read in pig data

# brain
pig_brain_FDRq0.05 <-
  read.delim("../../results/star/DEGs/LPS_Brain_gene_DEGs_FDRq0.05.txt")
pig_brain_FDRq1.00 <-
  read.delim("../../results/star/DEGs/LPS_Brain_gene_DEGs_FDRq1.00.txt")

# kidney
pig_kidney_FDRq0.05 <-
  read.delim("../../results/star/DEGs/LPS_Kidney_gene_DEGs_FDRq0.05.txt")
pig_kidney_FDRq1.00 <-
  read.delim("../../results/star/DEGs/LPS_Kidney_gene_DEGs_FDRq1.00.txt")

# blood
pig_blood_FDRq0.05 <-
  read.delim("../../results/star/DEGs/LPS_Blood_gene_DEGs_FDRq0.05.txt")
pig_blood_FDRq1.00 <-
  read.delim("../../results/star/DEGs/LPS_Blood_gene_DEGs_FDRq1.00.txt")

shared and unique between brain and kidney

# rename GeneName to gene_name to merge data sets and find intersections
kidneyBrain <- merge(pig_kidney_FDRq1.00, pig_brain_FDRq1.00, by = "gene_name")

shared_gene_name <- intersect(pig_brain_FDRq0.05$gene_name, pig_kidney_FDRq0.05$gene_name)
uniqueToKidney_gene_name <- setdiff(pig_kidney_FDRq0.05$gene_name, pig_brain_FDRq0.05$gene_name)
uniqueToBrain_gene_name <- setdiff(pig_brain_FDRq0.05$gene_name, pig_kidney_FDRq0.05$gene_name)

shared <- kidneyBrain[kidneyBrain$gene_name %in% shared_gene_name, ]
uniqueToBrain <- kidneyBrain[kidneyBrain$gene_name %in% uniqueToBrain_gene_name, ]
uniqueToKidney <- kidneyBrain[kidneyBrain$gene_name %in% uniqueToKidney_gene_name, ]
# Venn diagram to see what is gained and lost between the reanalysis
x = list(brain = pig_brain_FDRq0.05$gene_name, kidney = pig_kidney_FDRq0.05$gene_name)
ggvenn(
  x,
  fill_color = c("#EFC000FF", "blue"),
  stroke_size = 2,
  set_name_size = 6
)

correlation kidney & brain

shared DEGs

# Shapiro-Wilk normality test 
shapiro.test(shared$logFC.x)
## 
##  Shapiro-Wilk normality test
## 
## data:  shared$logFC.x
## W = 0.9411, p-value = 1.315e-10
shapiro.test(shared$logFC.y)
## 
##  Shapiro-Wilk normality test
## 
## data:  shared$logFC.y
## W = 0.94667, p-value = 5.848e-10
# correlation test 
res <- cor.test(shared$logFC.x, shared$logFC.y, 
                    method = "spearman")
p_value <- round(res$p.value, 3)
rho_value <- round(res$estimate, 3) 

p <- ggplot(data = shared, aes(x = logFC.x, y = logFC.y, text = paste(gene_name))) +
   annotate(
    "rect",
    xmin = 0,
    xmax = max(shared$logFC.x),
    ymin = max(shared$logFC.y),
    ymax = 0,
    fill = "lightpink3",
    alpha = .5
  ) +  annotate(
    "rect",
    xmin = 0,
    xmax = min(shared$logFC.x),
    ymin = 0,
    ymax = min(shared$logFC.y),
    fill = "cadetblue3",
    alpha = .5
  ) +
  geom_abline(color = "gray40") +
  geom_point(size = 2) + 
  theme_bw() +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.position = "none") +
  theme(axis.title.x = element_text(size = 10),
        axis.text.x = element_text(size = 10)) +
  theme(axis.title.y = element_text(size = 10),
        axis.text.y = element_text(size = 10)) +
    annotate(
    "text",
    x = -2,
    y = 5,
    label = paste0("rho = ", rho_value,"\n",
"p = ", p_value),
    parse = TRUE,
    color = "gray29",
    size = 5
  ) + labs(
  title = "Shared DEGs between brain and kidney",
  x = "kidney",
  y = "brain"
)  
ggplotly(p)
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## Please use `gather()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

write table

# difference between fold change
shared$logFC_difference <- (shared$logFC.x - shared$logFC.y)
# add direction information. 
# Is the gene down-regulated or up-regulated in both the mouse and pig data sets
shared$direction_value <- shared$logFC.x * shared$logFC.y

df_shared <- shared %>%
  mutate(direction_status = if_else(shared$direction_value < 0, "opposite", "same"))

# now reformat the whole dataframe to be pretty and legible
df_shared <-
  df_shared[, c(1, 14, 18, 32, 36, 38, 40)]
# rename columns
data.table::setnames(
  df_shared,
  old = c('logFC.x', 'adj.P.Val.x', 'logFC.y', 'adj.P.Val.y'),
  new = c(
    'kidney_logFC',
    'kidney_adj.P.Val',
    'brain_logFC',
    'brain_adj.P.Val'
  )
)

write.table(
  df_shared,
  "../../results/star/comparion_correlation/df_shared_kidney_brain.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

unique to kidney DEGs

shapiro.test(uniqueToKidney$logFC.x)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToKidney$logFC.x
## W = 0.95525, p-value < 2.2e-16
shapiro.test(uniqueToKidney$logFC.y)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToKidney$logFC.y
## W = 0.94302, p-value < 2.2e-16
res <- cor.test(uniqueToKidney$logFC.x, uniqueToKidney$logFC.y, 
                    method = "spearman")
p_value <- round(res$p.value, 3)
rho_value <- round(res$estimate, 3) 

p <- ggplot(data = uniqueToKidney, aes(x = logFC.x, y = logFC.y, text = paste(gene_name))) +
     annotate(
    "rect",
    xmin = 0,
    xmax = max(uniqueToKidney$logFC.x),
    ymin = max(uniqueToKidney$logFC.y),
    ymax = 0,
    fill = "lightpink3",
    alpha = .5
  ) +  annotate(
    "rect",
    xmin = 0,
    xmax = min(uniqueToKidney$logFC.x),
    ymin = 0,
    ymax = min(uniqueToKidney$logFC.y),
    fill = "cadetblue3",
    alpha = .5
  ) +
  geom_abline(color = "gray40") +
  geom_point(size = 2) + 
  theme_bw() +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.position = "none") +
  theme(axis.title.x = element_text(size = 10),
        axis.text.x = element_text(size = 10)) +
  theme(axis.title.y = element_text(size = 10),
        axis.text.y = element_text(size = 10)) +
    annotate(
    "text",
    x = -2,
    y = 1.5,
    label = paste0("rho = ", rho_value,"\n",
"p = ", p_value),
    parse = TRUE,
    color = "gray29",
    size = 5
  ) + labs(
  title = "DEGs unique to kidney",
  x = "kidney",
  y = "brain"
) 
ggplotly(p)
uniqueToKidney$logFC_difference <- (uniqueToKidney$logFC.x - uniqueToKidney$logFC.y)
uniqueToKidney$direction_value <- uniqueToKidney$logFC.x * uniqueToKidney$logFC.y

df_uniqueToKidney <- uniqueToKidney %>%
  mutate(direction_status = if_else(uniqueToKidney$direction_value < 0, "opposite", "same"))

df_uniqueToKidney <-
  df_uniqueToKidney[, c(1, 14, 18, 32, 36, 38, 40)]
data.table::setnames(
  df_uniqueToKidney,
  old = c('logFC.x', 'adj.P.Val.x', 'logFC.y', 'adj.P.Val.y'),
  new = c(
    'kidney_logFC',
    'kidney_adj.P.Val',
    'brain_logFC',
    'brain_adj.P.Val'
  )
)

write.table(
  df_uniqueToKidney,
  "../../results/star/comparion_correlation/df_uniqueToKidney_kidney_brain.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

unique to brain DEGs

shapiro.test(uniqueToBrain$logFC.x)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToBrain$logFC.x
## W = 0.95056, p-value = 1.92e-06
shapiro.test(uniqueToBrain$logFC.y)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToBrain$logFC.y
## W = 0.96226, p-value = 3.194e-05
res <- cor.test(uniqueToBrain$logFC.x, uniqueToBrain$logFC.y, 
                    method = "spearman")
p_value <- round(res$p.value, 3)
rho_value <- round(res$estimate, 3) 

p <- ggplot(data = uniqueToBrain, aes(x = logFC.x, y = logFC.y, text = paste(gene_name))) +
    annotate(
    "rect",
    xmin = 0,
    xmax = max(uniqueToBrain$logFC.x),
    ymin = max(uniqueToBrain$logFC.y),
    ymax = 0,
    fill = "lightpink3",
    alpha = .5
  ) +  annotate(
    "rect",
    xmin = 0,
    xmax = min(uniqueToBrain$logFC.x),
    ymin = 0,
    ymax = min(uniqueToBrain$logFC.y),
    fill = "cadetblue3",
    alpha = .5
  ) +
  geom_abline(color = "gray40") +
  geom_point(size = 2) + 
  theme_bw() +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.position = "none") +
  theme(axis.title.x = element_text(size = 10),
        axis.text.x = element_text(size = 10)) +
  theme(axis.title.y = element_text(size = 10),
        axis.text.y = element_text(size = 10)) +
    annotate(
    "text",
    x = -2,
    y = 2,
    label = paste0("rho = ", rho_value,"\n",
"p = ", p_value),
    parse = TRUE,
    color = "gray29",
    size = 5
  ) + labs(
  title = "DEGs unique to brain",
  x = "kidney",
  y = "brain"
) 
ggplotly(p)
uniqueToBrain$logFC_difference <- (uniqueToBrain$logFC.x - uniqueToBrain$logFC.y)
uniqueToBrain$direction_value <- uniqueToBrain$logFC.x * uniqueToBrain$logFC.y

df_uniqueToBrain <- uniqueToBrain %>%
  mutate(direction_status = if_else(uniqueToBrain$direction_value < 0, "opposite", "same"))

df_uniqueToBrain <-
  df_uniqueToBrain[, c(1, 14, 18, 32, 36, 38, 40)]
data.table::setnames(
  df_uniqueToBrain,
  old = c('logFC.x', 'adj.P.Val.x', 'logFC.y', 'adj.P.Val.y'),
  new = c(
    'kidney_logFC',
    'kidney_adj.P.Val',
    'brain_logFC',
    'brain_adj.P.Val'
  )
)

write.table(
  df_uniqueToBrain,
  "../../results/star/comparion_correlation/df_uniqueToBrain_kidney_brain.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

shared and unique between brain and blood

bloodBrain <- merge(pig_blood_FDRq1.00, pig_brain_FDRq1.00, by = "gene_name")

shared_gene_name <- intersect(pig_brain_FDRq0.05$gene_name, pig_blood_FDRq0.05$gene_name)
uniqueToBlood_gene_name <- setdiff(pig_blood_FDRq0.05$gene_name, pig_brain_FDRq0.05$gene_name)
uniqueToBrain_gene_name <- setdiff(pig_brain_FDRq0.05$gene_name, pig_blood_FDRq0.05$gene_name)

shared <- bloodBrain[bloodBrain$gene_name %in% shared_gene_name, ]
uniqueToBrain <- bloodBrain[bloodBrain$gene_name %in% uniqueToBrain_gene_name, ]
uniqueToBlood <- bloodBrain[bloodBrain$gene_name %in% uniqueToBlood_gene_name, ]

x = list(brain = pig_brain_FDRq0.05$gene_name, blood = pig_blood_FDRq0.05$gene_name)
ggvenn(
  x,
  fill_color = c("#EFC000FF", "pink"),
  stroke_size = 2,
  set_name_size = 6
)

correlation

shared DEGs

shared <- shared[!duplicated(shared[,c("gene_name")]),]

shapiro.test(shared$logFC.x)
## 
##  Shapiro-Wilk normality test
## 
## data:  shared$logFC.x
## W = 0.95565, p-value = 3.59e-06
shapiro.test(shared$logFC.y)
## 
##  Shapiro-Wilk normality test
## 
## data:  shared$logFC.y
## W = 0.96513, p-value = 4.194e-05
res <- cor.test(shared$logFC.x, shared$logFC.y, 
                    method = "spearman")
p_value <- round(res$p.value, 3)
rho_value <- round(res$estimate, 3) 

p <- ggplot(data = shared, aes(x = logFC.x, y = logFC.y, text = paste(gene_name))) +
   annotate(
    "rect",
    xmin = 0,
    xmax = max(shared$logFC.x),
    ymin = max(shared$logFC.y),
    ymax = 0,
    fill = "lightpink3",
    alpha = .5
  ) +  annotate(
    "rect",
    xmin = 0,
    xmax = min(shared$logFC.x),
    ymin = 0,
    ymax = min(shared$logFC.y),
    fill = "cadetblue3",
    alpha = .5
  ) +
  geom_abline(color = "gray40") +
  geom_point(size = 2) + 
  theme_bw() +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.position = "none") +
  theme(axis.title.x = element_text(size = 10),
        axis.text.x = element_text(size = 10)) +
  theme(axis.title.y = element_text(size = 10),
        axis.text.y = element_text(size = 10)) +
    annotate(
    "text",
    x = -2,
    y = 5,
    label = paste0("rho = ", rho_value,"\n",
"p = ", p_value),
    parse = TRUE,
    color = "gray29",
    size = 5
  ) + labs(
  title = "Shared DEGs between brain and blood",
  x = "blood",
  y = "brain"
)  
ggplotly(p)
shared$logFC_difference <- (shared$logFC.x - shared$logFC.y)
shared$direction_value <- shared$logFC.x * shared$logFC.y

df_shared <- shared %>%
  mutate(direction_status = if_else(shared$direction_value < 0, "opposite", "same"))

df_shared <-
  df_shared[, c(1, 14, 18, 32, 36, 38, 40)]
data.table::setnames(
  df_shared,
  old = c('logFC.x', 'adj.P.Val.x', 'logFC.y', 'adj.P.Val.y'),
  new = c(
    'blood_logFC',
    'blood_adj.P.Val',
    'brain_logFC',
    'brain_adj.P.Val'
  )
)

write.table(
  df_shared,
  "../../results/star/comparion_correlation/df_shared_blood_brain.txt",
  sep = "\t",
  quote = F,
  row.names = F
)
shared$logFC_difference <- (shared$logFC.x - shared$logFC.y)
shared$direction_value <- shared$logFC.x * shared$logFC.y

df_shared <- shared %>%
  mutate(direction_status = if_else(shared$direction_value < 0, "opposite", "same"))

df_shared <-
  df_shared[, c(1, 14, 18, 32, 36, 38, 40)]
data.table::setnames(
  df_shared,
  old = c('logFC.x', 'adj.P.Val.x', 'logFC.y', 'adj.P.Val.y'),
  new = c(
    'blood_logFC',
    'blood_adj.P.Val',
    'kidney_logFC',
    'kidney_adj.P.Val'
  )
)

write.table(
  df_shared,
  "../../results/star/comparion_correlation/df_shared_blood_kidney.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

unique to blood DEGs

shapiro.test(uniqueToBlood$logFC.x)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToBlood$logFC.x
## W = 0.95296, p-value < 2.2e-16
shapiro.test(uniqueToBlood$logFC.y)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToBlood$logFC.y
## W = 0.96023, p-value < 2.2e-16
res <- cor.test(uniqueToBlood$logFC.x, uniqueToBlood$logFC.y, 
                    method = "spearman")
p_value <- round(res$p.value, 3)
rho_value <- round(res$estimate, 3) 

p <- ggplot(data = uniqueToBlood, aes(x = logFC.x, y = logFC.y, text = paste(gene_name))) +
     annotate(
    "rect",
    xmin = 0,
    xmax = max(uniqueToBlood$logFC.x),
    ymin = max(uniqueToBlood$logFC.y),
    ymax = 0,
    fill = "lightpink3",
    alpha = .5
  ) +  annotate(
    "rect",
    xmin = 0,
    xmax = min(uniqueToBlood$logFC.x),
    ymin = 0,
    ymax = min(uniqueToBlood$logFC.y),
    fill = "cadetblue3",
    alpha = .5
  ) +
  geom_abline(color = "gray40") +
  geom_point(size = 2) + 
  theme_bw() +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.position = "none") +
  theme(axis.title.x = element_text(size = 10),
        axis.text.x = element_text(size = 10)) +
  theme(axis.title.y = element_text(size = 10),
        axis.text.y = element_text(size = 10)) +
    annotate(
    "text",
    x = -5.2,
    y = 1.5,
    label = paste0("rho = ", rho_value,"\n",
"p = ", p_value),
    parse = TRUE,
    color = "gray29",
    size = 5
  ) + labs(
  title = "DEGs unique to blood",
  x = "blood",
  y = "brain"
) 
ggplotly(p)
uniqueToBlood$logFC_difference <- (uniqueToBlood$logFC.x - uniqueToBlood$logFC.y)
uniqueToBlood$direction_value <- uniqueToBlood$logFC.x * uniqueToBlood$logFC.y

df_uniqueToBlood <- uniqueToBlood %>%
  mutate(direction_status = if_else(uniqueToBlood$direction_value < 0, "opposite", "same"))

df_uniqueToBlood <-
  df_uniqueToBlood[, c(1, 14, 18, 32, 36, 38, 40)]
data.table::setnames(
  df_uniqueToBlood,
  old = c('logFC.x', 'adj.P.Val.x', 'logFC.y', 'adj.P.Val.y'),
  new = c(
    'blood_logFC',
    'blood_adj.P.Val',
    'brain_logFC',
    'brain_adj.P.Val'
  )
)

write.table(
  df_uniqueToBlood,
  "../../results/star/comparion_correlation/df_uniqueToBlood_blood_brain.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

unique to brain DEGs

shapiro.test(uniqueToBrain$logFC.x)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToBrain$logFC.x
## W = 0.83232, p-value = 2.745e-15
shapiro.test(uniqueToBrain$logFC.y)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToBrain$logFC.y
## W = 0.9517, p-value = 4.174e-07
res <- cor.test(uniqueToBrain$logFC.x, uniqueToBrain$logFC.y, 
                    method = "spearman")
p_value <- round(res$p.value, 3)
rho_value <- round(res$estimate, 3) 

p <- ggplot(data = uniqueToBrain, aes(x = logFC.x, y = logFC.y, text = paste(gene_name))) +
    annotate(
    "rect",
    xmin = 0,
    xmax = max(uniqueToBrain$logFC.x),
    ymin = max(uniqueToBrain$logFC.y),
    ymax = 0,
    fill = "lightpink3",
    alpha = .5
  ) +  annotate(
    "rect",
    xmin = 0,
    xmax = min(uniqueToBrain$logFC.x),
    ymin = 0,
    ymax = min(uniqueToBrain$logFC.y),
    fill = "cadetblue3",
    alpha = .5
  ) +
  geom_abline(color = "gray40") +
  geom_point(size = 2) + 
  theme_bw() +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.position = "none") +
  theme(axis.title.x = element_text(size = 10),
        axis.text.x = element_text(size = 10)) +
  theme(axis.title.y = element_text(size = 10),
        axis.text.y = element_text(size = 10)) +
    annotate(
    "text",
    x = -6,
    y = 2,
    label = paste0("rho = ", rho_value,"\n",
"p = ", p_value),
    parse = TRUE,
    color = "gray29",
    size = 5
  ) + labs(
  title = "DEGs unique to brain",
  x = "blood",
  y = "brain"
) 
ggplotly(p)
uniqueToBrain$logFC_difference <- (uniqueToBrain$logFC.x - uniqueToBrain$logFC.y)
uniqueToBrain$direction_value <- uniqueToBrain$logFC.x * uniqueToBrain$logFC.y

df_uniqueToBrain <- uniqueToBrain %>%
  mutate(direction_status = if_else(uniqueToBrain$direction_value < 0, "opposite", "same"))

df_uniqueToBrain <-
  df_uniqueToBrain[, c(1, 14, 18, 32, 36, 38, 40)]
data.table::setnames(
  df_uniqueToBrain,
  old = c('logFC.x', 'adj.P.Val.x', 'logFC.y', 'adj.P.Val.y'),
  new = c(
    'blood_logFC',
    'blood_adj.P.Val',
    'brain_logFC',
    'brain_adj.P.Val'
  )
)

write.table(
  df_uniqueToBrain,
  "../../results/star/comparion_correlation/df_uniqueToBrain_blood_brain.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

shared and unique between kidney and blood

bloodKidney <- merge(pig_blood_FDRq1.00, pig_kidney_FDRq1.00, by = "gene_name")

shared_gene_name <- intersect(pig_kidney_FDRq0.05$gene_name, pig_blood_FDRq0.05$gene_name)
uniqueToBlood_gene_name <- setdiff(pig_blood_FDRq0.05$gene_name, pig_kidney_FDRq0.05$gene_name)
uniqueToKidney_gene_name <- setdiff(pig_kidney_FDRq0.05$gene_name, pig_blood_FDRq0.05$gene_name)

shared <- bloodKidney[bloodKidney$gene_name %in% shared_gene_name, ]
uniqueToKidney <- bloodKidney[bloodKidney$gene_name %in% uniqueToKidney_gene_name, ]
uniqueToBlood <- bloodKidney[bloodKidney$gene_name %in% uniqueToBlood_gene_name, ]

x = list(kidney = pig_kidney_FDRq0.05$gene_name, blood = pig_blood_FDRq0.05$gene_name)
ggvenn(
  x,
  fill_color = c("blue", "pink"),
  stroke_size = 2,
  set_name_size = 6
)

correlation

shared DEGs

shapiro.test(shared$logFC.x)
## 
##  Shapiro-Wilk normality test
## 
## data:  shared$logFC.x
## W = 0.95589, p-value = 5.784e-16
shapiro.test(shared$logFC.y)
## 
##  Shapiro-Wilk normality test
## 
## data:  shared$logFC.y
## W = 0.96131, p-value = 7.594e-15
res <- cor.test(shared$logFC.x, shared$logFC.y, 
                    method = "spearman")
p_value <- round(res$p.value, 3)
rho_value <- round(res$estimate, 3) 

p <- ggplot(data = shared, aes(x = logFC.x, y = logFC.y, text = paste(gene_name))) +
   annotate(
    "rect",
    xmin = 0,
    xmax = max(shared$logFC.x),
    ymin = max(shared$logFC.y),
    ymax = 0,
    fill = "lightpink3",
    alpha = .5
  ) +  annotate(
    "rect",
    xmin = 0,
    xmax = min(shared$logFC.x),
    ymin = 0,
    ymax = min(shared$logFC.y),
    fill = "cadetblue3",
    alpha = .5
  ) +
  geom_abline(color = "gray40") +
  geom_point(size = 2) + 
  theme_bw() +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.position = "none") +
  theme(axis.title.x = element_text(size = 10),
        axis.text.x = element_text(size = 10)) +
  theme(axis.title.y = element_text(size = 10),
        axis.text.y = element_text(size = 10)) +
    annotate(
    "text",
    x = -2,
    y = 5,
    label = paste0("rho = ", rho_value,"\n",
"p = ", p_value),
    parse = TRUE,
    color = "gray29",
    size = 5
  ) + labs(
  title = "Shared DEGs between kidney and blood",
  x = "blood",
  y = "kidney"
)  

ggplotly(p)
shared$logFC_difference <- (shared$logFC.x - shared$logFC.y)
shared$direction_value <- shared$logFC.x * shared$logFC.y

df_shared <- shared %>%
  mutate(direction_status = if_else(shared$direction_value < 0, "opposite", "same"))

df_shared <-
  df_shared[, c(1, 14, 18, 32, 36, 38, 40)]
data.table::setnames(
  df_shared,
  old = c('logFC.x', 'adj.P.Val.x', 'logFC.y', 'adj.P.Val.y'),
  new = c(
    'blood_logFC',
    'blood_adj.P.Val',
    'kidney_logFC',
    'kidney_adj.P.Val'
  )
)

write.table(
  df_shared,
  "../../results/star/comparion_correlation/df_shared_blood_kidney.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

unique to blood DEGs

shapiro.test(uniqueToBlood$logFC.x)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToBlood$logFC.x
## W = 0.95791, p-value < 2.2e-16
shapiro.test(uniqueToBlood$logFC.y)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToBlood$logFC.y
## W = 0.95666, p-value < 2.2e-16
res <- cor.test(uniqueToBlood$logFC.x, uniqueToBlood$logFC.y, 
                    method = "spearman")
p_value <- round(res$p.value, 3)
rho_value <- round(res$estimate, 3) 

p <- ggplot(data = uniqueToBlood, aes(x = logFC.x, y = logFC.y, text = paste(gene_name))) +
     annotate(
    "rect",
    xmin = 0,
    xmax = max(uniqueToBlood$logFC.x),
    ymin = max(uniqueToBlood$logFC.y),
    ymax = 0,
    fill = "lightpink3",
    alpha = .5
  ) +  annotate(
    "rect",
    xmin = 0,
    xmax = min(uniqueToBlood$logFC.x),
    ymin = 0,
    ymax = min(uniqueToBlood$logFC.y),
    fill = "cadetblue3",
    alpha = .5
  ) +
  geom_abline(color = "gray40") +
  geom_point(size = 2) + 
  theme_bw() +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.position = "none") +
  theme(axis.title.x = element_text(size = 10),
        axis.text.x = element_text(size = 10)) +
  theme(axis.title.y = element_text(size = 10),
        axis.text.y = element_text(size = 10)) +
    annotate(
    "text",
    x = -2,
    y = 1.5,
    label = paste0("rho = ", rho_value,"\n",
"p = ", p_value),
    parse = TRUE,
    color = "gray29",
    size = 5
  ) + labs(
  title = "DEGs unique to blood",
  x = "blood",
  y = "kidney"
) 
  
ggplotly(p)
uniqueToBlood$logFC_difference <- (uniqueToBlood$logFC.x - uniqueToBlood$logFC.y)
uniqueToBlood$direction_value <- uniqueToBlood$logFC.x * uniqueToBlood$logFC.y

df_uniqueToBlood <- uniqueToBlood %>%
  mutate(direction_status = if_else(uniqueToBlood$direction_value < 0, "opposite", "same"))

df_uniqueToBlood <-
  df_uniqueToBlood[, c(1, 14, 18, 32, 36, 38, 40)]
data.table::setnames(
  df_uniqueToBlood,
  old = c('logFC.x', 'adj.P.Val.x', 'logFC.y', 'adj.P.Val.y'),
  new = c(
    'blood_logFC',
    'blood_adj.P.Val',
    'kidney_logFC',
    'kidney_adj.P.Val'
  )
)

write.table(
  df_uniqueToBlood,
  "../../results/star/comparion_correlation/df_uniqueToBlood_blood_kidney.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

unique to kidney DEGs

shapiro.test(uniqueToKidney$logFC.x)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToKidney$logFC.x
## W = 0.83249, p-value < 2.2e-16
shapiro.test(uniqueToKidney$logFC.y)
## 
##  Shapiro-Wilk normality test
## 
## data:  uniqueToKidney$logFC.y
## W = 0.95856, p-value < 2.2e-16
res <- cor.test(uniqueToKidney$logFC.x, uniqueToKidney$logFC.y, 
                    method = "spearman")
p_value <- round(res$p.value, 3)
rho_value <- round(res$estimate, 3) 

p <- ggplot(data = uniqueToKidney, aes(x = logFC.x, y = logFC.y, text = paste(gene_name))) +
    annotate(
    "rect",
    xmin = 0,
    xmax = max(uniqueToKidney$logFC.x),
    ymin = max(uniqueToKidney$logFC.y),
    ymax = 0,
    fill = "lightpink3",
    alpha = .5
  ) +  annotate(
    "rect",
    xmin = 0,
    xmax = min(uniqueToKidney$logFC.x),
    ymin = 0,
    ymax = min(uniqueToKidney$logFC.y),
    fill = "cadetblue3",
    alpha = .5
  ) +
  geom_abline(color = "gray40") +
  geom_point(size = 2) + 
  theme_bw() +
  theme(plot.title = element_text(size = 10)) +
  theme(legend.position = "none") +
  theme(axis.title.x = element_text(size = 10),
        axis.text.x = element_text(size = 10)) +
  theme(axis.title.y = element_text(size = 10),
        axis.text.y = element_text(size = 10)) +
    annotate(
    "text",
    x = -5,
    y = 5,
    label = paste0("rho = ", rho_value,"\n",
"p = ", p_value),
    parse = TRUE,
    color = "gray29",
    size = 5
  ) + labs(
  title = "DEGs unique to kidney",
  x = "blood",
  y = "kidney"
) 
ggplotly(p)
uniqueToKidney$logFC_difference <- (uniqueToKidney$logFC.x - uniqueToKidney$logFC.y)
uniqueToKidney$direction_value <- uniqueToKidney$logFC.x * uniqueToKidney$logFC.y

df_uniqueToKidney <- uniqueToKidney %>%
  mutate(direction_status = if_else(uniqueToKidney$direction_value < 0, "opposite", "same"))

df_uniqueToKidney <-
  df_uniqueToKidney[, c(1, 14, 18, 32, 36, 38, 40)]
data.table::setnames(
  df_uniqueToKidney,
  old = c('logFC.x', 'adj.P.Val.x', 'logFC.y', 'adj.P.Val.y'),
  new = c(
    'blood_logFC',
    'blood_adj.P.Val',
    'kidney_logFC',
    'kidney_adj.P.Val'
  )
)

write.table(
  df_uniqueToKidney,
  "../../results/star/comparion_correlation/df_uniqueToKidney_blood_kidney.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

Uniquely expressed and DEGs within a tissue

sharedWithBrain <- c(kidneyBrain$gene_name, bloodBrain$gene_name)
UniqueToBrain_DE_exprssion <-
  setdiff(pig_brain_FDRq0.05$gene_name, sharedWithBrain)
df_UniqueToBrain_DE_exprssion <- subset(pig_brain_FDRq0.05, gene_name %in% UniqueToBrain_DE_exprssion)
write.table(
  df_UniqueToBrain_DE_exprssion,
  "../../results/star/comparion_correlation/UniqueToBrain_DE_exprssion.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

sharedWithKidney <- c(kidneyBrain$gene_name, bloodKidney$gene_name)
UniqueToKidney_DE_exprssion <-
  setdiff(pig_kidney_FDRq0.05$gene_name, sharedWithKidney)
df_UniqueToKidney_DE_exprssion <- subset(pig_kidney_FDRq0.05, gene_name %in% UniqueToKidney_DE_exprssion)
write.table(
  df_UniqueToKidney_DE_exprssion,
  "../../results/star/comparion_correlation/UniqueToKidney_DE_exprssion.txt",
  sep = "\t",
  quote = F,
  row.names = F
)

sharedWithBlood <- c(bloodKidney$gene_name, bloodBrain$gene_name)
UniqueToBlood_DE_exprssion <-
  setdiff(pig_blood_FDRq0.05$gene_name, sharedWithBlood)
df_UniqueToBlood_DE_exprssion <- subset(pig_blood_FDRq0.05, gene_name %in% UniqueToBlood_DE_exprssion)
write.table(
  df_UniqueToBlood_DE_exprssion,
  "../../results/star/comparion_correlation/UniqueToBlood_DE_exprssion.txt",
  sep = "\t",
  quote = F,
  row.names = F
)