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3.3 bulkRNAseq.additional_figures.Rmd
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3.3 bulkRNAseq.additional_figures.Rmd
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---
title: "Additional Figures"
author: '[Sander W. van der Laan, PhD](https://vanderlaan.science) | s.w.vanderlaan@gmail.com.'
date: '`r Sys.Date()`'
output:
html_notebook:
cache: yes
code_folding: hide
collapse: yes
df_print: paged
fig.align: center
fig_caption: yes
fig_height: 10
fig_retina: 2
fig_width: 12
theme: paper
toc: yes
toc_float:
collapsed: no
smooth_scroll: yes
highlight: tango
mainfont: Helvetica
subtitle: Accompanying 'Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy, an analysis in the Athero-EXPRESS Biobank.'
editor_options:
chunk_output_type: inline
bibliography: references.bib
knit: worcs::cite_all
---
# General Setup
```{r setup, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
warning = TRUE, # show warnings during codebook generation
message = TRUE, # show messages during codebook generation
error = TRUE, # do not interrupt codebook generation in case of errors,
# usually better for debugging
echo = TRUE, # show R code
eval = TRUE)
ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")
```
```{r echo = FALSE}
rm(list = ls())
```
```{r LocalSystem, echo = FALSE}
source("scripts/local.system.R")
```
```{r Source functions}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```
```{r Setting: loading_packages, message=FALSE, warning=FALSE}
source(paste0(PROJECT_loc, "/scripts/pack01.packages.R"))
```
```{r Setting: Colors, include = FALSE}
source(paste0(PROJECT_loc, "/scripts/colors.R"))
```
# Background
This notebook contains additional figures of the project "Plaque expression levels of _HDAC9_ in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy: an analysis in the Athero-EXPRESS Biobank.".
# Loading data
```{r Loading project data}
# load(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
load(paste0(PROJECT_loc, "/20220319.",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
```
```{r}
source("/Users/slaan3/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9/scripts/local.system.R")
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
```
# Fix some variables
We need to get the 'conventional unit' versions of cholesterols.
```{r}
AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9,
subset(AEDB.CEA, select = c("STUDY_NUMBER",
"risk614",
"LDL_finalCU", "HDL_finalCU", "TC_finalCU", "TG_finalCU")),
by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)
```
# Fix counts
We limit the maximum count at 100.
```{r}
# ?ggpubr::ggboxplot()
# compare_means(LRCH1 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
# ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup))
cat("Get summary statistics for target:\n")
summary(AERNASE.clin.hdac9$HDAC9)
cat("\nCount number of values > 100 for target:\n")
sum(AERNASE.clin.hdac9$HDAC9 > 100)
cat("\nSetting values > 100 for target to 100.\n")
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(HDAC9_max100 = ifelse(HDAC9 > 100, 100, HDAC9))
cat("Get summary statistics for target after fixing values:\n")
summary(AERNASE.clin.hdac9$HDAC9_max100)
```
# Additional figures
## Age and sex
We want to create per-age-group figures median ± interquartile range.
- Box and Whisker plot for target(s) plaque levels by sex.
- Box and Whisker plot for target(s) plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).
```{r per Sex}
# ?ggpubr::ggboxplot()
compare_means(HDAC9 ~ Gender, data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("Gender"),
y = "HDAC9",
xlab = "gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Gender.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Gender, data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("Gender"),
y = "HDAC9_max100",
xlab = "gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Gender.pdf"), plot = last_plot())
```
```{r AgeGroups}
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
Age >= 55 & Age <= 64 ~ "55-64",
Age >= 65 & Age <= 74 ~ "65-74",
Age >= 75 & Age <= 84 ~ "75-84",
Age >= 85 ~ "85+")))
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
Age >= 55 & Age <= 64 & Gender == "male"~ "55-64 males",
Age >= 65 & Age <= 74 & Gender == "male"~ "65-74 males",
Age >= 75 & Age <= 84 & Gender == "male"~ "75-84 males",
Age >= 85 & Gender == "male"~ "85+ males",
Age < 55 & Gender == "female" ~ "<55 females" ,
Age >= 55 & Age <= 64 & Gender == "female"~ "55-64 females ",
Age >= 65 & Age <= 74 & Gender == "female"~ "65-74 females",
Age >= 75 & Age <= 84 & Gender == "female"~ "75-84 females",
Age >= 85 & Gender == "female"~ "85+ females")))
table(AERNASE.clin.hdac9$AgeGroup, AERNASE.clin.hdac9$Gender)
table(AERNASE.clin.hdac9$AgeGroupSex)
```
Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.
```{r per AgeGroup per Sex}
# ?ggpubr::ggboxplot()
compare_means(HDAC9 ~ AgeGroup, data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("AgeGroup"),
y = "HDAC9",
xlab = "Age groups (years)",
ylab = "HDAC9 (normalized expression)",
color = "AgeGroup",
palette = "npg",
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AgeGroup.pdf"), plot = last_plot())
compare_means(HDAC9 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("AgeGroup"),
y = "HDAC9",
xlab = "Age groups (years) per gender",
ylab = "HDAC9 (normalized expression",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AgeGroup_perGender.pdf"), plot = last_plot())
# ?ggpubr::ggboxplot()
compare_means(HDAC9_max100 ~ AgeGroup, data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("AgeGroup"),
y = "HDAC9_max100",
xlab = "Age groups (years)",
ylab = "HDAC9 (normalized expression)",
color = "AgeGroup",
palette = "npg",
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.AgeGroup.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("AgeGroup"),
y = "HDAC9_max100",
xlab = "Age groups (years) per gender",
ylab = "HDAC9 (normalized expression",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.AgeGroup_perGender.pdf"), plot = last_plot())
```
## Hypertension & blood pressure
We want to create figures of target(s) levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs.
- Box and Whisker plot for target(s) plaque levels by hypertension group (no, yes)
- Box and Whisker plot for target(s) plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)
```{r BloodPressure}
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
systolic >= 120 & systolic <= 139 ~ "120-139",
systolic >= 140 & systolic <= 159 ~ "140-159",
systolic >= 160 ~ "160+")))
table(AERNASE.clin.hdac9$SBPGroup, AERNASE.clin.hdac9$Gender)
```
Now we can draw some graphs of plaque target(s) levels per sex and hypertension/blood pressure group as median ± interquartile range.
```{r per BloodPressure per Sex}
compare_means(HDAC9 ~ SBPGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "HDAC9",
xlab = "Systolic blood pressure (mmHg)",
ylab = "HDAC9 (normalized expression)",
color = "SBPGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Hypertension.selfreport, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "HDAC9",
xlab = "Self-reported hypertension",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.selfreport",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Hypertension.drugs, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "HDAC9",
xlab = "Hypertension medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.HypertensionDrugs.pdf"), plot = last_plot())
compare_means(HDAC9 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "HDAC9",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup_byGender.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "HDAC9",
xlab = "Self-reported hypertension per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension_byGender.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "HDAC9",
xlab = "Hypertension medication use per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())
compare_means(HDAC9 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)),
x = c("SBPGroup"),
y = "HDAC9",
xlab = "Systolic blood pressure (mmHg) by medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "HDAC9",
xlab = "Self-reported hypertension per medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())
```
```{r per BloodPressure per Sex HDAC9_max100}
compare_means(HDAC9_max100 ~ SBPGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "HDAC9_max100",
xlab = "Systolic blood pressure (mmHg)",
ylab = "HDAC9 (normalized expression)",
color = "SBPGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.SBPGroup.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Hypertension.selfreport, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "HDAC9_max100",
xlab = "Self-reported hypertension",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.selfreport",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Hypertension.drugs, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "HDAC9_max100",
xlab = "Hypertension medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.HypertensionDrugs.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "HDAC9_max100",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.SBPGroup_byGender.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "HDAC9_max100",
xlab = "Self-reported hypertension per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension_byGender.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "HDAC9_max100",
xlab = "Hypertension medication use per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)),
x = c("SBPGroup"),
y = "HDAC9_max100",
xlab = "Systolic blood pressure (mmHg) by medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "HDAC9_max100",
xlab = "Self-reported hypertension per medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())
```
## Hypercholesterolemia & LDL levels
We want to create figures of target(s) levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs.
- Box and Whisker plot for target(s) plaque levels by hypercholesterolemia (`risk614`) group (no, yes)
- Box and Whisker plot for target(s) plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for target(s) plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)
```{r LDLGroups}
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
LDL_finalCU >= 100 & LDL_finalCU <= 129 ~ "100-129",
LDL_finalCU >= 130 & LDL_finalCU <= 159 ~ "130-159",
LDL_finalCU >= 160 & LDL_finalCU <= 189 ~ "160-189",
LDL_finalCU >= 190 ~ "190+")))
table(AERNASE.clin.hdac9$LDLGroup, AERNASE.clin.hdac9$Gender)
```
```{r Fix Hypercholesterolemia, message=FALSE, warning=FALSE}
require(sjlabelled)
AERNASE.clin.hdac9$risk614 <- to_factor(AERNASE.clin.hdac9$risk614)
# Fix plaquephenotypes
attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"Hypercholesterolemia"] <- NA
# AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "missing value"] <- NA
# AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "no"] <- "no"
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "yes"] <- "yes"
detach(AERNASE.clin.hdac9)
table(AERNASE.clin.hdac9$risk614, AERNASE.clin.hdac9$Hypercholesterolemia)
# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
```
Now we can draw some graphs of plaque target(s) levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.
```{r per Hypercholesterolemia per Sex}
compare_means(HDAC9 ~ LDLGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "HDAC9",
xlab = "LDL (mg/dL) per gender",
ylab = "HDAC9 (normalized expression))",
color = "LDLGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups.pdf"), plot = last_plot())
compare_means(HDAC9 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "HDAC9",
xlab = "LDL (mg/dL) per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byGender.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Hypercholesterolemia, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "HDAC9",
xlab = "Diagnosed hypercholesterolemia",
ylab = "HDAC9 (normalized expression))",
color = "Hypercholesterolemia",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypercholesterolemia.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "HDAC9",
xlab = "Diagnosed hypercholesterolemia per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Med.Statin.LLD, data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "HDAC9",
xlab = "Lipid-lowering drug use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Med.Statin.LLD.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "HDAC9",
xlab = "Lipid-lowering drug use per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())
compare_means(HDAC9 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "HDAC9",
xlab = "LDL (mg/dL) per LLD use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)),
x = c("Hypercholesterolemia"),
y = "HDAC9",
xlab = "Diagnosed hypercholesterolemia per LLD use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())
```
```{r per Hypercholesterolemia per Sex HDAC9_max100}
compare_means(HDAC9_max100 ~ LDLGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "HDAC9_max100",
xlab = "LDL (mg/dL) per gender",
ylab = "HDAC9 (normalized expression))",
color = "LDLGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "HDAC9_max100",
xlab = "LDL (mg/dL) per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups_byGender.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Hypercholesterolemia, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "HDAC9_max100",
xlab = "Diagnosed hypercholesterolemia",
ylab = "HDAC9 (normalized expression))",
color = "Hypercholesterolemia",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypercholesterolemia.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "HDAC9_max100",
xlab = "Diagnosed hypercholesterolemia per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Med.Statin.LLD, data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "HDAC9_max100",
xlab = "Lipid-lowering drug use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Med.Statin.LLD.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "HDAC9_max100",
xlab = "Lipid-lowering drug use per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "HDAC9_max100",
xlab = "LDL (mg/dL) per LLD use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)),
x = c("Hypercholesterolemia"),
y = "HDAC9_max100",
xlab = "Diagnosed hypercholesterolemia per LLD use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())
```
## Kidney function (eGFR)
We want to create figures of target(s) levels stratified by kidney function.
- Box and Whisker plot for target(s) plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for target(s) plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)
```{r EGFR}
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
GFR_MDRD >= 15 & GFR_MDRD <= 29 ~ "15-29",
GFR_MDRD >= 30 & GFR_MDRD <= 59 ~ "30-59",
GFR_MDRD >= 60 & GFR_MDRD <= 89 ~ "60-89",
GFR_MDRD >= 90 ~ "90+")))
table(AERNASE.clin.hdac9$eGFRGroup, AERNASE.clin.hdac9$Gender)
table(AERNASE.clin.hdac9$eGFRGroup, AERNASE.clin.hdac9$KDOQI)
```
Now we can draw some graphs of plaque target(s) levels per sex and kidney function group as median ± interquartile range.
```{r per EGFR per Sex}
# Global test
compare_means(HDAC9 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "HDAC9",
xlab = "eGFR (mL/min per 1.73 m2)",
ylab = "HDAC9 (normalized expression)",
color = "eGFRGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR.pdf"), plot = last_plot())
compare_means(HDAC9 ~ eGFRGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "HDAC9",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR_byGender.pdf"), plot = last_plot())
compare_means(HDAC9 ~ KDOQI, data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "HDAC9",
xlab = "Kidney function (KDOQI)",
ylab = "HDAC9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.KDOQI.pdf"), plot = last_plot())
compare_means(HDAC9 ~ KDOQI, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "HDAC9",
xlab = "Kidney function (KDOQI) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.KDOQI_byGender.pdf"), plot = last_plot())
compare_means(HDAC9 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "HDAC9",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "HDAC9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR_KDOQI.pdf"), plot = last_plot())
```
```{r per EGFR per Sex HDAC9_max100}
# Global test
compare_means(HDAC9_max100 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "HDAC9_max100",
xlab = "eGFR (mL/min per 1.73 m2)",
ylab = "HDAC9 (normalized expression)",
color = "eGFRGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.EGFR.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ eGFRGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "HDAC9_max100",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.EGFR_byGender.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ KDOQI, data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "HDAC9_max100",
xlab = "Kidney function (KDOQI)",
ylab = "HDAC9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.KDOQI.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ KDOQI, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "HDAC9_max100",
xlab = "Kidney function (KDOQI) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.KDOQI_byGender.pdf"), plot = last_plot())
compare_means(HDAC9_max100 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "HDAC9_max100",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "HDAC9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.EGFR_KDOQI.pdf"), plot = last_plot())
```
## BMI
We want to create figures of target(s) levels stratified by BMI.
- Box and Whisker plot for target(s) plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for target(s) plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)
```{r BMI}
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
BMI >= 18.5 & BMI < 25 ~ "18.5-24",
BMI >= 25 & BMI < 30 ~ "25-29",
BMI >= 30 & BMI < 35 ~ "30-35",
BMI >= 35 ~ "35+")))
# require(labelled)
# AERNASE.clin.hdac9$BMI_US <- as_factor(AERNASE.clin.hdac9$BMI_US)
# AERNASE.clin.hdac9$BMI_WHO <- as_factor(AERNASE.clin.hdac9$BMI_WHO)
# table(AERNASE.clin.hdac9$BMI_WHO, AERNASE.clin.hdac9$BMI_US)
table(AERNASE.clin.hdac9$BMIGroup, AERNASE.clin.hdac9$Gender)
table(AERNASE.clin.hdac9$BMIGroup, AERNASE.clin.hdac9$BMI_WHO)
```
Now we can draw some graphs of plaque MCP1 levels per sex and age group as median ± interquartile range.
```{r per BMI per Sex}
# Global test
compare_means(HDAC9 ~ BMIGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "HDAC9",
xlab = "BMI groups (kg/m2)",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "BMIGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI.pdf"), plot = last_plot())
compare_means(HDAC9 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "HDAC9",
xlab = "BMI groups (kg/m2) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI_byGender.pdf"), plot = last_plot())
compare_means(HDAC9 ~ BMIGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("BMIGroup"),
y = "HDAC9",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "HDAC9 (normalized expression)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI_byWHO.pdf"), plot = last_plot())
```
```{r per BMI per Sex HDAC9_max100}
# Global test
compare_means(HDAC9_max100 ~ BMIGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "HDAC9_max100",
xlab = "BMI groups (kg/m2)",