20220519_IAA_pareto-front_components_1st_experiment
This is a pareto-front components calculation for 3 tomatoes experienced IAA and salt treatments on plate. Alpha and distance components data calculated by Arjun from 20220406_RSA_M248M058LA1511_IAA_100mM_Salt experiment done on December 2021.Because Low and High IAA had similar effect on root system architecture, I only traced and included high IAA here for this dataset. Also for d5, there is no LR so this date was omitted from pareto calculation.
getwd()## [1] "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20220519_IAA_pareto-front_components_1st_experiment"setwd("C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20220519_IAA_pareto-front_components_1st_experiment")list.files(pattern = (".csv"))## [1] "Iaa pareto-front -modified for R.csv"pareto_location<- read.csv("Iaa pareto-front -modified for R.csv")
pareto_locationcolnames(pareto_location)[1] <- "Accessions"
pareto_location$Accessions <- gsub("58", "M058", pareto_location$Accessions)
pareto_location$Accessions <- gsub("248", "M248", pareto_location$Accessions)
pareto_location$Accessions <- gsub("1511", "LA1511", pareto_location$Accessions)
pareto_location$Condition.all <- paste(pareto_location$Condition, pareto_location$Condition2, sep="_")
pareto_locationlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.5library(ggpubr)## Warning: package 'ggpubr' was built under R version 4.0.5library(multcompView)## Warning: package 'multcompView' was built under R version 4.0.5pareto_location$All.ID <- paste(pareto_location$Accessions, pareto_location$Condition.all, pareto_location$Day, sep="_")
pareto_locationpareto_location$Day <- as.numeric(as.character(pareto_location$Day))
pareto_loc2 <- subset(pareto_location, pareto_location$Day > 5)aov(pareto.front.scaling.location ~ All.ID, data = pareto_loc2)## Call:
## aov(formula = pareto.front.scaling.location ~ All.ID, data = pareto_loc2)
##
## Terms:
## All.ID Residuals
## Sum of Squares 1.106899 1.629386
## Deg. of Freedom 11 100
##
## Residual standard error: 0.1276474
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(pareto.front.scaling.location ~ All.ID, data = pareto_loc2))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = pareto.front.scaling.location ~ All.ID, data = pareto_loc2)
##
## $All.ID
## diff lwr upr
## LA1511_C_noiaa_9-LA1511_C_iaa_9 -0.0630000000 -0.296993474 0.170993474
## LA1511_S_iaa_9-LA1511_C_iaa_9 0.0058888889 -0.190401332 0.202179110
## LA1511_S_noiaa_9-LA1511_C_iaa_9 -0.0792500000 -0.281894293 0.123394293
## M058_C_iaa_9-LA1511_C_iaa_9 0.0450000000 -0.146054872 0.236054872
## M058_C_noiaa_9-LA1511_C_iaa_9 0.1730000000 -0.018054872 0.364054872
## M058_S_iaa_9-LA1511_C_iaa_9 0.0170000000 -0.174054872 0.208054872
## M058_S_noiaa_9-LA1511_C_iaa_9 0.2430000000 0.051945128 0.434054872
## M248_C_iaa_9-LA1511_C_iaa_9 0.1360000000 -0.055054872 0.327054872
## M248_C_noiaa_9-LA1511_C_iaa_9 0.1900000000 -0.001054872 0.381054872
## M248_S_iaa_9-LA1511_C_iaa_9 0.0050000000 -0.186054872 0.196054872
## M248_S_noiaa_9-LA1511_C_iaa_9 0.1670000000 -0.024054872 0.358054872
## LA1511_S_iaa_9-LA1511_C_noiaa_9 0.0688888889 -0.169398402 0.307176180
## LA1511_S_noiaa_9-LA1511_C_noiaa_9 -0.0162500000 -0.259798130 0.227298130
## M058_C_iaa_9-LA1511_C_noiaa_9 0.1080000000 -0.125993474 0.341993474
## M058_C_noiaa_9-LA1511_C_noiaa_9 0.2360000000 0.002006526 0.469993474
## M058_S_iaa_9-LA1511_C_noiaa_9 0.0800000000 -0.153993474 0.313993474
## M058_S_noiaa_9-LA1511_C_noiaa_9 0.3060000000 0.072006526 0.539993474
## M248_C_iaa_9-LA1511_C_noiaa_9 0.1990000000 -0.034993474 0.432993474
## M248_C_noiaa_9-LA1511_C_noiaa_9 0.2530000000 0.019006526 0.486993474
## M248_S_iaa_9-LA1511_C_noiaa_9 0.0680000000 -0.165993474 0.301993474
## M248_S_noiaa_9-LA1511_C_noiaa_9 0.2300000000 -0.003993474 0.463993474
## LA1511_S_noiaa_9-LA1511_S_iaa_9 -0.0851388889 -0.292726452 0.122448674
## M058_C_iaa_9-LA1511_S_iaa_9 0.0391111111 -0.157179110 0.235401332
## M058_C_noiaa_9-LA1511_S_iaa_9 0.1671111111 -0.029179110 0.363401332
## M058_S_iaa_9-LA1511_S_iaa_9 0.0111111111 -0.185179110 0.207401332
## M058_S_noiaa_9-LA1511_S_iaa_9 0.2371111111 0.040820890 0.433401332
## M248_C_iaa_9-LA1511_S_iaa_9 0.1301111111 -0.066179110 0.326401332
## M248_C_noiaa_9-LA1511_S_iaa_9 0.1841111111 -0.012179110 0.380401332
## M248_S_iaa_9-LA1511_S_iaa_9 -0.0008888889 -0.197179110 0.195401332
## M248_S_noiaa_9-LA1511_S_iaa_9 0.1611111111 -0.035179110 0.357401332
## M058_C_iaa_9-LA1511_S_noiaa_9 0.1242500000 -0.078394293 0.326894293
## M058_C_noiaa_9-LA1511_S_noiaa_9 0.2522500000 0.049605707 0.454894293
## M058_S_iaa_9-LA1511_S_noiaa_9 0.0962500000 -0.106394293 0.298894293
## M058_S_noiaa_9-LA1511_S_noiaa_9 0.3222500000 0.119605707 0.524894293
## M248_C_iaa_9-LA1511_S_noiaa_9 0.2152500000 0.012605707 0.417894293
## M248_C_noiaa_9-LA1511_S_noiaa_9 0.2692500000 0.066605707 0.471894293
## M248_S_iaa_9-LA1511_S_noiaa_9 0.0842500000 -0.118394293 0.286894293
## M248_S_noiaa_9-LA1511_S_noiaa_9 0.2462500000 0.043605707 0.448894293
## M058_C_noiaa_9-M058_C_iaa_9 0.1280000000 -0.063054872 0.319054872
## M058_S_iaa_9-M058_C_iaa_9 -0.0280000000 -0.219054872 0.163054872
## M058_S_noiaa_9-M058_C_iaa_9 0.1980000000 0.006945128 0.389054872
## M248_C_iaa_9-M058_C_iaa_9 0.0910000000 -0.100054872 0.282054872
## M248_C_noiaa_9-M058_C_iaa_9 0.1450000000 -0.046054872 0.336054872
## M248_S_iaa_9-M058_C_iaa_9 -0.0400000000 -0.231054872 0.151054872
## M248_S_noiaa_9-M058_C_iaa_9 0.1220000000 -0.069054872 0.313054872
## M058_S_iaa_9-M058_C_noiaa_9 -0.1560000000 -0.347054872 0.035054872
## M058_S_noiaa_9-M058_C_noiaa_9 0.0700000000 -0.121054872 0.261054872
## M248_C_iaa_9-M058_C_noiaa_9 -0.0370000000 -0.228054872 0.154054872
## M248_C_noiaa_9-M058_C_noiaa_9 0.0170000000 -0.174054872 0.208054872
## M248_S_iaa_9-M058_C_noiaa_9 -0.1680000000 -0.359054872 0.023054872
## M248_S_noiaa_9-M058_C_noiaa_9 -0.0060000000 -0.197054872 0.185054872
## M058_S_noiaa_9-M058_S_iaa_9 0.2260000000 0.034945128 0.417054872
## M248_C_iaa_9-M058_S_iaa_9 0.1190000000 -0.072054872 0.310054872
## M248_C_noiaa_9-M058_S_iaa_9 0.1730000000 -0.018054872 0.364054872
## M248_S_iaa_9-M058_S_iaa_9 -0.0120000000 -0.203054872 0.179054872
## M248_S_noiaa_9-M058_S_iaa_9 0.1500000000 -0.041054872 0.341054872
## M248_C_iaa_9-M058_S_noiaa_9 -0.1070000000 -0.298054872 0.084054872
## M248_C_noiaa_9-M058_S_noiaa_9 -0.0530000000 -0.244054872 0.138054872
## M248_S_iaa_9-M058_S_noiaa_9 -0.2380000000 -0.429054872 -0.046945128
## M248_S_noiaa_9-M058_S_noiaa_9 -0.0760000000 -0.267054872 0.115054872
## M248_C_noiaa_9-M248_C_iaa_9 0.0540000000 -0.137054872 0.245054872
## M248_S_iaa_9-M248_C_iaa_9 -0.1310000000 -0.322054872 0.060054872
## M248_S_noiaa_9-M248_C_iaa_9 0.0310000000 -0.160054872 0.222054872
## M248_S_iaa_9-M248_C_noiaa_9 -0.1850000000 -0.376054872 0.006054872
## M248_S_noiaa_9-M248_C_noiaa_9 -0.0230000000 -0.214054872 0.168054872
## M248_S_noiaa_9-M248_S_iaa_9 0.1620000000 -0.029054872 0.353054872
## p adj
## LA1511_C_noiaa_9-LA1511_C_iaa_9 0.9989802
## LA1511_S_iaa_9-LA1511_C_iaa_9 1.0000000
## LA1511_S_noiaa_9-LA1511_C_iaa_9 0.9761303
## M058_C_iaa_9-LA1511_C_iaa_9 0.9997105
## M058_C_noiaa_9-LA1511_C_iaa_9 0.1151877
## M058_S_iaa_9-LA1511_C_iaa_9 1.0000000
## M058_S_noiaa_9-LA1511_C_iaa_9 0.0026252
## M248_C_iaa_9-LA1511_C_iaa_9 0.4275528
## M248_C_noiaa_9-LA1511_C_iaa_9 0.0526596
## M248_S_iaa_9-LA1511_C_iaa_9 1.0000000
## M248_S_noiaa_9-LA1511_C_iaa_9 0.1480756
## LA1511_S_iaa_9-LA1511_C_noiaa_9 0.9980525
## LA1511_S_noiaa_9-LA1511_C_noiaa_9 1.0000000
## M058_C_iaa_9-LA1511_C_noiaa_9 0.9239744
## M058_C_noiaa_9-LA1511_C_noiaa_9 0.0461002
## M058_S_iaa_9-LA1511_C_noiaa_9 0.9917371
## M058_S_noiaa_9-LA1511_C_noiaa_9 0.0016939
## M248_C_iaa_9-LA1511_C_noiaa_9 0.1772508
## M248_C_noiaa_9-LA1511_C_noiaa_9 0.0224046
## M248_S_iaa_9-LA1511_C_noiaa_9 0.9979598
## M248_S_noiaa_9-LA1511_C_noiaa_9 0.0586175
## LA1511_S_noiaa_9-LA1511_S_iaa_9 0.9660980
## M058_C_iaa_9-LA1511_S_iaa_9 0.9999439
## M058_C_noiaa_9-LA1511_S_iaa_9 0.1760730
## M058_S_iaa_9-LA1511_S_iaa_9 1.0000000
## M058_S_noiaa_9-LA1511_S_iaa_9 0.0055884
## M248_C_iaa_9-LA1511_S_iaa_9 0.5406140
## M248_C_noiaa_9-LA1511_S_iaa_9 0.0876084
## M248_S_iaa_9-LA1511_S_iaa_9 1.0000000
## M248_S_noiaa_9-LA1511_S_iaa_9 0.2195493
## M058_C_iaa_9-LA1511_S_noiaa_9 0.6570900
## M058_C_noiaa_9-LA1511_S_noiaa_9 0.0036312
## M058_S_iaa_9-LA1511_S_noiaa_9 0.9088687
## M058_S_noiaa_9-LA1511_S_noiaa_9 0.0000394
## M248_C_iaa_9-LA1511_S_noiaa_9 0.0272157
## M248_C_noiaa_9-LA1511_S_noiaa_9 0.0013051
## M248_S_iaa_9-LA1511_S_noiaa_9 0.9626164
## M248_S_noiaa_9-LA1511_S_noiaa_9 0.0051401
## M058_C_noiaa_9-M058_C_iaa_9 0.5238627
## M058_S_iaa_9-M058_C_iaa_9 0.9999976
## M058_S_noiaa_9-M058_C_iaa_9 0.0352304
## M248_C_iaa_9-M058_C_iaa_9 0.9072698
## M248_C_noiaa_9-M058_C_iaa_9 0.3282233
## M248_S_iaa_9-M058_C_iaa_9 0.9999082
## M248_S_noiaa_9-M058_C_iaa_9 0.5979333
## M058_S_iaa_9-M058_C_noiaa_9 0.2261535
## M058_S_noiaa_9-M058_C_noiaa_9 0.9855593
## M248_C_iaa_9-M058_C_noiaa_9 0.9999578
## M248_C_noiaa_9-M058_C_noiaa_9 1.0000000
## M248_S_iaa_9-M058_C_noiaa_9 0.1421415
## M248_S_noiaa_9-M058_C_noiaa_9 1.0000000
## M058_S_noiaa_9-M058_S_iaa_9 0.0074427
## M248_C_iaa_9-M058_S_iaa_9 0.6346673
## M248_C_noiaa_9-M058_S_iaa_9 0.1151877
## M248_S_iaa_9-M058_S_iaa_9 1.0000000
## M248_S_noiaa_9-M058_S_iaa_9 0.2789244
## M248_C_iaa_9-M058_S_noiaa_9 0.7718399
## M248_C_noiaa_9-M058_S_noiaa_9 0.9986589
## M248_S_iaa_9-M058_S_noiaa_9 0.0035916
## M248_S_noiaa_9-M058_S_noiaa_9 0.9728957
## M248_C_noiaa_9-M248_C_iaa_9 0.9984110
## M248_S_iaa_9-M248_C_iaa_9 0.4871724
## M248_S_noiaa_9-M248_C_iaa_9 0.9999930
## M248_S_iaa_9-M248_C_noiaa_9 0.0669856
## M248_S_noiaa_9-M248_C_noiaa_9 0.9999997
## M248_S_noiaa_9-M248_S_iaa_9 0.1806039P6 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P6)
stat.test## LA1511_C_noiaa_9 LA1511_S_iaa_9 LA1511_S_noiaa_9 M058_C_iaa_9
## "ab" "abc" "a" "abc"
## M058_C_noiaa_9 M058_S_iaa_9 M058_S_noiaa_9 M248_C_iaa_9
## "cd" "abc" "d" "bcd"
## M248_C_noiaa_9 M248_S_iaa_9 M248_S_noiaa_9 LA1511_C_iaa_9
## "cd" "abc" "bcd" "abc"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testpareto_loc2$Condition.all <- factor(pareto_loc2$Condition.all, levels = c("C_noiaa", "C_iaa", "S_noiaa", "S_iaa"))
pareto_location_graph <- ggplot(data = pareto_loc2, mapping = aes(x = All.ID, y = pareto.front.scaling.location
, colour = Condition.all))
pareto_location_graph <- pareto_location_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
pareto_location_graph <- pareto_location_graph + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
pareto_location_graph <- pareto_location_graph + scale_color_manual(values = c("blue","blueviolet", "red", "deeppink"))
pareto_location_graph <- pareto_location_graph + ylab("Pareto front location") + xlab("") #+ ggtitle("")
pareto_location_graph <- pareto_location_graph + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
pareto_location_graph <- pareto_location_graph + stat_pvalue_manual(test, label = "Tukey", y.position = 1.5)
pareto_location_graph
pareto_location_graph2 <- ggplot(data=pareto_loc2, aes(x= Condition.all, y=pareto.front.scaling.location, color = Condition.all))
pareto_location_graph2 <- pareto_location_graph2 + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
pareto_location_graph2 <- pareto_location_graph2 + facet_grid(~ Accessions )
pareto_location_graph2 <- pareto_location_graph2 + ylab("Pareto front scaling location") + xlab("") + theme(legend.position='none')
pareto_location_graph2
aov(pareto.front.scaling.distance ~ All.ID, data = pareto_loc2)## Call:
## aov(formula = pareto.front.scaling.distance ~ All.ID, data = pareto_loc2)
##
## Terms:
## All.ID Residuals
## Sum of Squares 0.1201402 0.1997090
## Deg. of Freedom 11 100
##
## Residual standard error: 0.04468881
## Estimated effects may be unbalancedOutput <- TukeyHSD(aov(pareto.front.scaling.distance ~ All.ID, data = pareto_loc2))
Output## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = pareto.front.scaling.distance ~ All.ID, data = pareto_loc2)
##
## $All.ID
## diff lwr upr
## LA1511_C_noiaa_9-LA1511_C_iaa_9 -0.042031900 -0.1239520043 0.039888204
## LA1511_S_iaa_9-LA1511_C_iaa_9 -0.038459433 -0.1071797916 0.030260925
## LA1511_S_noiaa_9-LA1511_C_iaa_9 -0.023318225 -0.0942631164 0.047626666
## M058_C_iaa_9-LA1511_C_iaa_9 0.040551200 -0.0263362851 0.107438685
## M058_C_noiaa_9-LA1511_C_iaa_9 -0.046517700 -0.1134051851 0.020369785
## M058_S_iaa_9-LA1511_C_iaa_9 -0.019514100 -0.0864015851 0.047373385
## M058_S_noiaa_9-LA1511_C_iaa_9 -0.075962000 -0.1428494851 -0.009074515
## M248_C_iaa_9-LA1511_C_iaa_9 0.032429900 -0.0344575851 0.099317385
## M248_C_noiaa_9-LA1511_C_iaa_9 -0.008330600 -0.0752180851 0.058556885
## M248_S_iaa_9-LA1511_C_iaa_9 0.008791100 -0.0580963851 0.075678585
## M248_S_noiaa_9-LA1511_C_iaa_9 -0.029079200 -0.0959666851 0.037808285
## LA1511_S_iaa_9-LA1511_C_noiaa_9 0.003572467 -0.0798508842 0.086995818
## LA1511_S_noiaa_9-LA1511_C_noiaa_9 0.018713675 -0.0665514729 0.103978823
## M058_C_iaa_9-LA1511_C_noiaa_9 0.082583100 0.0006629957 0.164503204
## M058_C_noiaa_9-LA1511_C_noiaa_9 -0.004485800 -0.0864059043 0.077434304
## M058_S_iaa_9-LA1511_C_noiaa_9 0.022517800 -0.0594023043 0.104437904
## M058_S_noiaa_9-LA1511_C_noiaa_9 -0.033930100 -0.1158502043 0.047990004
## M248_C_iaa_9-LA1511_C_noiaa_9 0.074461800 -0.0074583043 0.156381904
## M248_C_noiaa_9-LA1511_C_noiaa_9 0.033701300 -0.0482188043 0.115621404
## M248_S_iaa_9-LA1511_C_noiaa_9 0.050823000 -0.0310971043 0.132743104
## M248_S_noiaa_9-LA1511_C_noiaa_9 0.012952700 -0.0689674043 0.094872804
## LA1511_S_noiaa_9-LA1511_S_iaa_9 0.015141208 -0.0575343006 0.087816717
## M058_C_iaa_9-LA1511_S_iaa_9 0.079010633 0.0102902751 0.147730992
## M058_C_noiaa_9-LA1511_S_iaa_9 -0.008058267 -0.0767786249 0.060662092
## M058_S_iaa_9-LA1511_S_iaa_9 0.018945333 -0.0497750249 0.087665692
## M058_S_noiaa_9-LA1511_S_iaa_9 -0.037502567 -0.1062229249 0.031217792
## M248_C_iaa_9-LA1511_S_iaa_9 0.070889333 0.0021689751 0.139609692
## M248_C_noiaa_9-LA1511_S_iaa_9 0.030128833 -0.0385915249 0.098849192
## M248_S_iaa_9-LA1511_S_iaa_9 0.047250533 -0.0214698249 0.115970892
## M248_S_noiaa_9-LA1511_S_iaa_9 0.009380233 -0.0593401249 0.078100592
## M058_C_iaa_9-LA1511_S_noiaa_9 0.063869425 -0.0070754664 0.134814316
## M058_C_noiaa_9-LA1511_S_noiaa_9 -0.023199475 -0.0941443664 0.047745416
## M058_S_iaa_9-LA1511_S_noiaa_9 0.003804125 -0.0671407664 0.074749016
## M058_S_noiaa_9-LA1511_S_noiaa_9 -0.052643775 -0.1235886664 0.018301116
## M248_C_iaa_9-LA1511_S_noiaa_9 0.055748125 -0.0151967664 0.126693016
## M248_C_noiaa_9-LA1511_S_noiaa_9 0.014987625 -0.0559572664 0.085932516
## M248_S_iaa_9-LA1511_S_noiaa_9 0.032109325 -0.0388355664 0.103054216
## M248_S_noiaa_9-LA1511_S_noiaa_9 -0.005760975 -0.0767058664 0.065183916
## M058_C_noiaa_9-M058_C_iaa_9 -0.087068900 -0.1539563851 -0.020181415
## M058_S_iaa_9-M058_C_iaa_9 -0.060065300 -0.1269527851 0.006822185
## M058_S_noiaa_9-M058_C_iaa_9 -0.116513200 -0.1834006851 -0.049625715
## M248_C_iaa_9-M058_C_iaa_9 -0.008121300 -0.0750087851 0.058766185
## M248_C_noiaa_9-M058_C_iaa_9 -0.048881800 -0.1157692851 0.018005685
## M248_S_iaa_9-M058_C_iaa_9 -0.031760100 -0.0986475851 0.035127385
## M248_S_noiaa_9-M058_C_iaa_9 -0.069630400 -0.1365178851 -0.002742915
## M058_S_iaa_9-M058_C_noiaa_9 0.027003600 -0.0398838851 0.093891085
## M058_S_noiaa_9-M058_C_noiaa_9 -0.029444300 -0.0963317851 0.037443185
## M248_C_iaa_9-M058_C_noiaa_9 0.078947600 0.0120601149 0.145835085
## M248_C_noiaa_9-M058_C_noiaa_9 0.038187100 -0.0287003851 0.105074585
## M248_S_iaa_9-M058_C_noiaa_9 0.055308800 -0.0115786851 0.122196285
## M248_S_noiaa_9-M058_C_noiaa_9 0.017438500 -0.0494489851 0.084325985
## M058_S_noiaa_9-M058_S_iaa_9 -0.056447900 -0.1233353851 0.010439585
## M248_C_iaa_9-M058_S_iaa_9 0.051944000 -0.0149434851 0.118831485
## M248_C_noiaa_9-M058_S_iaa_9 0.011183500 -0.0557039851 0.078070985
## M248_S_iaa_9-M058_S_iaa_9 0.028305200 -0.0385822851 0.095192685
## M248_S_noiaa_9-M058_S_iaa_9 -0.009565100 -0.0764525851 0.057322385
## M248_C_iaa_9-M058_S_noiaa_9 0.108391900 0.0415044149 0.175279385
## M248_C_noiaa_9-M058_S_noiaa_9 0.067631400 0.0007439149 0.134518885
## M248_S_iaa_9-M058_S_noiaa_9 0.084753100 0.0178656149 0.151640585
## M248_S_noiaa_9-M058_S_noiaa_9 0.046882800 -0.0200046851 0.113770285
## M248_C_noiaa_9-M248_C_iaa_9 -0.040760500 -0.1076479851 0.026126985
## M248_S_iaa_9-M248_C_iaa_9 -0.023638800 -0.0905262851 0.043248685
## M248_S_noiaa_9-M248_C_iaa_9 -0.061509100 -0.1283965851 0.005378385
## M248_S_iaa_9-M248_C_noiaa_9 0.017121700 -0.0497657851 0.084009185
## M248_S_noiaa_9-M248_C_noiaa_9 -0.020748600 -0.0876360851 0.046138885
## M248_S_noiaa_9-M248_S_iaa_9 -0.037870300 -0.1047577851 0.029017185
## p adj
## LA1511_C_noiaa_9-LA1511_C_iaa_9 0.8560572
## LA1511_S_iaa_9-LA1511_C_iaa_9 0.7726310
## LA1511_S_noiaa_9-LA1511_C_iaa_9 0.9940504
## M058_C_iaa_9-LA1511_C_iaa_9 0.6727844
## M058_C_noiaa_9-LA1511_C_iaa_9 0.4645862
## M058_S_iaa_9-LA1511_C_iaa_9 0.9978873
## M058_S_noiaa_9-LA1511_C_iaa_9 0.0125758
## M248_C_iaa_9-LA1511_C_iaa_9 0.8965981
## M248_C_noiaa_9-LA1511_C_iaa_9 0.9999996
## M248_S_iaa_9-LA1511_C_iaa_9 0.9999992
## M248_S_noiaa_9-LA1511_C_iaa_9 0.9488999
## LA1511_S_iaa_9-LA1511_C_noiaa_9 1.0000000
## LA1511_S_noiaa_9-LA1511_C_noiaa_9 0.9998540
## M058_C_iaa_9-LA1511_C_noiaa_9 0.0463121
## M058_C_noiaa_9-LA1511_C_noiaa_9 1.0000000
## M058_S_iaa_9-LA1511_C_noiaa_9 0.9987666
## M058_S_noiaa_9-LA1511_C_noiaa_9 0.9636133
## M248_C_iaa_9-LA1511_C_noiaa_9 0.1119530
## M248_C_noiaa_9-LA1511_C_noiaa_9 0.9653405
## M248_S_iaa_9-LA1511_C_noiaa_9 0.6403734
## M248_S_noiaa_9-LA1511_C_noiaa_9 0.9999947
## LA1511_S_noiaa_9-LA1511_S_iaa_9 0.9999126
## M058_C_iaa_9-LA1511_S_iaa_9 0.0107802
## M058_C_noiaa_9-LA1511_S_iaa_9 0.9999998
## M058_S_iaa_9-LA1511_S_iaa_9 0.9987329
## M058_S_noiaa_9-LA1511_S_iaa_9 0.7995680
## M248_C_iaa_9-LA1511_S_iaa_9 0.0369256
## M248_C_noiaa_9-LA1511_S_iaa_9 0.9458778
## M248_S_iaa_9-LA1511_S_iaa_9 0.4827467
## M248_S_noiaa_9-LA1511_S_iaa_9 0.9999989
## M058_C_iaa_9-LA1511_S_noiaa_9 0.1202229
## M058_C_noiaa_9-LA1511_S_noiaa_9 0.9943014
## M058_S_iaa_9-LA1511_S_noiaa_9 1.0000000
## M058_S_noiaa_9-LA1511_S_noiaa_9 0.3624428
## M248_C_iaa_9-LA1511_S_noiaa_9 0.2777064
## M248_C_noiaa_9-LA1511_S_noiaa_9 0.9998997
## M248_S_iaa_9-LA1511_S_noiaa_9 0.9330650
## M248_S_noiaa_9-LA1511_S_noiaa_9 1.0000000
## M058_C_noiaa_9-M058_C_iaa_9 0.0018242
## M058_S_iaa_9-M058_C_iaa_9 0.1224547
## M058_S_noiaa_9-M058_C_iaa_9 0.0000044
## M248_C_iaa_9-M058_C_iaa_9 0.9999997
## M248_C_noiaa_9-M058_C_iaa_9 0.3861053
## M248_S_iaa_9-M058_C_iaa_9 0.9090374
## M248_S_noiaa_9-M058_C_iaa_9 0.0336486
## M058_S_iaa_9-M058_C_noiaa_9 0.9697744
## M058_S_noiaa_9-M058_C_noiaa_9 0.9443713
## M248_C_iaa_9-M058_C_noiaa_9 0.0076650
## M248_C_noiaa_9-M058_C_noiaa_9 0.7497478
## M248_S_iaa_9-M058_C_noiaa_9 0.2103075
## M248_S_noiaa_9-M058_C_noiaa_9 0.9992430
## M058_S_noiaa_9-M058_S_iaa_9 0.1860063
## M248_C_iaa_9-M058_S_iaa_9 0.2944733
## M248_C_noiaa_9-M058_S_iaa_9 0.9999905
## M248_S_iaa_9-M058_S_iaa_9 0.9576226
## M248_S_noiaa_9-M058_S_iaa_9 0.9999981
## M248_C_iaa_9-M058_S_noiaa_9 0.0000256
## M248_C_noiaa_9-M058_S_noiaa_9 0.0449941
## M248_S_iaa_9-M058_S_noiaa_9 0.0027813
## M248_S_noiaa_9-M058_S_noiaa_9 0.4521308
## M248_C_noiaa_9-M248_C_iaa_9 0.6656722
## M248_S_iaa_9-M248_C_iaa_9 0.9891711
## M248_S_noiaa_9-M248_C_iaa_9 0.1024583
## M248_S_iaa_9-M248_C_noiaa_9 0.9993622
## M248_S_noiaa_9-M248_C_noiaa_9 0.9963739
## M248_S_noiaa_9-M248_S_iaa_9 0.7594817P5 = Output$All.ID[,'p adj']
stat.test<- multcompLetters(P5)
stat.test## LA1511_C_noiaa_9 LA1511_S_iaa_9 LA1511_S_noiaa_9 M058_C_iaa_9
## "abc" "ab" "abcd" "d"
## M058_C_noiaa_9 M058_S_iaa_9 M058_S_noiaa_9 M248_C_iaa_9
## "ab" "abcd" "b" "cd"
## M248_C_noiaa_9 M248_S_iaa_9 M248_S_noiaa_9 LA1511_C_iaa_9
## "acd" "acd" "abc" "acd"test <- as.data.frame(stat.test$Letters)
test$group1 <- rownames(test)
test$group2 <- rownames(test)
colnames(test)[1] <- "Tukey"
testpareto_distance_graph2 <- ggplot(data = pareto_loc2, mapping = aes(x = All.ID, y = pareto.front.scaling.distance, colour = Condition.all))
pareto_distance_graph2 <- pareto_distance_graph2 + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
pareto_distance_graph2 <- pareto_distance_graph2 + stat_summary(fun=mean, geom="point", shape=95, size=6, color="black", fill="black")
pareto_distance_graph2 <- pareto_distance_graph2 + scale_color_manual(values = c("blue","blueviolet","red", "deeppink"))
pareto_distance_graph2 <- pareto_distance_graph2 + ylab("Pareto front scaling distance") + xlab("") #+ ggtitle("")
pareto_distance_graph2 <- pareto_distance_graph2 + theme(axis.text.x = element_text(angle=90, hjust=0.9, vjust=0.5))
pareto_distance_graph2 <- pareto_distance_graph2 + stat_pvalue_manual(test, label = "Tukey", y.position = 1.5)
pareto_distance_graph2
pareto_distance_graph <- ggplot(data=pareto_loc2, aes(x= Condition.all, y=pareto.front.scaling.distance, color = Condition.all))
pareto_distance_graph <- pareto_distance_graph + geom_boxplot(alpha=0.2) + geom_jitter(width=0.1,alpha=0.2)
pareto_distance_graph <- pareto_distance_graph + facet_grid(~ Accessions )
pareto_distance_graph <- pareto_distance_graph + ylab("Pareto front scaling distance") + xlab("") + theme(legend.position='none')
pareto_distance_graph
I have not calculated nonscaling components at this point.
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