Hi everyone!

First question here, so if I will be not totally clear or not following rules, please tell me so I can adapt this and future qustions : )

I am analyzing data on some biossays and I need an advice on which statistical analysis to use and how to use ggplot2 R package to better visualize the results.

As you can see in the above image If have infectred bean plants with a pathogen, a Pseudomonas bacterium, and then tested the treatment with bacteriophages applied at the same time or 1h before the inoculation. In the scatter plot on the right you can see two repetitions of the four I have. In each plot on th y axis there is the disease index of the plants (from healthy plants designed by 0 to deceased plants, 5). I wanted to test if there is a significant difference between the four cases (positive and negative control and the two phage applications) and between all the possible pairs. I tried with Kruskall-Wallis test and Wilcoxon paired test and I found out that there are significant differences only in two of the four repetition (the two represented).

Now the doubts I have: 1) Are there better suited statistical test I can conduct? 2) There is a better way to visualize dthe data (I applied the letters directly on the image with power point, but I would prefer to do all by an R script)

I add here the script I used to analyze the data.

Thank you in advance for your time and help !

# load libraries

library(tidyverse) library(patchwork) library(broom) library(agricolae) library(ggplot2) library (reshape2)

Clear the Worskspace

rm(list=ls())

Read data

DF<- read_csv2("R_summary_bioassay_4.csv")

Looks at data as structure , first rows or whole dataset

str(DF) head(DF) View(DF)

DF1 <- DF %>% filter(date=="23/11/2020")

DF2 <- DF %>% filter(date=="18/02/2022")

DF3 <- DF %>% filter(date=="22/03/2022")

DF4 <- DF %>% filter(date=="29/04/2022")

class(DF$treatment)

plot1 <- DF1 %>% ggplot(aes(x = treatment, y = index)) + geom_col(fill = "grey") +

geom_errorbar(aes(ymax = M + SE, ymin = M - SE), width = .05) +

geom_jitter(data = DF, aes(x = treatment, y = index), width = 0.1, height = 0) + theme_classic() + theme(text = element_text(size = 20 )) + theme(axis.title.y = element_text(size = 14)) + labs(title = "23/11/2020",

caption = paste0("ANOVA value: ", unique(x$ANOVA)),

   x = "Treatment",
   y = "Disease index") 

plot1

plot2 <- DF2 %>% ggplot(aes(x = treatment, y = index)) + geom_col(fill = "grey") +

geom_errorbar(aes(ymax = M + SE, ymin = M - SE), width = .05) +

geom_jitter(data = DF, aes(x = treatment, y = index), width = 0.1, height = 0) + theme_classic() + theme(text = element_text(size = 20 )) + theme(axis.title.y = element_text(size = 14)) + labs(title = "18/02/2022",

   # caption = paste0("ANOVA value: ", unique(x$ANOVA)),
   x = "Treatment",
   y = "Disease index") 

plot2

plot3 <- DF3 %>% ggplot(aes(x = treatment, y = index)) + geom_col(fill = "grey") +

geom_errorbar(aes(ymax = M + SE, ymin = M - SE), width = .05) +

geom_jitter(data = DF, aes(x = treatment, y = index), width = 0.1, height = 0) + theme_classic() + theme(text = element_text(size = 20 )) + theme(axis.title.y = element_text(size = 14)) + labs(title = "23/03/2022",

   # caption = paste0("ANOVA value: ", unique(x$ANOVA)),
   x = "Treatment",
   y = "Disease index") 

plot3

plot4<- DF4 %>% ggplot(aes(x = treatment, y = index)) + geom_col(fill = "grey") +

geom_errorbar(aes(ymax = M + SE, ymin = M - SE), width = .05) +

geom_jitter(data = DF, aes(x = treatment, y = index), width = 0.1, height = 0) + theme_classic() + theme(text = element_text(size = 20 )) + theme(axis.title.y = element_text(size = 14)) + labs(title = "23/03/2022",

   # caption = paste0("ANOVA value: ", unique(x$ANOVA)),
   x = "Treatment",
   y = "Disease index") 

plot4

Correzione Simona, grafici scatter plot

scatterplot1 <- DF1 %>% ggplot(aes(x = treatment, y = index)) +

geom_col(fill = "grey") +

geom_errorbar(aes(ymax = M + SE, ymin = M - SE), width = .05) +

geom_jitter(data = DF1, aes(x = treatment, y = index, colour = treatment), show.legend = F, width = 0.1, height = 0) +

theme_classic() + theme(text = element_text(size = 20 )) + theme(axis.title.y = element_text(size = 14)) + theme(axis.text.x = element_text(angle = -15, vjust = 0.5, hjust=0)) +

labs(title = "23/11/2020",

   # caption = paste0("ANOVA value: ", unique(x$ANOVA)),
   x = "Treatment",
   y = "Disease index")

scatterplot1

scatterplot2 <- DF2 %>% ggplot(aes(x = treatment, y = index)) +

geom_col(fill = "grey") +

geom_errorbar(aes(ymax = M + SE, ymin = M - SE), width = .05) +

geom_jitter(data = DF2, aes(x = treatment, y = index, colour = treatment), show.legend = F, width = 0.1, height = 0) +

theme_classic() + theme(text = element_text(size = 20 )) + theme(axis.title.y = element_text(size = 14)) + theme(axis.text.x = element_text(angle = -15, vjust = 0.5, hjust=0)) +

labs(title = "18/02/202",

   # caption = paste0("ANOVA value: ", unique(x$ANOVA)),
   x = "Treatment",
   y = "Disease index")

scatterplot2

scatterplot3 <- DF3 %>% ggplot(aes(x = treatment, y = index)) +

geom_col(fill = "grey") +

geom_errorbar(aes(ymax = M + SE, ymin = M - SE), width = .05) +

geom_jitter(data = DF3, aes(x = treatment, y = index, colour = treatment), show.legend = F, width = 0.1, height = 0) +

theme_classic() + theme(text = element_text(size = 20 )) + theme(axis.title.y = element_text(size = 14)) + theme(axis.text.x = element_text(angle = -15, vjust = 0.5, hjust=0)) +

labs(title = "22/03/2022",

   # caption = paste0("ANOVA value: ", unique(x$ANOVA)),
   x = "Treatment",
   y = "Disease index")

scatterplot3

scatterplot4 <- DF4 %>% ggplot(aes(x = treatment, y = index)) +

geom_col(fill = "grey") +

geom_errorbar(aes(ymax = M + SE, ymin = M - SE), width = .05) +

geom_jitter(data = DF4, aes(x = treatment, y = index, colour = treatment), show.legend = F, width = 0.1, height = 0) +

theme_classic() + theme(text = element_text(size = 20 )) + theme(axis.title.y = element_text(size = 14)) + theme(axis.text.x = element_text(angle = -15, vjust = 0.5, hjust=0)) +

labs(title = "29/04/2022",

   # caption = paste0("ANOVA value: ", unique(x$ANOVA)),
   x = "Treatment",
   y = "Disease index")

scatterplot4

kruskal.test(index ~ treatment, data = DF1) kruskal.test(index ~ treatment, data = DF2) kruskal.test(index ~ treatment, data = DF3) kruskal.test(index ~ treatment, data = DF4)

pairwise.wilcox.test(DF1$index, DF1$treatment, p.adjust.method = "BH") pairwise.wilcox.test(DF2$index, DF2$treatment, p.adjust.method = "BH") pairwise.wilcox.test(DF3$index, DF3$treatment, p.adjust.method = "BH") pairwise.wilcox.test(DF4$index, DF4$treatment, p.adjust.method = "BH")