+mockiaa_ro-la_s+iaa_tip 9.13045668 -3.523977 21.7848903
## la_s+mockiaa_tip-la_s+iaa_tip -2.49481360 -15.149247 10.1596201
## la_s+noiaa_ro-la_s+iaa_tip 4.42556264 -8.228871 17.0799963
## la_s+noiaa_tip-la_s+iaa_tip 1.15697384 -11.497460 13.8114075
## M248_s+iaa_ro-la_s+iaa_tip 4.21816565 -9.271506 17.7078373
## M248_s+iaa_tip-la_s+iaa_tip -6.12538970 -18.779823 6.5290440
## M248_s+mockiaa_ro-la_s+iaa_tip 7.62638024 -5.028053 20.2808139
## M248_s+mockiaa_tip-la_s+iaa_tip -5.38625372 -18.040687 7.2681800
## M248_s+noiaa_ro-la_s+iaa_tip 9.19562944 -3.458804 21.8500631
## M248_s+noiaa_tip-la_s+iaa_tip -4.98536232 -17.639796 7.6690713
## la_s+mockiaa_tip-la_s+mockiaa_ro -11.62527027 -24.842395 1.5918546
## la_s+noiaa_ro-la_s+mockiaa_ro -4.70489404 -17.922019 8.5122309
## la_s+noiaa_tip-la_s+mockiaa_ro -7.97348283 -21.190608 5.2436421
## M248_s+iaa_ro-la_s+mockiaa_ro -4.91229103 -18.931169 9.1065869
## M248_s+iaa_tip-la_s+mockiaa_ro -15.25584637 -28.472971 -2.0387215
## M248_s+mockiaa_ro-la_s+mockiaa_ro -1.50407644 -14.721201 11.7130485
## M248_s+mockiaa_tip-la_s+mockiaa_ro -14.51671040 -27.733835 -1.2995855
## M248_s+noiaa_ro-la_s+mockiaa_ro 0.06517276 -13.151952 13.2822977
## M248_s+noiaa_tip-la_s+mockiaa_ro -14.11581900 -27.332944 -0.8986941
## la_s+noiaa_ro-la_s+mockiaa_tip 6.92037624 -6.296749 20.1375011
## la_s+noiaa_tip-la_s+mockiaa_tip 3.65178744 -9.565337 16.8689123
## M248_s+iaa_ro-la_s+mockiaa_tip 6.71297924 -7.305899 20.7318572
## M248_s+iaa_tip-la_s+mockiaa_tip -3.63057610 -16.847701 9.5865488
## M248_s+mockiaa_ro-la_s+mockiaa_tip 10.12119384 -3.095931 23.3383187
## M248_s+mockiaa_tip-la_s+mockiaa_tip -2.89144012 -16.108565 10.3256848
## M248_s+noiaa_ro-la_s+mockiaa_tip 11.69044304 -1.526682 24.9075679
## M248_s+noiaa_tip-la_s+mockiaa_tip -2.49054872 -15.707674 10.7265762
## la_s+noiaa_tip-la_s+noiaa_ro -3.26858880 -16.485714 9.9485361
## M248_s+iaa_ro-la_s+noiaa_ro -0.20739700 -14.226275 13.8114810
## M248_s+iaa_tip-la_s+noiaa_ro -10.55095234 -23.768077 2.6661726
## M248_s+mockiaa_ro-la_s+noiaa_ro 3.20081760 -10.016307 16.4179425
## M248_s+mockiaa_tip-la_s+noiaa_ro -9.81181636 -23.028941 3.4053085
## M248_s+noiaa_ro-la_s+noiaa_ro 4.77006680 -8.447058 17.9871917
## M248_s+noiaa_tip-la_s+noiaa_ro -9.41092496 -22.628050 3.8061999
## M248_s+iaa_ro-la_s+noiaa_tip 3.06119180 -10.957686 17.0800698
## M248_s+iaa_tip-la_s+noiaa_tip -7.28236354 -20.499488 5.9347614
## M248_s+mockiaa_ro-la_s+noiaa_tip 6.46940640 -6.747719 19.6865313
## M248_s+mockiaa_tip-la_s+noiaa_tip -6.54322756 -19.760352 6.6738973
## M248_s+noiaa_ro-la_s+noiaa_tip 8.03865560 -5.178469 21.2557805
## M248_s+noiaa_tip-la_s+noiaa_tip -6.14233616 -19.359461 7.0747887
## M248_s+iaa_tip-M248_s+iaa_ro -10.34355534 -24.362433 3.6753226
## M248_s+mockiaa_ro-M248_s+iaa_ro 3.40821459 -10.610663 17.4270926
## M248_s+mockiaa_tip-M248_s+iaa_ro -9.60441936 -23.623297 4.4144586
## M248_s+noiaa_ro-M248_s+iaa_ro 4.97746380 -9.041414 18.9963418
## M248_s+noiaa_tip-M248_s+iaa_ro -9.20352797 -23.222406 4.8153500
## M248_s+mockiaa_ro-M248_s+iaa_tip 13.75176994 0.534645 26.9688948
## M248_s+mockiaa_tip-M248_s+iaa_tip 0.73913598 -12.477989 13.9562609
## M248_s+noiaa_ro-M248_s+iaa_tip 15.32101914 2.103894 28.5381440
## M248_s+noiaa_tip-M248_s+iaa_tip 1.14002738 -12.077098 14.3571523
## M248_s+mockiaa_tip-M248_s+mockiaa_ro -13.01263396 -26.229759 0.2044909
## M248_s+noiaa_ro-M248_s+mockiaa_ro 1.56924920 -11.647876 14.7863741
## M248_s+noiaa_tip-M248_s+mockiaa_ro -12.61174256 -25.828867 0.6053823
## M248_s+noiaa_ro-M248_s+mockiaa_tip 14.58188316 1.364758 27.7990081
## M248_s+noiaa_tip-M248_s+mockiaa_tip 0.40089140 -12.816234 13.6180163
## M248_s+noiaa_tip-M248_s+noiaa_ro -14.18099176 -27.398117 -0.9638669
## p adj
## la_s+iaa_tip-la_s+iaa_ro 0.3345373
## la_s+mockiaa_ro-la_s+iaa_ro 1.0000000
## la_s+mockiaa_tip-la_s+iaa_ro 0.1144217
## la_s+noiaa_ro-la_s+iaa_ro 0.9760534
## la_s+noiaa_tip-la_s+iaa_ro 0.5962758
## M248_s+iaa_ro-la_s+iaa_ro 0.9790658
## M248_s+iaa_tip-la_s+iaa_ro 0.0095035
## M248_s+mockiaa_ro-la_s+iaa_ro 0.9999984
## M248_s+mockiaa_tip-la_s+iaa_ro 0.0165497
## M248_s+noiaa_ro-la_s+iaa_ro 1.0000000
## M248_s+noiaa_tip-la_s+iaa_ro 0.0221611
## la_s+mockiaa_ro-la_s+iaa_tip 0.3778693
## la_s+mockiaa_tip-la_s+iaa_tip 0.9999211
## la_s+noiaa_ro-la_s+iaa_tip 0.9863961
## la_s+noiaa_tip-la_s+iaa_tip 1.0000000
## M248_s+iaa_ro-la_s+iaa_tip 0.9944967
## M248_s+iaa_tip-la_s+iaa_tip 0.8760080
## M248_s+mockiaa_ro-la_s+iaa_tip 0.6455907
## M248_s+mockiaa_tip-la_s+iaa_tip 0.9437489
## M248_s+noiaa_ro-la_s+iaa_tip 0.3673443
## M248_s+noiaa_tip-la_s+iaa_tip 0.9669083
## la_s+mockiaa_tip-la_s+mockiaa_ro 0.1342192
## la_s+noiaa_ro-la_s+mockiaa_ro 0.9843952
## la_s+noiaa_tip-la_s+mockiaa_ro 0.6442104
## M248_s+iaa_ro-la_s+mockiaa_ro 0.9861882
## M248_s+iaa_tip-la_s+mockiaa_ro 0.0117078
## M248_s+mockiaa_ro-la_s+mockiaa_ro 0.9999997
## M248_s+mockiaa_tip-la_s+mockiaa_ro 0.0202312
## M248_s+noiaa_ro-la_s+mockiaa_ro 1.0000000
## M248_s+noiaa_tip-la_s+mockiaa_ro 0.0269658
## la_s+noiaa_ro-la_s+mockiaa_tip 0.8110536
## la_s+noiaa_tip-la_s+mockiaa_tip 0.9981078
## M248_s+iaa_ro-la_s+mockiaa_tip 0.8834244
## M248_s+iaa_tip-la_s+mockiaa_tip 0.9982033
## M248_s+mockiaa_ro-la_s+mockiaa_tip 0.2926867
## M248_s+mockiaa_tip-la_s+mockiaa_tip 0.9997827
## M248_s+noiaa_ro-la_s+mockiaa_tip 0.1292917
## M248_s+noiaa_tip-la_s+mockiaa_tip 0.9999496
## la_s+noiaa_tip-la_s+noiaa_ro 0.9993081
## M248_s+iaa_ro-la_s+noiaa_ro 1.0000000
## M248_s+iaa_tip-la_s+noiaa_ro 0.2383102
## M248_s+mockiaa_ro-la_s+noiaa_ro 0.9994306
## M248_s+mockiaa_tip-la_s+noiaa_ro 0.3362423
## M248_s+noiaa_ro-la_s+noiaa_ro 0.9826596
## M248_s+noiaa_tip-la_s+noiaa_ro 0.3976554
## M248_s+iaa_ro-la_s+noiaa_tip 0.9997865
## M248_s+iaa_tip-la_s+noiaa_tip 0.7581117
## M248_s+mockiaa_ro-la_s+noiaa_tip 0.8679874
## M248_s+mockiaa_tip-la_s+noiaa_tip 0.8594203
## M248_s+noiaa_ro-la_s+noiaa_tip 0.6329153
## M248_s+noiaa_tip-la_s+noiaa_tip 0.9022063
## M248_s+iaa_tip-M248_s+iaa_ro 0.3450660
## M248_s+mockiaa_ro-M248_s+iaa_ro 0.9994096
## M248_s+mockiaa_tip-M248_s+iaa_ro 0.4560869
## M248_s+noiaa_ro-M248_s+iaa_ro 0.9847020
## M248_s+noiaa_tip-M248_s+iaa_ro 0.5210109
## M248_s+mockiaa_ro-M248_s+iaa_tip 0.0347900
## M248_s+mockiaa_tip-M248_s+iaa_tip 1.0000000
## M248_s+noiaa_ro-M248_s+iaa_tip 0.0111454
## M248_s+noiaa_tip-M248_s+iaa_tip 1.0000000
## M248_s+mockiaa_tip-M248_s+mockiaa_ro 0.0572210
## M248_s+noiaa_ro-M248_s+mockiaa_ro 0.9999996
## M248_s+noiaa_tip-M248_s+mockiaa_ro 0.0740583
## M248_s+noiaa_ro-M248_s+mockiaa_tip 0.0192954
## M248_s+noiaa_tip-M248_s+mockiaa_tip 1.0000000
## M248_s+noiaa_tip-M248_s+noiaa_ro 0.0257473P9 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P9)
stat.test## la_s+iaa_tip la_s+mockiaa_ro la_s+mockiaa_tip la_s+noiaa_ro
## "abc" "a" "abc" "abc"
## la_s+noiaa_tip M248_s+iaa_ro M248_s+iaa_tip M248_s+mockiaa_ro
## "abc" "abc" "b" "ac"
## M248_s+mockiaa_tip M248_s+noiaa_ro M248_s+noiaa_tip la_s+iaa_ro
## "bc" "a" "bc" "a"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+iaa"test$info[[1]][3]## [1] "tip"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_IAA_4wICP_IAA_4w$All.condition <- factor(ICP_IAA_4w$All.condition, levels = c("s+noiaa", "s+mockiaa", "s+iaa"))
ICP_IAA_4w <- subset(ICP_IAA_4w, ICP_IAA_4w$K.con..mg.mg.dry.weight < 35)
k_content_IAA_4w <- ggplot(data = ICP_IAA_4w, mapping = aes(x = All.condition, y = K.con..mg.mg.dry.weight, colour = All.condition))
k_content_IAA_4w <- k_content_IAA_4w + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
k_content_IAA_4w <- k_content_IAA_4w + facet_grid(Tissue ~ Accession, scales = "free_y")
k_content_IAA_4w <-k_content_IAA_4w + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
k_content_IAA_4w<- k_content_IAA_4w + scale_color_manual(values = c("red","blueviolet","cyan"))
k_content_IAA_4w <- k_content_IAA_4w + ylab("k content, mg/mg dry weight") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 35)
k_content_IAA_4w <- k_content_IAA_4w + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
k_content_IAA_4w <-k_content_IAA_4w + rremove("legend")
k_content_IAA_4w
aov(Na.K.ratio~ All.ID2, data = ICP_IAA_4w)## Call:
## aov(formula = Na.K.ratio ~ All.ID2, data = ICP_IAA_4w)
##
## Terms:
## All.ID2 Residuals
## Sum of Squares 0.3024536 0.7409993
## Deg. of Freedom 11 46
##
## Residual standard error: 0.12692
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(Na.K.ratio ~ All.ID2, data = ICP_IAA_4w))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.K.ratio ~ All.ID2, data = ICP_IAA_4w)
##
## $All.ID2
## diff lwr upr
## la_s+iaa_tip-la_s+iaa_ro 0.083229360 -0.18123043 0.34768915
## la_s+mockiaa_ro-la_s+iaa_ro 0.120886265 -0.17208849 0.41386102
## la_s+mockiaa_tip-la_s+iaa_ro 0.077979764 -0.19823948 0.35419901
## la_s+noiaa_ro-la_s+iaa_ro 0.043040360 -0.23317889 0.31925961
## la_s+noiaa_tip-la_s+iaa_ro 0.068048596 -0.22492616 0.36102335
## M248_s+iaa_ro-la_s+iaa_ro 0.166968059 -0.12600669 0.45994281
## M248_s+iaa_tip-la_s+iaa_ro 0.296750164 0.02053092 0.57296941
## M248_s+mockiaa_ro-la_s+iaa_ro 0.146534097 -0.12968515 0.42275334
## M248_s+mockiaa_tip-la_s+iaa_ro 0.147815557 -0.12840369 0.42403480
## M248_s+noiaa_ro-la_s+iaa_ro 0.117008851 -0.15921039 0.39322810
## M248_s+noiaa_tip-la_s+iaa_ro 0.092356069 -0.18386318 0.36857531
## la_s+mockiaa_ro-la_s+iaa_tip 0.037656905 -0.24425818 0.31957199
## la_s+mockiaa_tip-la_s+iaa_tip -0.005249596 -0.26970939 0.25921020
## la_s+noiaa_ro-la_s+iaa_tip -0.040189000 -0.30464879 0.22427079
## la_s+noiaa_tip-la_s+iaa_tip -0.015180763 -0.29709585 0.26673432
## M248_s+iaa_ro-la_s+iaa_tip 0.083738699 -0.19817639 0.36565379
## M248_s+iaa_tip-la_s+iaa_tip 0.213520805 -0.05093899 0.47798060
## M248_s+mockiaa_ro-la_s+iaa_tip 0.063304737 -0.20115506 0.32776453
## M248_s+mockiaa_tip-la_s+iaa_tip 0.064586197 -0.19987360 0.32904599
## M248_s+noiaa_ro-la_s+iaa_tip 0.033779492 -0.23068030 0.29823929
## M248_s+noiaa_tip-la_s+iaa_tip 0.009126709 -0.25533308 0.27358650
## la_s+mockiaa_tip-la_s+mockiaa_ro -0.042906501 -0.33588125 0.25006825
## la_s+noiaa_ro-la_s+mockiaa_ro -0.077845905 -0.37082066 0.21512885
## la_s+noiaa_tip-la_s+mockiaa_ro -0.052837669 -0.36166017 0.25598484
## M248_s+iaa_ro-la_s+mockiaa_ro 0.046081794 -0.26274071 0.35490430
## M248_s+iaa_tip-la_s+mockiaa_ro 0.175863899 -0.11711085 0.46883865
## M248_s+mockiaa_ro-la_s+mockiaa_ro 0.025647832 -0.26732692 0.31862258
## M248_s+mockiaa_tip-la_s+mockiaa_ro 0.026929291 -0.26604546 0.31990404
## M248_s+noiaa_ro-la_s+mockiaa_ro -0.003877414 -0.29685217 0.28909734
## M248_s+noiaa_tip-la_s+mockiaa_ro -0.028530196 -0.32150495 0.26444456
## la_s+noiaa_ro-la_s+mockiaa_tip -0.034939404 -0.31115865 0.24127984
## la_s+noiaa_tip-la_s+mockiaa_tip -0.009931168 -0.30290592 0.28304359
## M248_s+iaa_ro-la_s+mockiaa_tip 0.088988295 -0.20398646 0.38196305
## M248_s+iaa_tip-la_s+mockiaa_tip 0.218770400 -0.05744885 0.49498965
## M248_s+mockiaa_ro-la_s+mockiaa_tip 0.068554333 -0.20766491 0.34477358
## M248_s+mockiaa_tip-la_s+mockiaa_tip 0.069835792 -0.20638345 0.34605504
## M248_s+noiaa_ro-la_s+mockiaa_tip 0.039029087 -0.23719016 0.31524833
## M248_s+noiaa_tip-la_s+mockiaa_tip 0.014376305 -0.26184294 0.29059555
## la_s+noiaa_tip-la_s+noiaa_ro 0.025008236 -0.26796652 0.31798299
## M248_s+iaa_ro-la_s+noiaa_ro 0.123927699 -0.16904705 0.41690245
## M248_s+iaa_tip-la_s+noiaa_ro 0.253709804 -0.02250944 0.52992905
## M248_s+mockiaa_ro-la_s+noiaa_ro 0.103493737 -0.17272551 0.37971298
## M248_s+mockiaa_tip-la_s+noiaa_ro 0.104775197 -0.17144405 0.38099444
## M248_s+noiaa_ro-la_s+noiaa_ro 0.073968491 -0.20225075 0.35018774
## M248_s+noiaa_tip-la_s+noiaa_ro 0.049315709 -0.22690354 0.32553495
## M248_s+iaa_ro-la_s+noiaa_tip 0.098919463 -0.20990304 0.40774197
## M248_s+iaa_tip-la_s+noiaa_tip 0.228701568 -0.06427318 0.52167632
## M248_s+mockiaa_ro-la_s+noiaa_tip 0.078485501 -0.21448925 0.37146025
## M248_s+mockiaa_tip-la_s+noiaa_tip 0.079766960 -0.21320779 0.37274171
## M248_s+noiaa_ro-la_s+noiaa_tip 0.048960255 -0.24401450 0.34193501
## M248_s+noiaa_tip-la_s+noiaa_tip 0.024307472 -0.26866728 0.31728223
## M248_s+iaa_tip-M248_s+iaa_ro 0.129782106 -0.16319265 0.42275686
## M248_s+mockiaa_ro-M248_s+iaa_ro -0.020433962 -0.31340871 0.27254079
## M248_s+mockiaa_tip-M248_s+iaa_ro -0.019152502 -0.31212726 0.27382225
## M248_s+noiaa_ro-M248_s+iaa_ro -0.049959208 -0.34293396 0.24301555
## M248_s+noiaa_tip-M248_s+iaa_ro -0.074611990 -0.36758674 0.21836276
## M248_s+mockiaa_ro-M248_s+iaa_tip -0.150216067 -0.42643531 0.12600318
## M248_s+mockiaa_tip-M248_s+iaa_tip -0.148934608 -0.42515385 0.12728464
## M248_s+noiaa_ro-M248_s+iaa_tip -0.179741313 -0.45596056 0.09647793
## M248_s+noiaa_tip-M248_s+iaa_tip -0.204394096 -0.48061334 0.07182515
## M248_s+mockiaa_tip-M248_s+mockiaa_ro 0.001281459 -0.27493779 0.27750071
## M248_s+noiaa_ro-M248_s+mockiaa_ro -0.029525246 -0.30574449 0.24669400
## M248_s+noiaa_tip-M248_s+mockiaa_ro -0.054178028 -0.33039727 0.22204122
## M248_s+noiaa_ro-M248_s+mockiaa_tip -0.030806705 -0.30702595 0.24541254
## M248_s+noiaa_tip-M248_s+mockiaa_tip -0.055459488 -0.33167873 0.22075976
## M248_s+noiaa_tip-M248_s+noiaa_ro -0.024652783 -0.30087203 0.25156646
## p adj
## la_s+iaa_tip-la_s+iaa_ro 0.9940296
## la_s+mockiaa_ro-la_s+iaa_ro 0.9534035
## la_s+mockiaa_tip-la_s+iaa_ro 0.9976405
## la_s+noiaa_ro-la_s+iaa_ro 0.9999924
## la_s+noiaa_tip-la_s+iaa_ro 0.9996020
## M248_s+iaa_ro-la_s+iaa_ro 0.7156792
## M248_s+iaa_tip-la_s+iaa_ro 0.0255183
## M248_s+mockiaa_ro-la_s+iaa_ro 0.7958060
## M248_s+mockiaa_tip-la_s+iaa_ro 0.7869449
## M248_s+noiaa_ro-la_s+iaa_ro 0.9444388
## M248_s+noiaa_tip-la_s+iaa_ro 0.9902158
## la_s+mockiaa_ro-la_s+iaa_tip 0.9999985
## la_s+mockiaa_tip-la_s+iaa_tip 1.0000000
## la_s+noiaa_ro-la_s+iaa_tip 0.9999941
## la_s+noiaa_tip-la_s+iaa_tip 1.0000000
## M248_s+iaa_ro-la_s+iaa_tip 0.9963338
## M248_s+iaa_tip-la_s+iaa_tip 0.2231311
## M248_s+mockiaa_ro-la_s+iaa_tip 0.9994718
## M248_s+mockiaa_tip-la_s+iaa_tip 0.9993634
## M248_s+noiaa_ro-la_s+iaa_tip 0.9999990
## M248_s+noiaa_tip-la_s+iaa_tip 1.0000000
## la_s+mockiaa_tip-la_s+mockiaa_ro 0.9999960
## la_s+noiaa_ro-la_s+mockiaa_ro 0.9986233
## la_s+noiaa_tip-la_s+mockiaa_ro 0.9999802
## M248_s+iaa_ro-la_s+mockiaa_ro 0.9999951
## M248_s+iaa_tip-la_s+mockiaa_ro 0.6481559
## M248_s+mockiaa_ro-la_s+mockiaa_ro 1.0000000
## M248_s+mockiaa_tip-la_s+mockiaa_ro 1.0000000
## M248_s+noiaa_ro-la_s+mockiaa_ro 1.0000000
## M248_s+noiaa_tip-la_s+mockiaa_ro 0.9999999
## la_s+noiaa_ro-la_s+mockiaa_tip 0.9999991
## la_s+noiaa_tip-la_s+mockiaa_tip 1.0000000
## M248_s+iaa_ro-la_s+mockiaa_tip 0.9955664
## M248_s+iaa_tip-la_s+mockiaa_tip 0.2467719
## M248_s+mockiaa_ro-la_s+mockiaa_tip 0.9992609
## M248_s+mockiaa_tip-la_s+mockiaa_tip 0.9991236
## M248_s+noiaa_ro-la_s+mockiaa_tip 0.9999972
## M248_s+noiaa_tip-la_s+mockiaa_tip 1.0000000
## la_s+noiaa_tip-la_s+noiaa_ro 1.0000000
## M248_s+iaa_ro-la_s+noiaa_ro 0.9449661
## M248_s+iaa_tip-la_s+noiaa_ro 0.0988190
## M248_s+mockiaa_ro-la_s+noiaa_ro 0.9764780
## M248_s+mockiaa_tip-la_s+noiaa_ro 0.9742522
## M248_s+noiaa_ro-la_s+noiaa_ro 0.9985233
## M248_s+noiaa_tip-la_s+noiaa_ro 0.9999695
## M248_s+iaa_ro-la_s+noiaa_tip 0.9930935
## M248_s+iaa_tip-la_s+noiaa_tip 0.2653695
## M248_s+mockiaa_ro-la_s+noiaa_tip 0.9985182
## M248_s+mockiaa_tip-la_s+noiaa_tip 0.9982873
## M248_s+noiaa_ro-la_s+noiaa_tip 0.9999844
## M248_s+noiaa_tip-la_s+noiaa_tip 1.0000000
## M248_s+iaa_tip-M248_s+iaa_ro 0.9258632
## M248_s+mockiaa_ro-M248_s+iaa_ro 1.0000000
## M248_s+mockiaa_tip-M248_s+iaa_ro 1.0000000
## M248_s+noiaa_ro-M248_s+iaa_ro 0.9999808
## M248_s+noiaa_tip-M248_s+iaa_ro 0.9990633
## M248_s+mockiaa_ro-M248_s+iaa_tip 0.7699003
## M248_s+mockiaa_tip-M248_s+iaa_tip 0.7790697
## M248_s+noiaa_ro-M248_s+iaa_tip 0.5315731
## M248_s+noiaa_tip-M248_s+iaa_tip 0.3388471
## M248_s+mockiaa_tip-M248_s+mockiaa_ro 1.0000000
## M248_s+noiaa_ro-M248_s+mockiaa_ro 0.9999999
## M248_s+noiaa_tip-M248_s+mockiaa_ro 0.9999222
## M248_s+noiaa_ro-M248_s+mockiaa_tip 0.9999998
## M248_s+noiaa_tip-M248_s+mockiaa_tip 0.9999021
## M248_s+noiaa_tip-M248_s+noiaa_ro 1.0000000P11 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P11)
stat.test## la_s+iaa_tip la_s+mockiaa_ro la_s+mockiaa_tip la_s+noiaa_ro
## "ab" "ab" "ab" "ab"
## la_s+noiaa_tip M248_s+iaa_ro M248_s+iaa_tip M248_s+mockiaa_ro
## "ab" "ab" "a" "ab"
## M248_s+mockiaa_tip M248_s+noiaa_ro M248_s+noiaa_tip la_s+iaa_ro
## "ab" "ab" "ab" "b"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+iaa"test$info[[1]][3]## [1] "tip"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ICP_IAA_4wICP_IAA_4w$All.condition <- factor(ICP_IAA_4w$All.condition, levels = c("s+noiaa", "s+mockiaa", "s+iaa"))
ICP_IAA_4w <- subset(ICP_IAA_4w, ICP_IAA_4w$Na.K.ratio < 0.74)
ratio_content_IAA_4w <- ggplot(data = ICP_IAA_4w, mapping = aes(x = All.condition, y = Na.K.ratio, colour = All.condition))
ratio_content_IAA_4w <- ratio_content_IAA_4w + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
ratio_content_IAA_4w <- ratio_content_IAA_4w + facet_grid(Tissue ~ Accession, scales = "free_y")
ratio_content_IAA_4w <-ratio_content_IAA_4w + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
ratio_content_IAA_4w<- ratio_content_IAA_4w + scale_color_manual(values = c("red","blueviolet","cyan"))
ratio_content_IAA_4w <- ratio_content_IAA_4w + ylab("Na/K ratio") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 1)
ratio_content_IAA_4w <- ratio_content_IAA_4w + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
ratio_content_IAA_4w <-ratio_content_IAA_4w + rremove("legend")
ratio_content_IAA_4w
ICP_IAA_4wnoiaa_sub <- subset(ICP_IAA_4w, ICP_IAA_4w$Condition2 %in% c("iaa", "noiaa"))
noiaa_subOutput <- TukeyHSD(aov(Na.K.ratio ~ All.ID2, data = noiaa_sub))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Na.K.ratio ~ All.ID2, data = noiaa_sub)
##
## $All.ID2
## diff lwr upr p adj
## la_s+iaa_tip-la_s+iaa_ro 0.083229360 -0.154866330 0.32132505 0.9437473
## la_s+noiaa_ro-la_s+iaa_ro 0.043040360 -0.205642478 0.29172320 0.9991003
## la_s+noiaa_tip-la_s+iaa_ro 0.068048596 -0.195719385 0.33181658 0.9893480
## M248_s+iaa_ro-la_s+iaa_ro 0.166968059 -0.096799922 0.43073604 0.4640657
## M248_s+iaa_tip-la_s+iaa_ro 0.296750164 0.048067327 0.54543300 0.0107367
## M248_s+noiaa_ro-la_s+iaa_ro 0.117008851 -0.131673986 0.36569169 0.7870987
## M248_s+noiaa_tip-la_s+iaa_ro 0.092356069 -0.156326769 0.34103891 0.9241500
## la_s+noiaa_ro-la_s+iaa_tip -0.040189000 -0.278284690 0.19790669 0.9992349
## la_s+noiaa_tip-la_s+iaa_tip -0.015180763 -0.268991622 0.23863009 0.9999993
## M248_s+iaa_ro-la_s+iaa_tip 0.083738699 -0.170072159 0.33754956 0.9581935
## M248_s+iaa_tip-la_s+iaa_tip 0.213520805 -0.024574885 0.45161649 0.1047269
## M248_s+noiaa_ro-la_s+iaa_tip 0.033779492 -0.204316198 0.27187518 0.9997553
## M248_s+noiaa_tip-la_s+iaa_tip 0.009126709 -0.228968981 0.24722240 1.0000000
## la_s+noiaa_tip-la_s+noiaa_ro 0.025008236 -0.238759745 0.28877622 0.9999839
## M248_s+iaa_ro-la_s+noiaa_ro 0.123927699 -0.139840282 0.38769568 0.7882794
## M248_s+iaa_tip-la_s+noiaa_ro 0.253709804 0.005026967 0.50239264 0.0429292
## M248_s+noiaa_ro-la_s+noiaa_ro 0.073968491 -0.174714346 0.32265133 0.9760524
## M248_s+noiaa_tip-la_s+noiaa_ro 0.049315709 -0.199367129 0.29799855 0.9978556
## M248_s+iaa_ro-la_s+noiaa_tip 0.098919463 -0.179116402 0.37695533 0.9385525
## M248_s+iaa_tip-la_s+noiaa_tip 0.228701568 -0.035066413 0.49246955 0.1278173
## M248_s+noiaa_ro-la_s+noiaa_tip 0.048960255 -0.214807726 0.31272824 0.9985899
## M248_s+noiaa_tip-la_s+noiaa_tip 0.024307472 -0.239460509 0.28807545 0.9999867
## M248_s+iaa_tip-M248_s+iaa_ro 0.129782105 -0.133985876 0.39355009 0.7482780
## M248_s+noiaa_ro-M248_s+iaa_ro -0.049959207 -0.313727189 0.21380877 0.9983964
## M248_s+noiaa_tip-M248_s+iaa_ro -0.074611990 -0.338379971 0.18915599 0.9819088
## M248_s+noiaa_ro-M248_s+iaa_tip -0.179741313 -0.428424151 0.06894152 0.3021161
## M248_s+noiaa_tip-M248_s+iaa_tip -0.204394096 -0.453076933 0.04428874 0.1706819
## M248_s+noiaa_tip-M248_s+noiaa_ro -0.024652783 -0.273335620 0.22403005 0.9999781P11 = Output$All.ID2[,'p adj']
stat.test<- multcompLetters(P11)
stat.test## la_s+iaa_tip la_s+noiaa_ro la_s+noiaa_tip M248_s+iaa_ro
## "ab" "a" "ab" "ab"
## M248_s+iaa_tip M248_s+noiaa_ro M248_s+noiaa_tip la_s+iaa_ro
## "b" "ab" "ab" "a"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testtest$info <- strsplit(test$group1, "_")
test$info[[1]][2]## [1] "s+iaa"test$info[[1]][3]## [1] "tip"test$Accession <- "none"
test$All.condition<- "none"
test$Tissue<- "none"
testfor(i in 1:nrow(test)){
test$Accession[i] <- test$info[[i]][1]
test$All.condition[i] <- test$info[[i]][2]
test$Tissue[i] <- test$info[[i]][3]
}
test2 <- test[,c(5:7,1)]
test2$group1 <- test2$All.condition
test2$group2 <- test2$All.condition
ratio_noiaa_IAA_4w <- ggplot(data = noiaa_sub, mapping = aes(x = All.condition, y = Na.K.ratio, colour = All.condition))
ratio_noiaa_IAA_4w <- ratio_noiaa_IAA_4w + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
ratio_noiaa_IAA_4w <- ratio_noiaa_IAA_4w + facet_grid(Tissue ~ Accession, scales = "free_y")
ratio_noiaa_IAA_4w <- ratio_noiaa_IAA_4w + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
ratio_noiaa_IAA_4w <- ratio_noiaa_IAA_4w + scale_color_manual(values = c("red","blueviolet","cyan"))
ratio_noiaa_IAA_4w <- ratio_noiaa_IAA_4w + ylab("Na/K ratio") + xlab("")+stat_pvalue_manual(test2, label = "Tukey", y.position = 1.3)
ratio_noiaa_IAA_4w <- ratio_noiaa_IAA_4w + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
ratio_noiaa_IAA_4w <- ratio_noiaa_IAA_4w + rremove("legend")
ratio_noiaa_IAA_4w
#That is all, now graph all 3 parameters for two developmental stages for both hormones
library(cowplot)##
## Attaching package: 'cowplot'## The following object is masked from 'package:ggpubr':
##
## get_legendpdf("ICP-all-ACC-soil-spray-10d.pdf", height = 5, width = 12)
plot_grid(Na_content_ACC_10d, k_content_ACC_10d, ratio_content_ACC_10d, ratio_noacc_ACC_10d, ncol=3,
align = "hv", labels=c("AUTO"),
label_size = 24)
dev.off()## png
## 2pdf("ICP-all-ACC-soil-spray-4weeks.pdf", height = 5, width = 12)
plot_grid(Na_content_ACC_4w, k_content_ACC_4w, ratio_content_ACC_4w, ratio_noacc_ACC_4w, ncol=3,
align = "hv", labels=c("AUTO"),
label_size = 24)
dev.off()## png
## 2pdf("ICP-all-IAA-soil-spray-10d.pdf", height = 5, width = 12)
plot_grid(Na_content_IAA_10d, K_content_IAA_10d, ratio_content_IAA_10d, ratio_noiaa_IAA_10d, ncol=3,
align = "hv", labels=c("AUTO"),
label_size = 24)
dev.off()## png
## 2pdf("ICP-all-IAA-soil-spray-4weeks.pdf", height = 5, width = 12)
plot_grid(Na_content_IAA_4w , k_content_IAA_4w, ratio_content_IAA_4w, ratio_noiaa_IAA_4w, ncol=3,
align = "hv", labels=c("AUTO"),
label_size = 24)
dev.off()## png
## 2