library(tidyverse)
raw_rwd <- read_csv("https://denvirlab.marshall.edu/BMR617-2023/data/RawRenalWesternData.csv")
raw_rwd <- raw_rwd %>% mutate(NormSGK1 = SGK1/`Beta actin`)
t.test(NormSGK1 ~ TissueType, paired=T, data=raw_rwd)
ggplot(raw_rwd, aes(x=TissueType, y=NormSGK1)) + geom_point() + geom_line(aes(group=Patient))

# Another way to normalize to loading controls:
refBetaActin <- raw_rwd[1, "Beta actin"] # Beta actin value from first row
raw_rwd <- raw_rwd %>% mutate(NormalizationFactor = `Beta actin`/refBetaActin)
raw_rwd <- raw_rwd %>% mutate(SGK1_Norm_By_Factor = SGK1/NormalizationFactor)
t.test(SGK1_Norm_By_Factor ~ TissueType, paired=T, data=raw_rwd)
ggplot(raw_rwd, aes(x=TissueType, y=SGK1_Norm_By_Factor)) + geom_point() + geom_line(aes(group=Patient))

meanNormalSGKNorm <- raw_rwd %>% 
  group_by(TissueType) %>% 
  summarise(MeanNormSGK1 = mean(NormSGK1)) %>% # Get the mean normalized SGK1 for each tissue type
  filter(TissueType == "Normal") %>% # Get only the normal tissue
  pull(MeanNormSGK1) # Extract the mean normalized SGK1 quantification

raw_rwd <- raw_rwd %>% mutate(RelativeSGK1 = NormSGK1 / meanNormalSGKNorm)
raw_rwd %>% group_by(TissueType) %>% summarise(MeanRelativeSGK1 = mean(RelativeSGK1))

t.test(RelativeSGK1 ~ TissueType, paired=T, data=raw_rwd)
ggplot(raw_rwd, aes(x=TissueType, y=RelativeSGK1)) + 
  geom_point() + geom_line(aes(group=Patient)) + 
  ylab("Relative SGK1 Expresison")