R Notebook

library(tidyverse)
CD <- "Nina-Feces Pool-CD.xlsx" %>% readxl::read_xlsx(1)
TF <- "Stool3_Pool_Data-TF.csv" %>% read.csv() %>% t() %>% data.frame()
colnames(TF) <- TF[1,]
TF <- TF[2:nrow(TF),]

colnames(CD) <- CD %>% colnames() %>% str_remove("Area: ") %>% str_remove(".raw.*")

Number of equal names(naive) metabolites

intersect(CD$Name, TF %>% rownames()) %>% length()

[1] 37

long_CD <- 
CD %>% pivot_longer(cols = 6:ncol(CD), names_to = "sample", values_to = "intensityCD")
long_CD$samplename <- paste0(long_CD$sample, "_", long_CD$Name)

long_TF <- 
  TF %>% rownames_to_column("Name") %>% 
  pivot_longer(cols = 2:ncol(TF), names_to = "sample", values_to = "intensityTF")
long_TF$samplename <- paste0(long_TF$sample, "_", long_TF$Name)
long_TF$intensityTF <- long_TF$intensityTF %>% as.numeric()

big_table <- 
  inner_join(long_CD, long_TF, by = "samplename")

Big picture correlation

big_table %>% ggplot(aes(x = intensityCD, y = intensityTF)) +
  geom_point()

Corelation by sample

big_table %>% group_by(sample.x) %>% 
  summarise(R=cor(intensityCD, intensityTF))%>% 
  ggplot(aes(sample.x, R)) + 
  geom_col() + 
  theme_classic()

Corelation by Metabolites

big_table %>% group_by(Name.x) %>% 
  summarise(R=cor(intensityCD, intensityTF)) %>% 
  ggplot(aes(Name.x, R)) + 
  geom_col() + 
  theme_classic() + 
  theme(axis.text.x = element_text(angle = -45, hjust = 0))

big_table %>% 
  filter(intensityCD > 8e7) %>% 
  filter(intensityTF > 1e7) %>% 
  ggplot(aes(x = intensityCD, y = intensityTF)) +
  geom_point()

big_table %>% group_by(sample.x) %>% 
  filter(intensityCD > 8e7) %>% 
  filter(intensityTF > 1e7) %>% 
  summarise(R=cor(intensityCD, intensityTF))%>% 
  ggplot(aes(sample.x, R)) + 
  geom_col() + 
  theme_classic()

big_table %>% group_by(Name.x) %>% 
    filter(intensityCD > 8e7) %>% 
  filter(intensityTF > 1e7) %>% 
  summarise(R=cor(intensityCD, intensityTF), medianCD=median(intensityCD), medianTF=median(intensityTF)) %>% 
  pivot_longer(cols = c(medianCD, medianTF), names_to = 'metric', values_to = "val") %>% 
  ggplot(aes(Name.x, val, fill = metric, alpha = R)) + 
  geom_col(position = "dodge") +
  # theme_classic() + 
  theme(axis.text.x = element_text(angle = -45, hjust = 0))