library(readxl)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggpubr)

## Loading required package: ggplot2

library(prettydoc)

## Warning: package 'prettydoc' was built under R version 4.1.2

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

band na isti visini (YBX1 = YBX1pSer102)

WB<- read_excel("Z:/TSever/Western Blot/YBX1_pSer102.xlsx", 
    sheet = "YBX1pS")

invert values of band and background intenseties

WB$liver_YBX1_inverted<-c(255-WB$Liver_YBX1)
WB$liver_YBX1_background_inverted<-c(255-WB$liver_YBX1_background)
WB$kidney_YBX1_inverted<-c(255-WB$Kidney_YBX1)
WB$kidney_YBX1_background_inverted<-c(255-WB$kidney_YBX1_background)
WB$spleen_YBX1_inverted<-c(255-WB$spleen_YBX1)
WB$spleen_YBX1_background_inverted<-c(255-WB$spleen_YBX1_background)
WB$liver_YBX1_GAPDH_background_inverted<-c(255-WB$liver_YBX1_GAPDH)
WB$liver_YBX1_GAPDH_inverted<-c(255-WB$liver_YBX1_GAPDH_background)
WB$kidney_YBX1_GAPDH_inverted<-c(255-WB$kidney_YBX1_GAPDH)
WB$kidney_YBX1_GAPDH_background_inverted<-c(255-WB$kidney_YBX1_GAPDH_background)
WB$spleen_YBX1_GAPDH_inverted<-c(255-WB$spleen_YBX1_GAPDH)
WB$spleen_YBX1_GAPDH_background_inverted<-c(255-WB$spleen_YBX1_GAPDH_background)

WB$liver_YBX1pS102_inverted<-c(255-WB$Liver_YBX1pS102)
WB$liver_YBX1pS102_background_inverted<-c(255-WB$liver_YBX1pS102_background)
WB$kidney_YBX1pS102_inverted<-c(255-WB$Kidney_YBX1pS102)
WB$kidney_YBX1pS102_background_inverted<-c(255-WB$kidney_YBX1pS102_background)
WB$spleen_YBX1pS102_inverted<-c(255-WB$spleen_YBX1pS102)
WB$spleen_YBX1pS102_background_inverted<-c(255-WB$spleen_YBX1pS102_background)
WB$liver_YBX1pS102_GAPDH_inverted<-c(255-WB$liver_YBX1pS102_GAPDH)
WB$liver_YBX1pS102_GAPDH_background_inverted<-c(255-WB$liver_YBX1pS102_GAPDH_background)
WB$kidney_YBX1pS102_GAPDH_inverted<-c(255-WB$kidney_YBX1pS102_GAPDH)
WB$kidney_YBX1pS102_GAPDH_background_inverted<-c(255-WB$kidney_YBX1pS102_GAPDH_background)
WB$spleen_YBX1pS102_GAPDH_inverted<-c(255-WB$spleen_YBX1pS102_GAPDH)
WB$spleen_YBX1pS102_GAPDH_background_inverted<-c(255-WB$spleen_YBX1pS102_GAPDH_background)

deduct background

WB[,50]<-c(WB$liver_YBX1_inverted - WB$liver_YBX1_background_inverted)
WB[,51]<-c(WB$kidney_YBX1_inverted - WB$kidney_YBX1_background_inverted)
WB[,52]<-c(WB$spleen_YBX1_inverted - WB$spleen_YBX1_background_inverted)
WB[,53]<-c(WB$liver_YBX1_GAPDH_inverted - WB$liver_YBX1_GAPDH_background_inverted)
WB[,54]<-c(WB$kidney_YBX1_GAPDH_inverted - WB$kidney_YBX1_GAPDH_background_inverted)
WB[,55]<-c(WB$spleen_YBX1_GAPDH_inverted - WB$spleen_YBX1_GAPDH_background_inverted)

WB[,56]<-c(WB$liver_YBX1pS102_inverted - WB$liver_YBX1pS102_background_inverted)
WB[,57]<-c(WB$kidney_YBX1pS102_inverted - WB$kidney_YBX1pS102_background_inverted)
WB[,58]<-c(WB$spleen_YBX1pS102_inverted - WB$spleen_YBX1pS102_background_inverted)
WB[,59]<-c(WB$liver_YBX1pS102_GAPDH_inverted - WB$liver_YBX1pS102_GAPDH_background_inverted)
WB[,60]<-c(WB$kidney_YBX1pS102_GAPDH_inverted - WB$kidney_YBX1pS102_GAPDH_background_inverted)
WB[,61]<-c(WB$spleen_YBX1pS102_GAPDH_inverted - WB$spleen_YBX1pS102_GAPDH_background_inverted)

colnames(WB)[c(50,51,52,53,54,55,56,57,58,59,60,61)] <-c('net_liver_YBX1','net_kidney_YBX1','net_spleen_YBX1','net_liver_GAPDH_YBX1','net_kidney_GAPDH_YBX1','net_spleen_GAPDH_YBX1','net_liver_YBX1pS102','net_kidney_YBX1pS102','net_spleen_YBX1pS102','net_liver_GAPDH_YBX1pS102','net_kidney_GAPDH_YBX1pS102','net_spleen_GAPDH_YBX1pS102')

calculate ratio of net protein over net loading control (YBOX1 / GAPDH)

WB[,62]<-c(WB$net_liver_YBX1 / WB$net_liver_GAPDH_YBX1)
WB[,63]<-c(WB$net_kidney_YBX1 / WB$net_kidney_GAPDH_YBX1)
WB[,64]<-c(WB$net_spleen_YBX1 / WB$net_spleen_GAPDH_YBX1)

WB[,65]<-c(WB$net_liver_YBX1pS102 / WB$net_liver_GAPDH_YBX1pS102)
WB[,66]<-c(WB$net_kidney_YBX1pS102 / WB$net_kidney_GAPDH_YBX1pS102)
WB[,67]<-c(WB$net_spleen_YBX1pS102 / WB$net_spleen_GAPDH_YBX1pS102)

colnames(WB)[c(62,63,64,65,66,67)]<-c('ratio_liver_YBX1','ratio_kidney_YBX1','ratio_spleen_YBX1','ratio_liver_YBX1pS102','ratio_kidney_YBX1pS102','ratio_spleen_YBX1pS102')

calculate ration phosphorilated to whole protein

WB[,68]<-c(WB$ratio_liver_YBX1 / WB$ratio_liver_YBX1pS102)
WB[,69]<-c(WB$ratio_kidney_YBX1 / WB$ratio_kidney_YBX1pS102)
WB[,70]<-c(WB$ratio_spleen_YBX1 / WB$ratio_spleen_YBX1pS102)

colnames(WB)[c(68,69,70)]<-c('ratio_liver_YBX1YBX1pS102','ratio_kidney_YBX1YBX1pS102','ratio_spleen_YBX1YBX1pS102')

group treatments by changing names of rows (WT=44, 45, 46, KO= 58, 60, 68)

WBt<-WB

WBt[,1]<-c('WT','WT','WT','KO','KO','KO')

Liver

F-test

F test is used to check if variances of both sets of data are equal

var.test(ratio_liver_YBX1YBX1pS102 ~ Sample, data=WBt )

## 
##  F test to compare two variances
## 
## data:  ratio_liver_YBX1YBX1pS102 by Sample
## F = 8.5167, num df = 2, denom df = 2, p-value = 0.2102
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##    0.2183761 332.1500946
## sample estimates:
## ratio of variances 
##           8.516669

the p-value of F test is p=0.5687, greather than alpha=0.05, alternative hypothesis accepted -> thereis no significant difference between the variances of the two data sets

Shapiro-Wilk test

Shapiro-Wilk test is used to test if data has normal distribution

with(WBt, shapiro.test(ratio_liver_YBX1YBX1pS102 [Sample == 'WT']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_liver_YBX1YBX1pS102[Sample == "WT"]
## W = 0.9546, p-value = 0.5899

with(WBt, shapiro.test(ratio_liver_YBX1YBX1pS102 [Sample == 'KO']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_liver_YBX1YBX1pS102[Sample == "KO"]
## W = 0.99373, p-value = 0.8486

both groups have normal distribution (p-value greater than 0.05)

t-test

unpaired t-test is used to check wheter samples have different means

liver_test<-t.test(ratio_liver_YBX1YBX1pS102  ~ Sample , data = WBt, var.equal = TRUE, conf.level = 0.99)
liver_test

## 
##  Two Sample t-test
## 
## data:  ratio_liver_YBX1YBX1pS102 by Sample
## t = 0.22785, df = 4, p-value = 0.8309
## alternative hypothesis: true difference in means between group KO and group WT is not equal to 0
## 99 percent confidence interval:
##  -14.72859  16.26226
## sample estimates:
## mean in group KO mean in group WT 
##        -7.535454        -8.302291

p-value of the t-test is 0.03744, which is less than alpha 0.05. the means are different

liv<-ggboxplot(WBt, x = "Sample", y = "ratio_liver_YBX1YBX1pS102", 
          color = "Sample", palette = c("#00AFBB", "#E7B800"),
        ylab = "normalised band intensity", xlab = "Sample", main='Liver')+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(legend.position = 'right')
liv

Kidney

F-test

F test is used to check if variances of both sets of data are equal

var.test(ratio_kidney_YBX1YBX1pS102 ~ Sample, data=WBt )

## 
##  F test to compare two variances
## 
## data:  ratio_kidney_YBX1YBX1pS102 by Sample
## F = 4.9204, num df = 2, denom df = 2, p-value = 0.3378
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##    0.1261629 191.8937708
## sample estimates:
## ratio of variances 
##           4.920353

the p-value of F test is p=0.5687, greather than alpha=0.05, alternative hypothesis accepted -> thereis no significant difference between the variances of the two data sets

Shapiro-Wilk test

Shapiro-Wilk test is used to test if data has normal distribution

with(WBt, shapiro.test(ratio_kidney_YBX1YBX1pS102 [Sample == 'WT']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_kidney_YBX1YBX1pS102[Sample == "WT"]
## W = 0.93168, p-value = 0.4949

with(WBt, shapiro.test(ratio_kidney_YBX1YBX1pS102 [Sample == 'KO']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_kidney_YBX1YBX1pS102[Sample == "KO"]
## W = 0.92702, p-value = 0.4776

both groups have normal distribution (p-value greater than 0.05)

t-test

unpaired t-test is used to check wheter samples have different means

kidney_test<-t.test(ratio_kidney_YBX1YBX1pS102  ~ Sample , data = WBt, var.equal = TRUE, conf.level = 0.99)
kidney_test

## 
##  Two Sample t-test
## 
## data:  ratio_kidney_YBX1YBX1pS102 by Sample
## t = 0.37509, df = 4, p-value = 0.7266
## alternative hypothesis: true difference in means between group KO and group WT is not equal to 0
## 99 percent confidence interval:
##  -9.723074 11.447867
## sample estimates:
## mean in group KO mean in group WT 
##         6.668270         5.805874

p-value of the t-test is 0.03744, which is less than alpha 0.05. the means are different

kid<-ggboxplot(WBt, x = "Sample", y = "ratio_kidney_YBX1YBX1pS102", 
          color = "Sample", palette = c("#00AFBB", "#E7B800"),
        ylab = "normalised band intensity", xlab = "Sample", main='Kidney')+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(legend.position = 'right')
kid

Spleen

F-test

F test is used to check if variances of both sets of data are equal

var.test(ratio_spleen_YBX1YBX1pS102 ~ Sample, data=WBt )

## 
##  F test to compare two variances
## 
## data:  ratio_spleen_YBX1YBX1pS102 by Sample
## F = 0.93888, num df = 2, denom df = 2, p-value = 0.9685
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##   0.02407381 36.61626379
## sample estimates:
## ratio of variances 
##          0.9388786

the p-value of F test is p=0.5687, greather than alpha=0.05, alternative hypothesis accepted -> thereis no significant difference between the variances of the two data sets

Shapiro-Wilk test

Shapiro-Wilk test is used to test if data has normal distribution

with(WBt, shapiro.test(ratio_spleen_YBX1YBX1pS102 [Sample == 'WT']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_spleen_YBX1YBX1pS102[Sample == "WT"]
## W = 0.99807, p-value = 0.9162

with(WBt, shapiro.test(ratio_spleen_YBX1YBX1pS102 [Sample == 'KO']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_spleen_YBX1YBX1pS102[Sample == "KO"]
## W = 0.9498, p-value = 0.5684

both groups have normal distribution (p-value greater than 0.05)

t-test

unpaired t-test is used to check wheter samples have different means

spleen_test<-t.test(ratio_spleen_YBX1YBX1pS102  ~ Sample , data = WBt, var.equal = TRUE, conf.level = 0.99)
spleen_test

## 
##  Two Sample t-test
## 
## data:  ratio_spleen_YBX1YBX1pS102 by Sample
## t = -0.74776, df = 4, p-value = 0.4962
## alternative hypothesis: true difference in means between group KO and group WT is not equal to 0
## 99 percent confidence interval:
##  -183.6976  132.3651
## sample estimates:
## mean in group KO mean in group WT 
##        -36.84795        -11.18174

p-value of the t-test is 0.03744, which is less than alpha 0.05. the means are different

spl<-ggboxplot(WBt, x = "Sample", y = "ratio_spleen_YBX1YBX1pS102", 
          color = "Sample", palette = c("#00AFBB", "#E7B800"),
        ylab = "normalised band intensity", xlab = "Sample", main='Spleen')+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(legend.position = 'right')
spl

glavni band (YBX1pSer102 nizje od YBX1)

WB<- read_excel("Z:/TSever/Western Blot/YBX1_pSer102.xlsx", 
    sheet = "YBX1pScopy")

invert values of band and background intenseties

WB$liver_YBX1_inverted<-c(255-WB$Liver_YBX1)
WB$liver_YBX1_background_inverted<-c(255-WB$liver_YBX1_background)
WB$kidney_YBX1_inverted<-c(255-WB$Kidney_YBX1)
WB$kidney_YBX1_background_inverted<-c(255-WB$kidney_YBX1_background)
WB$spleen_YBX1_inverted<-c(255-WB$spleen_YBX1)
WB$spleen_YBX1_background_inverted<-c(255-WB$spleen_YBX1_background)
WB$liver_YBX1_GAPDH_background_inverted<-c(255-WB$liver_YBX1_GAPDH)
WB$liver_YBX1_GAPDH_inverted<-c(255-WB$liver_YBX1_GAPDH_background)
WB$kidney_YBX1_GAPDH_inverted<-c(255-WB$kidney_YBX1_GAPDH)
WB$kidney_YBX1_GAPDH_background_inverted<-c(255-WB$kidney_YBX1_GAPDH_background)
WB$spleen_YBX1_GAPDH_inverted<-c(255-WB$spleen_YBX1_GAPDH)
WB$spleen_YBX1_GAPDH_background_inverted<-c(255-WB$spleen_YBX1_GAPDH_background)

WB$liver_YBX1pS102_inverted<-c(255-WB$Liver_YBX1pS102)
WB$liver_YBX1pS102_background_inverted<-c(255-WB$liver_YBX1pS102_background)
WB$kidney_YBX1pS102_inverted<-c(255-WB$Kidney_YBX1pS102)
WB$kidney_YBX1pS102_background_inverted<-c(255-WB$kidney_YBX1pS102_background)
WB$spleen_YBX1pS102_inverted<-c(255-WB$spleen_YBX1pS102)
WB$spleen_YBX1pS102_background_inverted<-c(255-WB$spleen_YBX1pS102_background)
WB$liver_YBX1pS102_GAPDH_inverted<-c(255-WB$liver_YBX1pS102_GAPDH)
WB$liver_YBX1pS102_GAPDH_background_inverted<-c(255-WB$liver_YBX1pS102_GAPDH_background)
WB$kidney_YBX1pS102_GAPDH_inverted<-c(255-WB$kidney_YBX1pS102_GAPDH)
WB$kidney_YBX1pS102_GAPDH_background_inverted<-c(255-WB$kidney_YBX1pS102_GAPDH_background)
WB$spleen_YBX1pS102_GAPDH_inverted<-c(255-WB$spleen_YBX1pS102_GAPDH)
WB$spleen_YBX1pS102_GAPDH_background_inverted<-c(255-WB$spleen_YBX1pS102_GAPDH_background)

deduct background

WB[,50]<-c(WB$liver_YBX1_inverted - WB$liver_YBX1_background_inverted)
WB[,51]<-c(WB$kidney_YBX1_inverted - WB$kidney_YBX1_background_inverted)
WB[,52]<-c(WB$spleen_YBX1_inverted - WB$spleen_YBX1_background_inverted)
WB[,53]<-c(WB$liver_YBX1_GAPDH_inverted - WB$liver_YBX1_GAPDH_background_inverted)
WB[,54]<-c(WB$kidney_YBX1_GAPDH_inverted - WB$kidney_YBX1_GAPDH_background_inverted)
WB[,55]<-c(WB$spleen_YBX1_GAPDH_inverted - WB$spleen_YBX1_GAPDH_background_inverted)

WB[,56]<-c(WB$liver_YBX1pS102_inverted - WB$liver_YBX1pS102_background_inverted)
WB[,57]<-c(WB$kidney_YBX1pS102_inverted - WB$kidney_YBX1pS102_background_inverted)
WB[,58]<-c(WB$spleen_YBX1pS102_inverted - WB$spleen_YBX1pS102_background_inverted)
WB[,59]<-c(WB$liver_YBX1pS102_GAPDH_inverted - WB$liver_YBX1pS102_GAPDH_background_inverted)
WB[,60]<-c(WB$kidney_YBX1pS102_GAPDH_inverted - WB$kidney_YBX1pS102_GAPDH_background_inverted)
WB[,61]<-c(WB$spleen_YBX1pS102_GAPDH_inverted - WB$spleen_YBX1pS102_GAPDH_background_inverted)

colnames(WB)[c(50,51,52,53,54,55,56,57,58,59,60,61)] <-c('net_liver_YBX1','net_kidney_YBX1','net_spleen_YBX1','net_liver_GAPDH_YBX1','net_kidney_GAPDH_YBX1','net_spleen_GAPDH_YBX1','net_liver_YBX1pS102','net_kidney_YBX1pS102','net_spleen_YBX1pS102','net_liver_GAPDH_YBX1pS102','net_kidney_GAPDH_YBX1pS102','net_spleen_GAPDH_YBX1pS102')

calculate ratio of net protein over net loading control (YBOX1 / GAPDH)

WB[,62]<-c(WB$net_liver_YBX1 / WB$net_liver_GAPDH_YBX1)
WB[,63]<-c(WB$net_kidney_YBX1 / WB$net_kidney_GAPDH_YBX1)
WB[,64]<-c(WB$net_spleen_YBX1 / WB$net_spleen_GAPDH_YBX1)

WB[,65]<-c(WB$net_liver_YBX1pS102 / WB$net_liver_GAPDH_YBX1pS102)
WB[,66]<-c(WB$net_kidney_YBX1pS102 / WB$net_kidney_GAPDH_YBX1pS102)
WB[,67]<-c(WB$net_spleen_YBX1pS102 / WB$net_spleen_GAPDH_YBX1pS102)

colnames(WB)[c(62,63,64,65,66,67)]<-c('ratio_liver_YBX1','ratio_kidney_YBX1','ratio_spleen_YBX1','ratio_liver_YBX1pS102','ratio_kidney_YBX1pS102','ratio_spleen_YBX1pS102')

calculate ration phosphorilated to whole protein

WB[,68]<-c(WB$ratio_liver_YBX1 / WB$ratio_liver_YBX1pS102)
WB[,69]<-c(WB$ratio_kidney_YBX1 / WB$ratio_kidney_YBX1pS102)
WB[,70]<-c(WB$ratio_spleen_YBX1 / WB$ratio_spleen_YBX1pS102)

colnames(WB)[c(68,69,70)]<-c('ratio_liver_YBX1YBX1pS102','ratio_kidney_YBX1YBX1pS102','ratio_spleen_YBX1YBX1pS102')

group treatments by changing names of rows (WT=44, 45, 46, KO= 58, 60, 68)

WBt<-WB

WBt[,1]<-c('WT','WT','WT','KO','KO','KO')

Liver

F-test

F test is used to check if variances of both sets of data are equal

var.test(ratio_liver_YBX1YBX1pS102 ~ Sample, data=WBt )

## 
##  F test to compare two variances
## 
## data:  ratio_liver_YBX1YBX1pS102 by Sample
## F = 0.34326, num df = 2, denom df = 2, p-value = 0.5111
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##   0.008801532 13.387130402
## sample estimates:
## ratio of variances 
##          0.3432598

the p-value of F test is p=0.5687, greather than alpha=0.05, alternative hypothesis accepted -> thereis no significant difference between the variances of the two data sets

Shapiro-Wilk test

Shapiro-Wilk test is used to test if data has normal distribution

with(WBt, shapiro.test(ratio_liver_YBX1YBX1pS102 [Sample == 'WT']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_liver_YBX1YBX1pS102[Sample == "WT"]
## W = 0.88542, p-value = 0.3405

with(WBt, shapiro.test(ratio_liver_YBX1YBX1pS102 [Sample == 'KO']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_liver_YBX1YBX1pS102[Sample == "KO"]
## W = 0.99974, p-value = 0.9693

both groups have normal distribution (p-value greater than 0.05)

t-test

unpaired t-test is used to check wheter samples have different means

liver_test<-t.test(ratio_liver_YBX1YBX1pS102  ~ Sample , data = WBt, var.equal = TRUE, conf.level = 0.99)
liver_test

## 
##  Two Sample t-test
## 
## data:  ratio_liver_YBX1YBX1pS102 by Sample
## t = 0.5537, df = 4, p-value = 0.6093
## alternative hypothesis: true difference in means between group KO and group WT is not equal to 0
## 99 percent confidence interval:
##  -1.619236  2.061943
## sample estimates:
## mean in group KO mean in group WT 
##        -1.308287        -1.529641

p-value of the t-test is 0.03744, which is less than alpha 0.05. the means are different

liv<-ggboxplot(WBt, x = "Sample", y = "ratio_liver_YBX1YBX1pS102", 
          color = "Sample", palette = c("#00AFBB", "#E7B800"),
        ylab = "normalised band intensity", xlab = "Sample", main='Liver')+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(legend.position = 'right')
liv

Kidney

F-test

F test is used to check if variances of both sets of data are equal

var.test(ratio_kidney_YBX1YBX1pS102 ~ Sample, data=WBt )

## 
##  F test to compare two variances
## 
## data:  ratio_kidney_YBX1YBX1pS102 by Sample
## F = 93.286, num df = 2, denom df = 2, p-value = 0.02121
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##     2.391942 3638.144461
## sample estimates:
## ratio of variances 
##           93.28576

the p-value of F test is p=0.5687, greather than alpha=0.05, alternative hypothesis accepted -> thereis no significant difference between the variances of the two data sets

Shapiro-Wilk test

Shapiro-Wilk test is used to test if data has normal distribution

with(WBt, shapiro.test(ratio_kidney_YBX1YBX1pS102 [Sample == 'WT']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_kidney_YBX1YBX1pS102[Sample == "WT"]
## W = 0.76968, p-value = 0.04399

with(WBt, shapiro.test(ratio_kidney_YBX1YBX1pS102 [Sample == 'KO']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_kidney_YBX1YBX1pS102[Sample == "KO"]
## W = 0.82582, p-value = 0.1777

both groups have normal distribution (p-value greater than 0.05)

t-test

unpaired t-test is used to check wheter samples have different means

kidney_test<-t.test(ratio_kidney_YBX1YBX1pS102  ~ Sample , data = WBt, var.equal = TRUE, conf.level = 0.99)
kidney_test

## 
##  Two Sample t-test
## 
## data:  ratio_kidney_YBX1YBX1pS102 by Sample
## t = 1.606, df = 4, p-value = 0.1836
## alternative hypothesis: true difference in means between group KO and group WT is not equal to 0
## 99 percent confidence interval:
##  -3.031427  6.279073
## sample estimates:
## mean in group KO mean in group WT 
##        2.4570183        0.8331954

p-value of the t-test is 0.03744, which is less than alpha 0.05. the means are different

kid<-ggboxplot(WBt, x = "Sample", y = "ratio_kidney_YBX1YBX1pS102", 
          color = "Sample", palette = c("#00AFBB", "#E7B800"),
        ylab = "normalised band intensity", xlab = "Sample", main='Kidney')+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(legend.position = 'right')
kid

Spleen

F-test

F test is used to check if variances of both sets of data are equal

var.test(ratio_spleen_YBX1YBX1pS102 ~ Sample, data=WBt )

## 
##  F test to compare two variances
## 
## data:  ratio_spleen_YBX1YBX1pS102 by Sample
## F = 1.0455, num df = 2, denom df = 2, p-value = 0.9778
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##   0.02680681 40.77316450
## sample estimates:
## ratio of variances 
##           1.045466

the p-value of F test is p=0.5687, greather than alpha=0.05, alternative hypothesis accepted -> thereis no significant difference between the variances of the two data sets

Shapiro-Wilk test

Shapiro-Wilk test is used to test if data has normal distribution

with(WBt, shapiro.test(ratio_spleen_YBX1YBX1pS102 [Sample == 'WT']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_spleen_YBX1YBX1pS102[Sample == "WT"]
## W = 0.91008, p-value = 0.4183

with(WBt, shapiro.test(ratio_spleen_YBX1YBX1pS102 [Sample == 'KO']))

## 
##  Shapiro-Wilk normality test
## 
## data:  ratio_spleen_YBX1YBX1pS102[Sample == "KO"]
## W = 0.96237, p-value = 0.6272

both groups have normal distribution (p-value greater than 0.05)

t-test

unpaired t-test is used to check wheter samples have different means

spleen_test<-t.test(ratio_spleen_YBX1YBX1pS102  ~ Sample , data = WBt, var.equal = TRUE, conf.level = 0.99)
spleen_test

## 
##  Two Sample t-test
## 
## data:  ratio_spleen_YBX1YBX1pS102 by Sample
## t = 2.2636, df = 4, p-value = 0.08633
## alternative hypothesis: true difference in means between group KO and group WT is not equal to 0
## 99 percent confidence interval:
##  -1.285190  3.771161
## sample estimates:
## mean in group KO mean in group WT 
##        2.0261082        0.7831226

p-value of the t-test is 0.03744, which is less than alpha 0.05. the means are different

spl<-ggboxplot(WBt, x = "Sample", y = "ratio_spleen_YBX1YBX1pS102", 
          color = "Sample", palette = c("#00AFBB", "#E7B800"),
        ylab = "normalised band intensity", xlab = "Sample", main='Spleen')+
  theme(plot.title = element_text(hjust = 0.5))+
  theme(legend.position = 'right')
spl

YBX1pS102_pSer102

band na isti visini (YBX1 = YBX1pSer102)

invert values of band and background intenseties

deduct background

calculate ratio of net protein over net loading control (YBOX1 / GAPDH)

calculate ration phosphorilated to whole protein

group treatments by changing names of rows (WT=44, 45, 46, KO= 58, 60, 68)

Liver

F-test

Shapiro-Wilk test

t-test

Kidney

F-test

Shapiro-Wilk test

t-test

Spleen

F-test

Shapiro-Wilk test

t-test

glavni band (YBX1pSer102 nizje od YBX1)

invert values of band and background intenseties

deduct background

calculate ratio of net protein over net loading control (YBOX1 / GAPDH)

calculate ration phosphorilated to whole protein

group treatments by changing names of rows (WT=44, 45, 46, KO= 58, 60, 68)

Liver

F-test

Shapiro-Wilk test

t-test

Kidney

F-test

Shapiro-Wilk test

t-test

Spleen

F-test

Shapiro-Wilk test

t-test