Sorghum range 28 project phenotype profile

Load libraries

library(ggplot2)
library(tidyr)
library(dplyr)
library(ggpubr)
library(corrplot)
library(cowplot)

Load phenotype data

LI1 <- read.csv("/Users/Leon/OneDrive - Cornell University/Projects/9_Sorghum/5_Phenotype/2_Clean_data/LI6800_clean-20220324-morning.csv",                 header = T, row.names = 1)
dat_LI1 <- LI1[, 7:18]
LI1_ave <- data.frame(matrix(ncol = 12, nrow = 48))
vector1 <- unique(LI1$ID)
colnames(LI1_ave) <- colnames(dat_LI1 )
rownames(LI1_ave) <- vector1
head(dat_LI1,20) 

LIA <- read.csv("/Users/Leon/OneDrive - Cornell University/Projects/9_Sorghum/5_Phenotype/2_Clean_data/LI6800_clean-20220324-afternoon.csv",                 header = T, row.names = 1)
dat_LIA <- LIA[, 7:18]
LIA_ave <- data.frame(matrix(ncol = 12, nrow = 48))
vector1 <- unique(LIA$ID)
colnames(LIA_ave) <- colnames(dat_LIA )
rownames(LIA_ave) <- vector1
head(dat_LIA,20) 

Trait <- read.csv("/Users/Leon/OneDrive - Cornell University/Projects/9_Sorghum/5_Phenotype/2_Clean_data/Oxidative_clean-20220324.csv",                 header = T, row.names = 1)
datTrait <- Trait[, 6:34]
datTrait_ave <- data.frame(matrix(ncol = 29, nrow = 48))
vector <- unique(Trait$ID)
colnames(datTrait_ave) <- colnames(datTrait)
rownames(datTrait_ave) <- vector
head(datTrait,30)

Calculate the mean score for each phenotype entry

### View vectors been created
vector1

##  [1] "A_WW_TP1" "B_WW_TP1" "C_WW_TP1" "D_WW_TP1" "E_WW_TP1" "F_WW_TP1"
##  [7] "A_WL_TP1" "B_WL_TP1" "C_WL_TP1" "D_WL_TP1" "E_WL_TP1" "F_WL_TP1"
## [13] "A_WW_TP3" "B_WW_TP3" "C_WW_TP3" "D_WW_TP3" "E_WW_TP3" "F_WW_TP3"
## [19] "A_WL_TP3" "B_WL_TP3" "C_WL_TP3" "D_WL_TP3" "E_WL_TP3" "F_WL_TP3"
## [25] "A_WW_TP5" "B_WW_TP5" "C_WW_TP5" "D_WW_TP5" "E_WW_TP5" "F_WW_TP5"
## [31] "A_WL_TP5" "B_WL_TP5" "C_WL_TP5" "D_WL_TP5" "E_WL_TP5" "F_WL_TP5"
## [37] "A_WW_TP7" "B_WW_TP7" "C_WW_TP7" "D_WW_TP7" "E_WW_TP7" "F_WW_TP7"
## [43] "A_WL_TP7" "B_WL_TP7" "C_WL_TP7" "D_WL_TP7" "E_WL_TP7" "F_WL_TP7"

vector

##  [1] "A_WW_TP1" "B_WW_TP1" "C_WW_TP1" "D_WW_TP1" "E_WW_TP1" "F_WW_TP1"
##  [7] "A_WL_TP1" "B_WL_TP1" "C_WL_TP1" "D_WL_TP1" "E_WL_TP1" "F_WL_TP1"
## [13] "A_WW_TP3" "B_WW_TP3" "C_WW_TP3" "D_WW_TP3" "E_WW_TP3" "F_WW_TP3"
## [19] "A_WL_TP3" "B_WL_TP3" "C_WL_TP3" "D_WL_TP3" "E_WL_TP3" "F_WL_TP3"
## [25] "A_WW_TP5" "B_WW_TP5" "C_WW_TP5" "D_WW_TP5" "E_WW_TP5" "F_WW_TP5"
## [31] "A_WL_TP5" "B_WL_TP5" "C_WL_TP5" "D_WL_TP5" "E_WL_TP5" "F_WL_TP5"
## [37] "A_WW_TP7" "B_WW_TP7" "C_WW_TP7" "D_WW_TP7" "E_WW_TP7" "F_WW_TP7"
## [43] "A_WL_TP7" "B_WL_TP7" "C_WL_TP7" "D_WL_TP7" "E_WL_TP7" "F_WL_TP7"

### Calculate the average by loop
for (i in 1:length(vector1)){
  LI1_ave[grepl(vector1[i],rownames(LI1_ave)),] <- colMeans(dat_LI1[grepl(vector1[i],rownames(dat_LI1)),])
}

for (i in 1:length(vector1)){
  LIA_ave[grepl(vector1[i],rownames(LIA_ave)),] <- colMeans(dat_LIA[grepl(vector1[i],rownames(dat_LIA)),])
}

for (i in 1:length(vector)){
  datTrait_ave[grepl(vector[i],rownames(datTrait_ave)),] <- colMeans(datTrait[grepl(vector[i],rownames(datTrait)),])
}

Calculate the score for each component under the PCA

LIM_PCA <- data.frame(t(LI1_ave))
results <- prcomp(LIM_PCA, scale = TRUE)
results$rotation <- -1*results$rotation
results$sdev^2 / sum(results$sdev^2)

##  [1] 9.881866e-01 1.017782e-02 1.449840e-03 1.087013e-04 4.083482e-05
##  [6] 3.227476e-05 3.878055e-06 2.299865e-08 4.743275e-09 1.156701e-10
## [11] 1.050832e-11 1.787578e-33

LIM_result <- data.frame(results$rotation)
LIM_result

write.csv(LIM_result, 
"/Users/Leon/OneDrive - Cornell University/Projects/9_Sorghum/5_Phenotype/2_Clean_data/LIM_PCA.csv")

LIA_PCA <- data.frame(t(LIA_ave))
LIA_results <- prcomp(LIA_PCA, scale = TRUE)
LIA_results$rotation <- -1*LIA_results$rotation
LIA_results$sdev^2 / sum(LIA_results$sdev^2)

##  [1] 9.726724e-01 2.275437e-02 3.958922e-03 4.494922e-04 1.260522e-04
##  [6] 2.248722e-05 1.616649e-05 7.779885e-08 1.982320e-08 1.832158e-10
## [11] 1.612612e-11 3.133588e-33

LIA_result <- data.frame(results$rotation)
LIA_result

LIA_plot <- data.frame(LIA_results$rotation)
write.csv(LIA_plot, 
"/Users/Leon/OneDrive - Cornell University/Projects/9_Sorghum/5_Phenotype/2_Clean_data/LIA_PCA.csv")

datTrait_PCA <- data.frame(t(datTrait_ave))
datTrait_results <- prcomp(datTrait_PCA, scale = TRUE)
datTrait_results$rotation <- -1*datTrait_results$rotation
datTrait_results$sdev^2 / sum(datTrait_results$sdev^2)

##  [1] 9.955661e-01 3.024152e-03 6.446452e-04 4.409515e-04 2.275682e-04
##  [6] 4.614399e-05 1.796990e-05 1.462262e-05 5.936261e-06 4.408063e-06
## [11] 3.661380e-06 1.553189e-06 8.505632e-07 5.282590e-07 3.813104e-07
## [16] 1.386509e-07 1.170517e-07 8.963051e-08 5.393089e-08 3.354159e-08
## [21] 2.850385e-08 2.095280e-08 1.668543e-08 9.253554e-09 7.824310e-09
## [26] 6.320954e-10 6.851839e-11 8.527574e-12 1.963408e-31

datTrait_result <- data.frame(datTrait_results$rotation)
datTrait_result

write.csv(datTrait_result, 
"/Users/Leon/OneDrive - Cornell University/Projects/9_Sorghum/5_Phenotype/2_Clean_data/Oxdative_PCA.csv")

Re-load the clean data

weight <- read.csv("/Users/Leon/OneDrive - Cornell University/Projects/9_Sorghum/5_Phenotype/2_Clean_data/Weight.csv")
Oxidative <- read.csv("/Users/Leon/OneDrive - Cornell University/Projects/9_Sorghum/5_Phenotype/2_Clean_data/Oxidative_clean-20220324.csv")
LIM <- read.csv("/Users/Leon/OneDrive - Cornell University/Projects/9_Sorghum/5_Phenotype/2_Clean_data/LI6800_clean-20220324-morning.csv")
LIA <- read.csv("/Users/Leon/OneDrive - Cornell University/Projects/9_Sorghum/5_Phenotype/2_Clean_data/LI6800_clean-20220324-afternoon.csv")

Pearson Correlation and clustering of three classes of phenotype

Oxidative_cor <- Oxidative
rownames(Oxidative_cor) <- Oxidative_cor$Name
Oxidative_cor <- Oxidative_cor[,c(7:35)]
M <- cor(Oxidative_cor)
Cor_plot1 <- corrplot(M, method = 'color', order = 'hclust',
         addrect = 6, rect.col = 'blue', rect.lwd = 2,)

weight_cor <- weight
rownames(weight_cor) <- weight_cor$Plant_ID
weight_cor <- weight_cor[,c(6:10)]
M <- cor(weight_cor)
Cor_plot2 <- corrplot(M, method = 'color', order = 'hclust',
                      addrect = 2, rect.col = 'blue', rect.lwd = 2,)

LIM_cor <- LIM
rownames(LIM_cor) <- LIM_cor$Name
LIM_cor <- LIM_cor[,c(8:19)]
M <- cor(LIM_cor)
Cor_plot3 <- corrplot(M, method = 'color', order = 'hclust',
         addrect = 5, rect.col = 'blue', rect.lwd = 2,)

LIA_cor <- LIA
rownames(LIA_cor) <- LIA_cor$Name
LIA_cor <- LIA_cor[,c(8:19)]
M <- cor(LIA_cor)
Cor_plot4 <- corrplot(M, method = 'color', order = 'hclust',
                      addrect = 5, rect.col = 'blue', rect.lwd = 2,)

Plot the plant weight data

### Plot weight data
P1 <- ggplot(weight, aes(x = Genotype, y = biomass_total, fill = Treat)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.8, color = "grey40") +
      theme_bw() +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Treat), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Genotype), method = "anova",label.y = 900)
      
P2 <- ggplot(weight, aes(x = Genotype, y = biomass_bag, fill = Treat)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                    size = 0.8, color = "grey40") +
      theme_bw() +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Treat), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Genotype), method = "anova",label.y = 85)

P3 <- ggplot(weight, aes(x = Genotype, y = root_total, fill = Treat)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                    size = 0.8, color = "grey40") +
      theme_bw() +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Treat), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Genotype), method = "anova",label.y = 350)


P4 <- ggplot(weight, aes(x = Genotype, y = bag_total, fill = Treat)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.8, color = "grey40") +
      theme_bw() +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Treat), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Genotype), method = "anova",label.y = 40)


P5 <- ggplot(weight, aes(x = Genotype, y = Three_leaves, fill = Treat)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.8, color = "grey40") +
      theme_bw() +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Treat), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Genotype), method = "anova",label.y = 12)

plot_grid(P1,P2,P3,P4,P5, labels = c('A', 'B', 'C', 'D', 'E'), label_size = 12)

plot Licor6800 (afternoon) data

data_plot = LIA

M1 <- ggplot(data_plot, aes(x = Time, y = WUEi, fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 300)

M2 <- ggplot(data_plot, aes(x = Time, y = A, fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 70)

M3 <- ggplot(data_plot, aes(x = Time, y = Ci, fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 210)


M4 <- ggplot(data_plot, aes(x = Time, y = gsw, fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 1)

M5 <- ggplot(data_plot, aes(x = Time, y = VPDleaf, fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 8)


M6 <- ggplot(data_plot, aes(x = Time, y = Fm., fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 2500)

M7 <- ggplot(data_plot, aes(x = Time, y = Fv..Fm., fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 1)


M8 <- ggplot(data_plot, aes(x = Time, y = Fo., fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 850)

M9 <- ggplot(data_plot, aes(x = Time, y = Fv., fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 1650)

M10 <- ggplot(data_plot, aes(x = Time, y = H2O_r, fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 50)

M11 <- ggplot(data_plot, aes(x = Time, y = Tair, fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 50)

M12 <- ggplot(data_plot, aes(x = Time, y = Tleaf, fill = Condition)) +
      geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                   size = 0.5, color = "grey40") +
      theme_bw() +
      facet_wrap( ~ Genotype) +
      geom_jitter(size = 0.6, color = "red") +
      scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
      stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                 geom = "point", shape = 4, size = 3,
                 show.legend = FALSE) +
      stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
      stat_compare_means(aes(group = Time), method = "anova",label.y = 50)

#plot_grid(M1,M2,M3,M4,M5,M6,M7,M8,M9,M10,M11,M12, 
          #labels = c('A', 'B', 'C', 'D', 'E',
                    # 'F', 'G', 'H', 'I', 'J', 'K', 'L'), label_size = 12)
M1

M2

M3

M4

M5

M6

M7

M8

M9

M10

M11

M12 

Plot the oxdative stress data phenotype

Oxidative 

Oxd_vector <- colnames(Oxidative[,7:35])

for (i in 1:length(Oxd_vector)){
  Yplot <- Oxd_vector[i]
  print(ggplot(Oxidative, aes(x = Time, y = get(Yplot), fill = Condition)) +
        geom_boxplot(outlier.size = 0.3, outlier.shape = 21,
                     size = 0.5, color = "grey40") +
        theme_bw() +
        facet_wrap( ~ Genotype) +
        geom_jitter(size = 0.6, color = "red") +
        scale_fill_manual(values = c(WL="tomato", WW="steelblue")) +
        stat_summary(fun = mean, color = "darkred", position = position_dodge(0.75),
                   geom = "point", shape = 4, size = 3,
                   show.legend = FALSE) +
        stat_compare_means(aes(group = Condition), method = "t.test", label = "p.signif") +
        stat_compare_means(aes(group = Time), method = "anova", label.y = 1.2 * max(Oxidative[,Oxd_vector[i]])) +
        ylab(Oxd_vector[i]))
  #assign(paste0(Oxd_vector[i], "_plot_OXD"),plot_df)
}

Combine figure panel (optional)

OXD_list <- ls(pattern = "_plot_OXD")
plot_grid(AO_plot_OXD,APX_plot_OXD,ascorbate_plot_OXD,caro_plot_OXD,
          CAT_plot_OXD,chl_a_plot_OXD,chl_a.b_plot_OXD,chl_b_plot_OXD,
          DHAR_plot_OXD,flav_plot_OXD,Frxs_plot_OXD,glutathione_plot_OXD,
          GOX_plot_OXD,GPX_plot_OXD,GR_plot_OXD,Grxs_plot_OXD,HPR_plot_OXD,
          LOX_plot_OXD,MDA_plot_OXD,MDHAR_plot_OXD,polyph_plot_OXD,
          POX_plot_OXD,pro_plot_OXD,Prxs_plot_OXD,SOD_plot_OXD,sugars_plot_OXD,
          TAC_plot_OXD,total_AA_plot_OXD,Trxs_plot_OXD,
          label_size = 12)