Data organization
# Define the list of metabolites for each tissue type
ls.serum.metabolites <- c("Tryptophan", "Kynurenine", "Quinolinate", "Serotonin")
ls.liver.metabolites <- c("Tryptophan", "Kynurenine", "NAD", "NAD+NADP","Serotonin")
ls.brain.metabolites <- c("Tryptophan", "Serotonin")
ls.urine.metabolites <- c("Tryptophan", "Quinolinate", "1-MN", "3-IS", "2PY")
Data.serum <- inner_join(Serum, PigInfo, by = "PigID") %>%
select(Diet, all_of(ls.serum.metabolites)) %>%
mutate(Region = "Serum") %>%
pivot_longer(cols = -c(Diet, Region), names_to = "variable", values_to = "value") %>%
mutate(Class = "I")
Data.liver <- inner_join(Liver, PigInfo, by = "PigID") %>%
mutate(`NAD+NADP` = `NAD+` + `NADP+`) %>%
rename(`NAD` = "NAD+") %>% # fix name
select(Diet, all_of(ls.liver.metabolites)) %>%
mutate(Region = "Liver") %>%
pivot_longer(cols = -c(Diet, Region), names_to = "variable", values_to = "value") %>%
mutate(Class = "II")
Data.brain <- inner_join(Brain, PigInfo, by = "PigID") %>%
select(Diet, Region, all_of(ls.brain.metabolites)) %>%
pivot_longer(cols = -c(Diet, Region), names_to = "variable", values_to = "value") %>%
mutate(Class = "II")
#Calculate effective osmolality
Effective.Osmolality <- Urine$`Urine osmolality` - (Urine$Urea/1000)
Data.urine <- Urine %>%
rename(`1-MN` = "1-Methylnicotinamide") %>% # fix name
rename(`2PY` = "N-Methyl-2-pyridone-5-carboxamide") %>% # fix name
rename(`3-IS` = "3-Indoxylsulfate") %>% # fix name
mutate(across(`1-MN`:`β-Pseudouridine`, ~ .x / Effective.Osmolality)) %>% # Osmolality adjustment
inner_join(PigInfo, by = "PigID") %>%
select(Diet, all_of(ls.urine.metabolites)) %>%
mutate(Region = "Urine") %>%
pivot_longer(cols = -c(Diet, Region), names_to = "variable", values_to = "value") %>%
mutate(Class = "III")
All.data <- bind_rows(Data.serum, Data.liver, Data.brain, Data.urine) %>%
mutate(Region = factor(Region, levels = c("Serum", "Liver", "Hippocampus", "Hypothalamus", "PFC", "Striatum", "Urine")))
Fig <- All.data %>%
filter(variable %in% c("Kynurenine", "Quinolinate", "NAD+", "1-MN", "3-IS", "2PY", "NAD", "NAD+NADP")) %>%
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:
ggsave(plot=Fig, height=4, width=13, dpi=300, filename="Figure 5/Fig5A.pdf", useDingbats=FALSE)
Evaluate group differences
#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(Kynurenine, Quinolinate, Diet, SowID, Formula.consumed.g, Time.postprandial.min) %>%
pivot_longer(cols = -c(Diet, SowID, Formula.consumed.g, Time.postprandial.min), names_to = "variable", values_to = "value") %>%
filter(Diet %in% c("ALAC", "WPI")) %>%
group_by(variable) %>%
mutate(glog.value = glog(value*10)) %>%
anova_test(glog.value ~ Diet + SowID + Formula.consumed.g + Time.postprandial.min, type = 1) %>%
as_tibble() %>%
filter(Effect == "Diet") %>%
kable() %>% # Print the tibble in a nice table format
kable_styling(bootstrap_options = c("striped", "hover")) # Enhance table formatting
|
variable
|
Effect
|
DFn
|
DFd
|
F
|
p
|
p<.05
|
ges
|
|
Kynurenine
|
Diet
|
1
|
17
|
2.946
|
0.104
|
|
0.148
|
|
Quinolinate
|
Diet
|
1
|
17
|
6.247
|
0.023
|
|
0.269
|
Liver %>% mutate(`NAD+NADP` = `NAD+` + `NADP+`) %>%
inner_join(PigInfo, by = "PigID") %>%
inner_join(Nec_metadata, by = "PigID") %>%
select(Kynurenine, `NAD+`, `NAD+NADP`, Diet, SowID, Formula.consumed.g, Time.postprandial.min) %>%
pivot_longer(cols = -c(Diet, SowID, Formula.consumed.g, Time.postprandial.min), names_to = "variable", values_to = "value") %>%
filter(Diet %in% c("ALAC", "WPI")) %>%
group_by(variable) %>%
mutate(glog.value = glog(value*10)) %>%
anova_test(glog.value ~ Diet + SowID + Formula.consumed.g + Time.postprandial.min, type = 1) %>%
as_tibble() %>%
filter(Effect == "Diet") %>%
kable() %>% # Print the tibble in a nice table format
kable_styling(bootstrap_options = c("striped", "hover")) # Enhance table formatting
|
variable
|
Effect
|
DFn
|
DFd
|
F
|
p
|
p<.05
|
ges
|
|
Kynurenine
|
Diet
|
1
|
17
|
1.843
|
0.192
|
|
0.098
|
|
NAD+
|
Diet
|
1
|
17
|
5.673
|
0.029
|
|
0.250
|
|
NAD+NADP
|
Diet
|
1
|
17
|
5.020
|
0.039
|
|
0.228
|
Urine data, adjusting for sow ID
Urine %>% mutate(across(`1-Methylnicotinamide`:`β-Pseudouridine`, ~ .x / Effective.Osmolality)) %>% # osmolality adjustment
inner_join(PigInfo, by = "PigID") %>%
select(Quinolinate, `1-Methylnicotinamide`, `3-Indoxylsulfate`, `N-Methyl-2-pyridone-5-carboxamide`, Diet, SowID) %>%
pivot_longer(cols = -c(Diet, SowID), 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")) %>%
filter(p.adj.signif != "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
|
|
1-Methylnicotinamide
|
Diet
|
ALAC
|
SF
|
0
|
-1.3392238
|
-2.153914
|
-0.5245339
|
0.00135
|
|
|
1-Methylnicotinamide
|
Diet
|
ALAC
|
WPI
|
0
|
-0.9084302
|
-1.618660
|
-0.1982001
|
0.01100
|
|
|
3-Indoxylsulfate
|
Diet
|
ALAC
|
SF
|
0
|
-1.1858386
|
-2.206152
|
-0.1655252
|
0.02110
|
|
|
3-Indoxylsulfate
|
Diet
|
ALAC
|
WPI
|
0
|
-0.7875184
|
-1.677007
|
0.1019702
|
0.08860
|
#
|
|
N-Methyl-2-pyridone-5-carboxamide
|
Diet
|
ALAC
|
SF
|
0
|
-1.1293781
|
-1.704696
|
-0.5540603
|
0.00021
|
|
|
N-Methyl-2-pyridone-5-carboxamide
|
Diet
|
ALAC
|
WPI
|
0
|
-0.8046146
|
-1.306165
|
-0.3030642
|
0.00170
|
|
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