2020-kruskal

R Markdown

#https://rcompanion.org/handbook/F_08.html
#library
library("readxl")
library(ggplot2) 

## Warning: package 'ggplot2' was built under R version 3.6.3

#Open data
df<-read_excel("D:/Research/REFERENCES/Network analysis/PROJECT/FATIMA/F.xlsx",sheet = "AMY 3")

## New names:
## * `` -> ...13
## * `` -> ...14
## * `` -> ...15
## * `` -> ...16
## * `` -> ...17
## * ...

df

## # A tibble: 42 x 22
##    Group Genotype Groupxgenotype    D2L   D2S    D1  STMN1 NR3C1 Rab11  Rab4
##    <chr> <chr>    <chr>           <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
##  1 CON   VEH      CON-VEH         0.857 0.269 0.782 3.24   1.73  0.935 0.644
##  2 CON   VEH      CON-VEH         0.211 0.315 0.928 1.29   0.524 1.03  0.870
##  3 CON   VEH      CON-VEH         0.153 0.311 0.782 2.63   0.792 0.960 0.973
##  4 CON   VEH      CON-VEH         1.17  0.526 1.35  2.15   1.19  0.845 0.645
##  5 CON   VEH      CON-VEH         4.32  2.55  0.860 1.57   0.753 0.946 1.14 
##  6 CON   VEH      CON-VEH         3.91  5.39  1.24  0.0111 1.45  1.18  2.58 
##  7 CON   VEH      CON-VEH         1.83  5.24  1.22  2.42   1.07  1.15  0.968
##  8 CON   DRUG     CON-DRUG        5.37  1.34  1.82  2.70   1.32  0.849 1.10 
##  9 CON   DRUG     CON-DRUG       10.9   7.77  4.10  2.78   1.45  0.700 0.909
## 10 CON   DRUG     CON-DRUG        6.64  2.46  4.20  1.20   0.916 0.778 0.884
## # ... with 32 more rows, and 12 more variables: GASP1 <dbl>, ARF6 <dbl>,
## #   ...13 <lgl>, ...14 <lgl>, ...15 <lgl>, ...16 <lgl>, ...17 <lgl>,
## #   ...18 <lgl>, ...19 <lgl>, ...20 <chr>, ...21 <chr>, ...22 <chr>

#Plot
library("ggpubr")

## Warning: package 'ggpubr' was built under R version 3.6.2

## Loading required package: magrittr

ggline(df, x = "Groupxgenotype", y = "D2L",add = c("mean_se", "jitter"))

#Nonparametric-kruskal test
kruskal.test(D2L ~ Groupxgenotype, data = df)

## 
##  Kruskal-Wallis rank sum test
## 
## data:  D2L by Groupxgenotype
## Kruskal-Wallis chi-squared = 11.46, df = 5, p-value = 0.04298

#Posthoc

library(FSA)

## ## FSA v0.8.27. See citation('FSA') if used in publication.
## ## Run fishR() for related website and fishR('IFAR') for related book.

df$Groupxgenotype=as.factor(df$Groupxgenotype)
dunnTest(D2L ~ Groupxgenotype, data = df,
              method="bh")  

## Dunn (1964) Kruskal-Wallis multiple comparison

##   p-values adjusted with the Benjamini-Hochberg method.

##             Comparison           Z     P.unadj      P.adj
## 1   CON-DRUG - CON-VEH  2.65783223 0.007864503 0.05898378
## 2  CON-DRUG - SUS-DRUG  0.23964061 0.810608879 0.86850951
## 3   CON-VEH - SUS-DRUG -2.41819162 0.015597861 0.07798930
## 4   CON-DRUG - SUS-VEH  1.21998856 0.222469211 0.37078202
## 5    CON-VEH - SUS-VEH -1.43784366 0.150478417 0.37619604
## 6   SUS-DRUG - SUS-VEH  0.98034795 0.326914392 0.44579235
## 7  CON-DRUG - UNS-DRUG -0.06535653 0.947890134 0.94789013
## 8   CON-VEH - UNS-DRUG -2.72318876 0.006465511 0.09698266
## 9  SUS-DRUG - UNS-DRUG -0.30499714 0.760368330 0.95046041
## 10  SUS-VEH - UNS-DRUG -1.28534509 0.198671719 0.42572511
## 11  CON-DRUG - UNS-VEH  1.50320019 0.132787428 0.39836228
## 12   CON-VEH - UNS-VEH -1.15463203 0.248241143 0.37236172
## 13  SUS-DRUG - UNS-VEH  1.26355958 0.206388148 0.38697778
## 14   SUS-VEH - UNS-VEH  0.28321163 0.777014613 0.89655532
## 15  UNS-DRUG - UNS-VEH  1.56855672 0.116751263 0.43781724