filaminA blot

library(ggplot2)

wt<-(c(-0.735745183,
0.470618359,
0.028640459,
-0.106965867,
-0.011203433,
-0.091575168
))

wt1<-(c(-0.045276,-0.045276,
0.028640459,
-0.106965867,
-0.011203433,
-0.091575168
))

ko<-(c(-0.021604198,
0.041376732,
0.09211971,
-0.09787147,
-0.038354481,
0.059974628
))

value<-(c(ko,wt))
treatment<-c(rep('ko', 6), rep('wt',6))


liver<-data.frame(treatment,value)

t.test(wt,ko,
       conf.level = 0.01
       )

## 
##  Welch Two Sample t-test
## 
## data:  wt and ko
## t = -0.49952, df = 5.3341, p-value = 0.6373
## alternative hypothesis: true difference in means is not equal to 0
## 1 percent confidence interval:
##  -0.08242322 -0.07820070
## sample estimates:
##    mean of x    mean of y 
## -0.074371806  0.005940153

ggplot(liver, aes(x=treatment, y=value, fill=treatment)) + 
    geom_boxplot(alpha=0.3) +
    theme(legend.position="none") +
    theme_classic()+
    scale_fill_brewer(palette="Dark2")+
    labs(title = 'liver')+
    theme(plot.title = element_text(hjust = 0.5))

value<-(c(ko,wt1))
treatment<-c(rep('ko', 6), rep('wt1',6))


liver<-data.frame(treatment,value)

t.test(wt1,ko,
       conf.level = 0.01
       )

## 
##  Welch Two Sample t-test
## 
## data:  wt1 and ko
## t = -1.4453, df = 9.0046, p-value = 0.1823
## alternative hypothesis: true difference in means is not equal to 0
## 1 percent confidence interval:
##  -0.05167279 -0.05075952
## sample estimates:
##    mean of x    mean of y 
## -0.045276002  0.005940153

wt<-(c(1.631455159,
0.980223714,
0.506393967,
1.16656634,
0.841894811,
0.769316319

))


ko<-(c(0.419496795,
0.371012029,
0.412084424,
1.204285926,
-1.246416915,
2.587630632

))

value<-(c(ko,wt))
treatment<-c(rep('ko', 6), rep('wt',6))


kidney<-data.frame(treatment,value)

t.test(wt,ko,
       conf.level = 0.01
       )

## 
##  Welch Two Sample t-test
## 
## data:  wt and ko
## t = 0.66901, df = 5.9439, p-value = 0.5286
## alternative hypothesis: true difference in means is not equal to 0
## 1 percent confidence interval:
##  0.3509666 0.3649525
## sample estimates:
## mean of x mean of y 
## 0.9826417 0.6246821

ggplot(kidney, aes(x=treatment, y=value, fill=treatment)) + 
    geom_boxplot(alpha=0.3) +
    theme(legend.position="none") +
    theme_classic()+
    scale_fill_brewer(palette="Dark2")+
    labs(title = 'kidney')+
    theme(plot.title = element_text(hjust = 0.5))