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, replacing melt() from reshape2
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 and liver data
Serum <- read_excel("Data/Data.xlsx", sheet = "Nec_serum_targeted")
Liver <- read_excel("Data/Data.xlsx", sheet = "Liver_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
# Define the list of metabolites for each sample type
ls.serum.metabolites <- c("3-Hydroxybutyrate", "Acetoacetate")
ls.liver.metabolites <- c("3-Hydroxybutyrate")
Data.serum <- inner_join(Serum, PigInfo, by = "PigID") %>%
select(Diet, all_of(ls.serum.metabolites)) %>%
mutate(Region = "Serum") %>%
pivot_longer(cols = all_of(ls.serum.metabolites), names_to = "variable", values_to = "value")
Data.liver <- inner_join(Liver, PigInfo, by = "PigID") %>%
select(Diet, all_of(ls.liver.metabolites)) %>%
mutate(Region = "Liver") %>%
pivot_longer(cols = all_of(ls.liver.metabolites), names_to = "variable", values_to = "value")
# Combine serum and liver data into a single dataset
All.data <- bind_rows(Data.serum, Data.liver) %>%
mutate(Region = factor(Region, levels = c("Serum", "Liver")))
Plotting data
Fig <- All.data %>%
mutate(Diet = factor(Diet, levels = c("ALAC", "WPI", "SF"))) %>%
ggerrorplot(x = "variable", y = "value", color = "Diet",
palette = c("turquoise3", "purple", "orange"),
na.rm = TRUE,
xlab = "",
ylab = "Concentration",
add = "mean_se",
size = 0.6,
position = position_dodge(0.7)) +
facet_nested(~Region + variable, scales = "free", independent = "y") + # Free x axis with proportional facet widths using facet_nested
theme_classic2() +
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank())
Fig
# Save the figure in pdf format:
ggsave(plot=Fig, height=4, width=6, dpi=300, filename="Figure 5/Fig5B.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") %>%
select(`3-Hydroxybutyrate`, `Acetoacetate`, Diet, SowID, Formula.consumed.g, Time.postprandial.min) %>%
pivot_longer(cols = c(`3-Hydroxybutyrate`, `Acetoacetate`), names_to = "variable", values_to = "value") %>%
filter(Diet %in% c("ALAC", "WPI")) %>%
group_by(variable) %>%
mutate(glog.value = glog(value * 10)) %>% # Apply generalized log transformation to metabolite values
anova_test(glog.value ~ Diet + SowID + Formula.consumed.g + Time.postprandial.min, type = 1) %>% # Perform ANOVA
add_significance(p.col = "p", cutpoints = c(0, 0.05, 0.1, 1), symbols = c("*", "#", "ns")) %>% # Add significance markers
as_tibble() %>%
filter(Effect == "Diet") %>%
kable() %>% # Print the tibble in a nicely formatted table
kable_styling(bootstrap_options = c("striped", "hover")) # Enhance table formatting with kableExtra
|
variable
|
Effect
|
DFn
|
DFd
|
F
|
p
|
p<.05
|
ges
|
p.signif
|
|
3-Hydroxybutyrate
|
Diet
|
1
|
17
|
16.933
|
0.000723
|
|
0.499
|
|
|
Acetoacetate
|
Diet
|
1
|
17
|
3.073
|
0.098000
|
|
0.153
|
#
|
Liver %>% inner_join(PigInfo, by = "PigID") %>%
inner_join(Nec_metadata, by = "PigID") %>%
filter(Diet %in% c("ALAC", "WPI")) %>%
mutate(glog.3HB = glog(`3-Hydroxybutyrate` * 10)) %>% # glog transformation
anova_test(glog.3HB ~ 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 8.665 9.00e-03 * 0.338
## 2 SowID 2 17 17.317 7.92e-05 * 0.671
## 3 Formula.consumed.g 1 17 2.581 1.27e-01 0.132
## 4 Time.postprandial.min 1 17 1.582 2.26e-01 0.085
Between formula-fed groups and SF, adjusting for sow ID
Serum %>% inner_join(PigInfo, by = "PigID") %>%
inner_join(Nec_metadata, by = "PigID") %>%
select(`3-Hydroxybutyrate`, `Acetoacetate`, Diet, SowID) %>%
pivot_longer(cols = c(`3-Hydroxybutyrate`, `Acetoacetate`), names_to = "variable", values_to = "value") %>%
group_by(variable) %>%
mutate(glog.value = glog(value * 10)) %>% # Apply generalized log transformation to metabolite values
tukey_hsd(glog.value ~ 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")) %>%
kable() %>% # Print the tibble in a nice table format
kable_styling(bootstrap_options = c("striped", "hover")) # Enhance table formatting
|
variable
|
term
|
group1
|
group2
|
null.value
|
estimate
|
conf.low
|
conf.high
|
p.adj
|
p.adj.signif
|
|
3-Hydroxybutyrate
|
Diet
|
ALAC
|
SF
|
0
|
-1.4876968
|
-2.2638967
|
-0.7114968
|
0.000204
|
|
|
3-Hydroxybutyrate
|
Diet
|
ALAC
|
WPI
|
0
|
-0.7500827
|
-1.3884903
|
-0.1116751
|
0.019100
|
|
|
3-Hydroxybutyrate
|
Diet
|
SF
|
WPI
|
0
|
0.7376140
|
-0.0270860
|
1.5023141
|
0.060100
|
#
|
|
Acetoacetate
|
Diet
|
ALAC
|
SF
|
0
|
-1.1346856
|
-1.8158411
|
-0.4535301
|
0.000989
|
|
|
Acetoacetate
|
Diet
|
ALAC
|
WPI
|
0
|
-0.3770512
|
-0.9372868
|
0.1831845
|
0.233000
|
ns
|
|
Acetoacetate
|
Diet
|
SF
|
WPI
|
0
|
0.7576344
|
0.0865706
|
1.4286982
|
0.024800
|
|
Liver %>% inner_join(PigInfo, by = "PigID") %>%
inner_join(Nec_metadata, by = "PigID") %>%
select(`3-Hydroxybutyrate`, Diet, SowID) %>%
pivot_longer(cols = c(`3-Hydroxybutyrate`), names_to = "variable", values_to = "value") %>%
group_by(variable) %>%
mutate(glog.value = glog(value * 10)) %>%
tukey_hsd(glog.value ~ Diet + SowID) %>%
filter(term == "Diet") %>%
add_significance(p.col = "p.adj", cutpoints = c(0, 0.05, 0.1, 1), symbols = c("*", "#", "ns")) %>%
kable() %>% # Print the tibble in a nice table format
kable_styling(bootstrap_options = c("striped", "hover")) # Enhance table formatting
|
variable
|
term
|
group1
|
group2
|
null.value
|
estimate
|
conf.low
|
conf.high
|
p.adj
|
p.adj.signif
|
|
3-Hydroxybutyrate
|
Diet
|
ALAC
|
SF
|
0
|
-4.4165417
|
-6.707699
|
-2.125384
|
0.000191
|
|
|
3-Hydroxybutyrate
|
Diet
|
ALAC
|
WPI
|
0
|
-0.5942223
|
-2.478650
|
1.290205
|
0.714000
|
ns
|
|
3-Hydroxybutyrate
|
Diet
|
SF
|
WPI
|
0
|
3.8223194
|
1.565107
|
6.079532
|
0.000832
|
|
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