Root_Shoot_FW_Arabidopsis_duf247-5_RSA

Experimental procedures:

Seeds were sterilized with 50% bleach for 10 min, rinsed 5 times with MQ sterile water, and incubated at 4°C for 24 h. ½ ms media + sucrose (including vitamins) was made for both control and salt with 75 and 125 mM NaCl. Salt added after PH adjustment.The seeds were germinated for 4 complete days at control plate, and then at d5, they were transferred to +/- salt plates, and scanned for 5 times, every other day. and then the samples were harvested after 2 weeks on salt plate for root and shoot FW. The Arabidopsis growth chamber (#34 at BTI) had 21 °C and 16 h light/8 h dark cycle, with 60% humidity.

getwd()
## [1] "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20211004_duf160-5_RSA_correct_genotype"
setwd("C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20211004_duf160-5_RSA_correct_genotype/")
getwd()
## [1] "C:/Users/Julkowska Lab/Desktop/R codes by Maryam/20211004_duf160-5_RSA_correct_genotype"
list.files(pattern = ".csv")
## [1] "20211011_duf-5_growth_factors.csv"                                         
## [2] "20211013_duf-5_growth_factors.csv"                                         
## [3] "8-19-21 FW for duf160-5 correct genotype at 0 75 125 salt_2weeksOnSalt.csv"
## [4] "d11_duf160-5_correct_Genotype.csv"                                         
## [5] "d13_duf160-5_correct_Genotype.csv"                                         
## [6] "d5_duf160-5_correct_Genotype.csv"                                          
## [7] "d7_duf160-5_correct_Genotype.csv"                                          
## [8] "d9_duf160-5_correct_Genotype.csv"                                          
## [9] "Eric10282021R1and R2_analyzed v3 for R analysis.csv"
Arabidopsis <- read.csv("8-19-21 FW for duf160-5 correct genotype at 0 75 125 salt_2weeksOnSalt.csv")

I was seeing NA graph….so I ran this code to omit NA from my data, and then check by unique to see whether it is fixed or not.

Arabidopsis2 <- na.omit(Arabidopsis)
Arabidopsis2
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.0.5
library(ggpubr)
## Warning: package 'ggpubr' was built under R version 4.0.5
library(RColorBrewer)
library(ggridges)
library(gapminder)
library(ggbeeswarm)
unique(Arabidopsis2$Condition)
## [1] 125   0  75
unique(Arabidopsis2$Genotype)
## [1] "duf" "col"
Shoot_plot <- ggplot(data = Arabidopsis2, mapping = aes(x = Genotype, y = Shoot.weight..g., colour=Genotype))
Shoot_plot <- Shoot_plot + geom_quasirandom(alpha = 0.6)
Shoot_plot <- Shoot_plot + facet_wrap(~Condition)
Shoot_plot <- Shoot_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=10, color="black", fill="black")
## Warning: `fun.y` is deprecated. Use `fun` instead.
Shoot_plot <- Shoot_plot + scale_color_manual (values = c("blue", "red"))
Shoot_plot <- Shoot_plot + labs(y = "weight (g)", x = "", title = "Shoot Weight")
my_comparisons <- list( c("col", "duf"))
Shoot_plot <- Shoot_plot + stat_compare_means(comparisons = my_comparisons, method = "t.test")
Shoot_plot <- Shoot_plot + theme(legend.position = "none")
Shoot_plot

pdf("Arabidopsis_duf-5_Shoot_Plot.pdf")
Shoot_plot
dev.off()
## png 
##   2

now I try to plot root FW.

Root_plot <- ggplot(data = Arabidopsis2, mapping = aes(x = Genotype, y = Root.weight..g., colour=Genotype))
Root_plot <- Root_plot + geom_quasirandom(alpha = 0.6)
Root_plot <- Root_plot + facet_wrap(~Condition)
Root_plot <- Root_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=10, color="black", fill="black")
## Warning: `fun.y` is deprecated. Use `fun` instead.
Root_plot <- Root_plot + scale_color_manual (values = c("blue", "red"))
Root_plot <- Root_plot + labs(y = "weight (g)", x = "", title = "Root Weight")
my_comparisons <- list( c("col", "duf"))
Root_plot <- Root_plot + stat_compare_means(comparisons = my_comparisons, method = "t.test")
Root_plot <- Root_plot + theme(legend.position = "none")
Root_plot

pdf("Arabidopsis_duf-5_Root_Plot.pdf")
Root_plot
dev.off()
## png 
##   2

NOW let’s calculate root per shoot ratio

RpS_plot <- ggplot(data = Arabidopsis2, mapping = aes(x = Genotype, y = Root.Shoot, colour=Genotype)) 
RpS_plot <- RpS_plot + geom_quasirandom(alpha = 0.6)
RpS_plot <- RpS_plot + facet_wrap(~Condition) 
RpS_plot <- RpS_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=10, color="black", fill="black")
## Warning: `fun.y` is deprecated. Use `fun` instead.
RpS_plot <- RpS_plot + scale_color_manual (values = c("blue", "red"))
RpS_plot <- RpS_plot + labs(y = "Ratio (Root / Shoot)", x = "", title = "Root per Shoot ratio")
my_comparisons <- list( c("col", "duf"))
RpS_plot <- RpS_plot + stat_compare_means(comparisons = my_comparisons, method = "t.test")
RpS_plot <- RpS_plot + theme(legend.position = "none")
RpS_plot

now let’s calculate whole seedling weight

Arabidopsis2$total <- Arabidopsis2$Shoot.weight..g.+ Arabidopsis2$Root.weight..g.
head(Arabidopsis2)

now I want to plot it

Total_plot <- ggplot(data = Arabidopsis2, mapping = aes(x = Genotype, y = total, colour=Genotype)) 
Total_plot <- Total_plot + geom_quasirandom(alpha = 0.6)
Total_plot <- Total_plot + facet_wrap(~Condition) 
Total_plot <- Total_plot + stat_summary(fun.y=mean, geom="point", shape=95, size=10, color="black", fill="black")
## Warning: `fun.y` is deprecated. Use `fun` instead.
Total_plot <- Total_plot + scale_color_manual (values = c("blue", "red"))
Total_plot <- Total_plot + labs(y = "weight (g)", x = "", title = "Whole Seedling Weight")
my_comparisons <- list( c("col", "duf"))
Total_plot <- Total_plot + stat_compare_means(comparisons = my_comparisons, method = "t.test")
Total_plot <- Total_plot + theme(legend.position = "none")
Total_plot

library(cowplot)
## 
## Attaching package: 'cowplot'
## The following object is masked from 'package:ggpubr':
## 
##     get_legend
pdf("20211012_Arabidopsis_duf247-5_Fresh_weight_All.pdf", width = 13, height = 13)
plot_grid(Root_plot, Shoot_plot, Total_plot, RpS_plot, labels = c("AUTO"), ncol = 2)
#plot_grid(Shoot_plot, Root_plot, Total_plot, RpS_plot, labels = c("AUTO"), ncol = 2)
dev.off()
## png 
##   2