Clean up the environment
rm(list = ls())
Load library
library(readxl) # For reading Excel files
library(ggpubr) # For creating publication-ready plots
library(dplyr) # For data manipulation
library(tidyr) # For reshaping data
library(ggh4x) # For advanced faceting functions in ggplot2
library(rstatix) # For statistical tests and adding significance markers
library(knitr) # For displaying tibbles in a nice table format
library(kableExtra) # For enhancing table formatting
Load data
# Load targeted serum, liver and brain data
Serum <- read_excel("Data/Data.xlsx", sheet = "Nec_serum_targeted")
Liver <- read_excel("Data/Data.xlsx", sheet = "Liver_targeted")
Brain <- read_excel("Data/Data.xlsx", sheet = "Brain_targeted")
# Load pig information and necropsy metadata
PigInfo <- read_excel("Data/Data.xlsx", sheet = "PigInfo")
Nec_metadata <- read_excel("Data/Data.xlsx", sheet = "Nec_metadata")
Data organization
Data.serum <- inner_join(Serum, PigInfo, by = "PigID") %>%
select(Diet, Serotonin) %>%
mutate(Region = "Serum") %>%
pivot_longer(cols = Serotonin, names_to = "variable", values_to = "value") %>%
mutate(Class = "I")
Data.liver <- inner_join(Liver, PigInfo, by = "PigID") %>%
select(Diet, Serotonin) %>%
mutate(Region = "Liver") %>%
pivot_longer(cols = Serotonin, names_to = "variable", values_to = "value") %>%
mutate(Class = "II")
Data.brain <- inner_join(Brain, PigInfo, by = "PigID") %>%
select(Diet, Region, Serotonin) %>%
pivot_longer(cols = Serotonin, names_to = "variable", values_to = "value") %>%
mutate(Class = "II")
# Combine serum, liver, and brain data
All.data <- bind_rows(Data.serum, Data.liver, Data.brain) %>%
mutate(Region = factor(Region, levels = c("Serum", "Liver", "Hippocampus", "Hypothalamus", "PFC", "Striatum")))
Plotting data for serotonin concentration by diet and sample
type
Fig <- All.data %>%
mutate(Diet = factor(Diet, levels = c("ALAC", "WPI", "SF"))) %>%
ggerrorplot(x = "Region", y = "value", color = "Diet",
palette = c("turquoise3","purple", "orange"),
na.rm = TRUE, xlab = "",
ylab ="Serotonin concentration", add = "mean_se", position = position_dodge(0.7)) +
facet_grid2(.~ Class , scales = "free", independent = "y", space = "free_x") +
theme_classic() +
theme(strip.background = element_blank(), strip.text.x = element_blank())
Fig
# Save the figure in pdf format:
ggsave(plot=Fig, height=4, width=7.5, dpi=300, filename="Figure 5/Fig5C.pdf", useDingbats=FALSE)
Group difference evaluation
#This function transforms the input values by the generalized #log function.
glog <- function(y) {
#Using lambda = 1
yt <- log(y+sqrt(y^2+1))
return(yt)
}
ALAC vs WPI: Serum and liver data, adjusting for intake volume and
postprandial time
Serum %>% inner_join(PigInfo, by = "PigID") %>%
inner_join(Nec_metadata, by = "PigID") %>%
filter(Diet %in% c("ALAC", "WPI")) %>%
mutate(glog.5HT = glog(Serotonin * 10)) %>% # glog transformation
anova_test(glog.5HT ~ Diet + SowID + Formula.consumed.g + Time.postprandial.min, type = 1)
## ANOVA Table (type I tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Diet 1 17 4.599 0.047 * 0.213
## 2 SowID 2 17 2.603 0.103 0.234
## 3 Formula.consumed.g 1 17 1.070 0.315 0.059
## 4 Time.postprandial.min 1 17 0.890 0.359 0.050
Liver %>% inner_join(PigInfo, by = "PigID") %>%
inner_join(Nec_metadata, by = "PigID") %>%
filter(Diet %in% c("ALAC", "WPI")) %>%
mutate(glog.5HT = glog(Serotonin * 10)) %>% # glog transformation
anova_test(glog.5HT ~ Diet + SowID + Formula.consumed.g + Time.postprandial.min, type = 1)
## ANOVA Table (type I tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 Diet 1 17 12.250 0.003 * 0.419
## 2 SowID 2 17 3.989 0.038 * 0.319
## 3 Formula.consumed.g 1 17 3.372 0.084 0.166
## 4 Time.postprandial.min 1 17 3.756 0.069 0.181
Brain data, adjusting for sow ID
Brain %>% inner_join(PigInfo, by = "PigID") %>%
mutate(glog.5HT = glog(Serotonin * 10)) %>% # Applying glog transformation
group_by(Region) %>%
tukey_hsd(glog.5HT ~ Diet + SowID) %>% # Perform Tukey HSD test
filter(term == "Diet") %>%
add_significance(p.col = "p.adj", cutpoints = c(0, 0.05, 0.1, 1), symbols = c("*", "#", "ns")) %>%
filter(p.adj.signif != "ns") %>%
select(Region, group1, group2, p.adj, p.adj.signif) %>%
kable() %>% # Print the tibble in a nicely formatted table
kable_styling(bootstrap_options = c("striped", "hover")) # Enhance table formatting with kableExtra
|
Region
|
group1
|
group2
|
p.adj
|
p.adj.signif
|
|
Hypothalamus
|
ALAC
|
SF
|
0.0785
|
#
|
|
Hypothalamus
|
SF
|
WPI
|
0.0509
|
#
|
|
PFC
|
SF
|
WPI
|
0.0489
|
|
|
Striatum
|
ALAC
|
WPI
|
0.0628
|
#
|
sessionInfo()
## R version 4.2.2 (2022-10-31)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] kableExtra_1.3.4 knitr_1.45 rstatix_0.7.2 ggh4x_0.2.6
## [5] tidyr_1.3.0 dplyr_1.1.3 ggpubr_0.6.0 ggplot2_3.5.1
## [9] readxl_1.4.3
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 xfun_0.40 bslib_0.5.1 purrr_1.0.2
## [5] carData_3.0-5 colorspace_2.1-0 vctrs_0.6.5 generics_0.1.3
## [9] viridisLite_0.4.2 htmltools_0.5.6.1 yaml_2.3.7 utf8_1.2.4
## [13] rlang_1.1.2 jquerylib_0.1.4 pillar_1.9.0 glue_1.6.2
## [17] withr_3.0.1 lifecycle_1.0.4 stringr_1.5.1 munsell_0.5.1
## [21] ggsignif_0.6.4 gtable_0.3.5 cellranger_1.1.0 ragg_1.2.6
## [25] rvest_1.0.3 evaluate_1.0.1 labeling_0.4.3 fastmap_1.1.1
## [29] fansi_1.0.6 highr_0.10 broom_1.0.5 scales_1.3.0
## [33] backports_1.4.1 cachem_1.0.8 webshot_0.5.5 jsonlite_1.8.8
## [37] abind_1.4-5 farver_2.1.1 systemfonts_1.0.5 textshaping_0.3.7
## [41] digest_0.6.33 stringi_1.7.12 grid_4.2.2 cli_3.6.2
## [45] tools_4.2.2 magrittr_2.0.3 sass_0.4.7 tibble_3.2.1
## [49] car_3.1-2 pkgconfig_2.0.3 xml2_1.3.5 rmarkdown_2.28
## [53] svglite_2.1.2 httr_1.4.7 rstudioapi_0.15.0 R6_2.5.1
## [57] compiler_4.2.2