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1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rmd
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---
title: "Mapping common variants to atherosclerotic plaques characteristics."
author: "[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan | 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: 6
fig_retina: 2
fig_width: 7
highlight: tango
theme: lumen
toc: yes
toc_float:
collapsed: no
smooth_scroll: yes
mainfont: Arial
subtitle: "A 'druggable-MI-targets' project"
editor_options:
chunk_output_type: inline
markdown:
wrap: 80
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
wwarning = 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)
```
# Setup
We will clean the environment, setup the locations, define colors, and create a datestamp.
*Clean the environment.*
```{r echo = FALSE}
rm(list = ls())
```
*Set locations and working directories...*
```{r LocalSystem, echo = FALSE}
### Operating System Version
### MacBook Pro
# ROOT_loc = "/Users/swvanderlaan/OneDrive - UMC Utrecht"
### MacBook Air
ROOT_loc = "/Users/slaan3/OneDrive - UMC Utrecht"
GENOMIC_loc = paste0(ROOT_loc, "/Genomics")
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
PLINK_loc=paste0(ROOT_loc,"/PLINK")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_TEMPLATE")
AEGSQC_loc = paste0(PLINK_loc, "/_AE_ORIGINALS/AEGS_COMBINED_QC2018")
MICHIMP_loc=paste0(PLINK_loc,"/_AE_ORIGINALS/AEGS_COMBINED_EAGLE2_1000Gp3v5HRCr11")
# use this if there is relevant information here.
TARGET_loc = paste0(GENOMIC_loc, "/Athero-Express/Forms/[YEAR]/AE_TEMPLATE")
### SOME VARIABLES WE NEED DOWN THE LINE
TRAIT_OF_INTEREST = "PCSK9" # Phenotype
PROJECTNAME = "SOME_FANCY_PROJECTNAME"
cat("\nCreate a new analysis directory...\n")
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)),
dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)),
FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")),
dir.create(file.path(ANALYSIS_loc, "/PLOTS")),
FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")),
dir.create(file.path(PLOT_loc, "/QC")),
FALSE)
QC_loc = paste0(PLOT_loc,"/QC")
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")),
dir.create(file.path(ANALYSIS_loc, "/OUTPUT")),
FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")),
dir.create(file.path(ANALYSIS_loc, "/BASELINE")),
FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")
ifelse(!dir.exists(file.path(PROJECT_loc, "/SNP")),
dir.create(file.path(PROJECT_loc, "/SNP")),
FALSE)
SNP_loc = paste0(PROJECT_loc, "/SNP")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```
*... a package-installation function ...*
```{r Function: installations}
install.packages.auto <- function(x) {
x <- as.character(substitute(x))
if(isTRUE(x %in% .packages(all.available = TRUE))) {
eval(parse(text = sprintf("require(\"%s\")", x)))
} else {
# Update installed packages - this may mean a full upgrade of R, which in turn
# may not be warrented.
#update.install.packages.auto(ask = FALSE)
eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
}
if(isTRUE(x %in% .packages(all.available = TRUE))) {
eval(parse(text = sprintf("require(\"%s\")", x)))
} else {
if (!requireNamespace("BiocManager"))
install.packages("BiocManager")
BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
# Code for older versions of R (<3.5.0)
# source("http://bioconductor.org/biocLite.R")
# Update installed packages - this may mean a full upgrade of R, which in turn
# may not be warrented.
# biocLite(character(), ask = FALSE)
eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
eval(parse(text = sprintf("require(\"%s\")", x)))
}
}
```
*... and load those packages.*
```{r loading_packages, message=FALSE, warning=FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")
# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)
install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("eeptools")
install.packages.auto("haven")
install.packages.auto("tableone")
install.packages.auto("BlandAltmanLeh")
# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# for plotting
install.packages.auto("pheatmap")
install.packages.auto("forestplot")
install.packages.auto("ggplot2")
install.packages.auto("ggpubr")
install.packages.auto("UpSetR")
devtools::install_github("thomasp85/patchwork")
```
*We will create a datestamp and define the Utrecht Science Park Colour Scheme*.
```{r Setting: Colors}
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
###
### No. Color HEX (RGB) CHR MAF/INFO
###---------------------------------------------------------------------------------------
### 1 yellow #FBB820 (251,184,32) => 1 or 1.0>INFO
### 2 gold #F59D10 (245,157,16) => 2
### 3 salmon #E55738 (229,87,56) => 3 or 0.05<MAF<0.2 or 0.4<INFO<0.6
### 4 darkpink #DB003F ((219,0,63) => 4
### 5 lightpink #E35493 (227,84,147) => 5 or 0.8<INFO<1.0
### 6 pink #D5267B (213,38,123) => 6
### 7 hardpink #CC0071 (204,0,113) => 7
### 8 lightpurple #A8448A (168,68,138) => 8
### 9 purple #9A3480 (154,52,128) => 9
### 10 lavendel #8D5B9A (141,91,154) => 10
### 11 bluepurple #705296 (112,82,150) => 11
### 12 purpleblue #686AA9 (104,106,169) => 12
### 13 lightpurpleblue #6173AD (97,115,173/101,120,180) => 13
### 14 seablue #4C81BF (76,129,191) => 14
### 15 skyblue #2F8BC9 (47,139,201) => 15
### 16 azurblue #1290D9 (18,144,217) => 16 or 0.01<MAF<0.05 or 0.2<INFO<0.4
### 17 lightazurblue #1396D8 (19,150,216) => 17
### 18 greenblue #15A6C1 (21,166,193) => 18
### 19 seaweedgreen #5EB17F (94,177,127) => 19
### 20 yellowgreen #86B833 (134,184,51) => 20
### 21 lightmossgreen #C5D220 (197,210,32) => 21
### 22 mossgreen #9FC228 (159,194,40) => 22 or MAF>0.20 or 0.6<INFO<0.8
### 23 lightgreen #78B113 (120,177,19) => 23/X
### 24 green #49A01D (73,160,29) => 24/Y
### 25 grey #595A5C (89,90,92) => 25/XY or MAF<0.01 or 0.0<INFO<0.2
### 26 lightgrey #A2A3A4 (162,163,164) => 26/MT
###
### ADDITIONAL COLORS
### 27 midgrey #D7D8D7
### 28 verylightgrey #ECECEC"
### 29 white #FFFFFF
### 30 black #000000
###----------------------------------------------------------------------------------------------
uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
"#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
"#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
"#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
"#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
"#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
"#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
"#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
"#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
"#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------
```
# Introduction
## Project details
*Collaborators*
[First name] [Initials] [Last name] [First name] [Initials] [Last name] [First name] [Initials] [Last name]
*Athero-Express Team*
Sander W. van der Laan, Arjan Boltjes, Michal Mokry, Hester den Ruijter, Folkert W. Asselbergs, Gert Jan de Borst,
Gerard Pasterkamp.
**Project ID** [`AE_[YYYYMMDD]_[PROJECTNUMBER]_[LEADCOLLABORATOR]_[PROJECTNAME]`]
## Background
Collaboration to study common variants for `r TRAIT_OF_INTEREST` in relation to atherosclerotic plaques characteristics.
The main list of variants are given below.
- `Genes.xlsx` - list of genes of interest.
- `Variants.xlsx` - list of variants of interest.
```{r targets, message=FALSE, warning=FALSE}
library(openxlsx)
gene_list_df <- read.xlsx(paste0(PROJECT_loc, "/SNP/Genes.xlsx"), sheet = "Genes")
gene_list <- unlist(gene_list_df$Gene)
gene_list
variant_list <- read.xlsx(paste0(PROJECT_loc, "/SNP/Variants.xlsx"), sheet = "Variants")
DT::datatable(variant_list)
```
We will test the hypothesis that common variants `r TRAIT_OF_INTEREST` are associated with plaque characteristics. We
will use data from the **Athero-Express Biobank Study**.
These are the questions we will address:
- Are any of the variants associated to plaque characteristics?
- Gene expression correlated to characteristics of plaques?
- Where target genes expressed, which cell types?
- What phenotypes available in the UK Biobank are correlated? Assessed through a PheWAS using [GWAS
Atlas](https://atlas.ctglab.nl).
- Lookup of available eQTLs and tissue expression in the [GTEx Portal](https://gtexportal.org/home/).
## Study design
### Genetic analysis modeling
For the genetic analyses we will perform regression analyses adjusted for age, sex (where applicable) and principal
components. So, we will apply the following model:
We will perform regression analyses adjusted for age, sex (where applicable) and principal components.
model 1: `phenotype ~ age + sex + chip-used + PC1 + PC2 + year-of-surgery`
We can also run sex-stratified analyses:
model 1m: `phenotype ~ age + chip-used + PC1 + PC2 + year-of-surgery` males only model 1f:
`phenotype ~ age + chip-used + PC1 + PC2 + year-of-surgery` females only
phenotypes are:
- `calcification`, coded `Calc.bin` no/minor vs. moderate/heavy staining
- `collagen`, coded `Collagen.bin` no/minor vs. moderate/heavy staining
- `fat10`, coded `Fat.bin_10` no/\<10% fat vs. \>10% fat
- `fat40`, coded `Fat.bin_40` no/\<40% fat vs. \>40% fat
- `intraplaque hemorrhage`, coded `IPH.bin` no vs. yes
- `macrophages (CD68)`, coded `macmean0` mean of computer-assisted calculation CD68<sup>+</sup> region of interest
- `smooth muscle cells (alpha-actin)`, coded `smcmean0` mean of computer-assisted calculation SMA<sup>+</sup> region
of interest
- `intraplaque vessel density (CD34)`, coded `vessel_density` manually counted CD34<sup>+</sup> cells per 3-4 hotspots
- `mast cells`, coded `Mast_cells_plaque` manually counted mast cell tryptase<sup>+</sup> cells
(<https://academic.oup.com/eurheartj/article/34/48/3699/484981>) [Note: low sample size]
- `neutrophils (CD66b)`, coded `neutrophils` manually counted CD66b<sup>+</sup> cells
(<https://pubmed.ncbi.nlm.nih.gov/20595650/>) [Note: low sample size]
- `plaque vulnerability index`, scaled from 0 to 4, where 0 is most stable, and 4 is least stable plaque phenotype.
Continuous variables were inverse-rank normal transformated, indicated by `_rankNorm`.
### Mapping genes to bulk RNAseq from carotid plaques
We will explore the expression of target gene(s) in bulk RNAseq data from carotid plaques and associate these to plaque
characteristics.
### Mapping genes to single cells in carotid plaques
We will use the last dataset from the scRNAseq data, including 35 individuals, to the project target gene(s).
## Athero-Express Biobank Study
The [*Athero-Express Biobank Study (AE)*](http://www.atheroexpress.nl){target="_blank"} contains plaque material of
patients that underwent endarterectomyat two Dutch tertiary referral centers. Details of the study design were described
before. Briefly, blood and plaque material were obtained during endarterectomy and stored at -80 ℃. Only carotid
endarterectomy (CEA) patients were included in the present study. All patients provided informed consent and the study
was approved by the medical ethics committee.
## Athero-Express Genomics Study
### DNA isolation and genotyping
We genotyped the AE in three separate, but consecutive experiments. In short, DNA was extracted from EDTA blood or (when
no blood was available) plaque samples of 1,858 consecutive patients from the Athero-Express Biobank Study and genotyped
in 3 batches.
For the *Athero-Express Genomics Study 1 (AEGS1)* 891 patients (602 males, 262 females, 27 unknown sex), included
between 2002 and 2007, were genotyped (440,763 markers) using the Affymetrix Genome-Wide Human SNP Array 5.0 (SNP5) chip
(Affymetrix Inc., Santa Clara, CA, USA) at [Eurofins Genomics](https://www.eurofinsgenomics.eu/){target="_blank"}
(formerly known as AROS).
For the *Athero-Express Genomics Study 2 (AEGS2)* 954 patients (640 makes, 313 females, 1 unknown sex), included between
2002 and 2013, were genotyped (587,351 markers) using the Affymetrix AxiomⓇ GW CEU 1 Array (AxM) at the [Genome Analysis
Center](https://www.helmholtz-muenchen.de/no_cache/gac/index.html){target="_blank"}.
For the *Athero-Express Genomics Study 3 (AEGS3)* 658 patients (448 males, 203 females, 5 unknown sex), included between
2002 and 2016, were genotyped (693,931 markers) using the Illumina GSA MD v1 BeadArray (GSA) at [Human Genomics
Facility, HUGE-F](http://glimdna.org/index.html){target="_blank"}.
All experiments were carried out according to OECD standards.
### Genotyping calling
We used the genotyping calling algorithms as advised by Affymetrix (AEGS1 and AEGS2) and Illumina (AEGS3):
- AEGS1: BRLMM-P
- AEGS2: AxiomGT1
- AEGS3: Illumina GenomeStudio
### Quality control after genotyping
After genotype calling, we adhered to community standard quality control and assurance (QCA) procedures of the genotype
data from AEGS1, AEGS2, and AEGS3. Samples with low average genotype calling and sex discrepancies (compared to the
clinical data available) were excluded. The data was further filtered on:
1) individual (sample) call rate \> 97%,
2) SNP call rate \> 97%,
3) minor allele frequencies (MAF) \> 3%,
4) average heterozygosity rate ± 3.0 s.d.,
5) relatedness (pi-hat \> 0.20),
6) Hardy--Weinberg Equilibrium (HWE p \< 1.0×10<sup>−3\<\sup\>), and
7) Monomorphic SNPs (\< 1.0×10<sup>−6\<\sup\>).
After QCA 2,493 samples remained, 108 of non-European descent/ancestry, and 156 related pairs. These comprise 890
samples and 407,712 SNPs in AEGS1, 869 samples and 534,508 SNPs in AEGS2, and 649954 samples and 534,508 SNPs in AEGS3
remained.
### Imputation
Before phasing using SHAPEIT2, data was lifted to genome build b37 using the liftOver tool from UCSC
(<https://genome.ucsc.edu/cgi-bin/hgLiftOver>). Finally, data was imputed with 1000G phase 3, version 5 and HRC release
1.1 as a reference using the [Michigan Imputation Server](https://imputationserver.sph.umich.edu/){target="_blank"}.
These results were further integrated using QCTOOL v2, where HRC imputed variants are given precendence over 1000G phase
3 imputed variants.
### Quality control after imputation
We compared quality of the three AEGS datasets, and listed some variables of interest.
- sample type (EDTA blood or plaque)
- genotyping chip used
- reason for filtering
We checked the studytype (AE or not), and *identity-by-descent (IBD)* within and between datasets to aid in sample
mixups, duplicate sample use, and relatedness. In addition, during genotyping quality control samples were identified
that deviated from *Hardy-Weinberg Equilibrium (HWE)*, had discordance in sex-coding and genotype sex, and deviated from
the *principal component analysis (PCA)* plot.
We will load the Athero-Express Biobank Study data, and all the samples that were send for genotyping and the final
QC'ed sampleList.
# Loading data
Loading Athero-Express Biobank Study clinical and biobank data, as well as the SampleList of genetic data.
## Clinical data
```{r LoadAEDB}
cat("* get Athero-Express Biobank Study Database...")
# METHOD 1: It seems this method gives loads of errors and warnings, which all are hard to comprehend
# or debug. We expect 3,527 samples, and 927 variables; we get 927 variables!!!
# AEdata = as.data.table(read.spss(paste0(INP_loc,"/2017-1NEW_AtheroExpressDatabase_ScientificAE_20171306_v1.0.sav"),
# trim.factor.names = TRUE, trim_values = TRUE, # we trim spaces in values
# reencode = TRUE, # we re-encode to the local locale encoding
# add.undeclared.levels = "append", # we do *not* want to convert to R-factors
# use.value.labels = FALSE, # we do *not* convert variables with value labels into R factors
# use.missings = TRUE, sub = "NA", # we will set every missing variable to NA
# duplicated.value.labels = "condense", # we will condense duplicated value labels
# to.data.frame = TRUE))
# AEdata.labels <- as.data.table(attr(AEdata, "variable.labels"))
# names(AEdata.labels) <- "Variable"
# METHOD 2: Using library("haven") importing seems flawless; best argument being:
# we expect 3,527 samples and 888 variables, which is what you'd get with this method
# So for now, METHOD 2 is prefered.
#
require(haven)
# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))
# writing off the SPSS data to an Excel.
# fwrite(AEdata, file = paste0(INP_loc,"/2017-1NEW_AtheroExpressDatabase_ScientificAE_20171306_v1.0.values.xlsx"),
# sep = ";", na = "NA", dec = ".", col.names = TRUE, row.names = FALSE,
# dateTimeAs = "ISO", showProgress = TRUE, verbose = TRUE)
# warnings()
AEDB[1:10, 1:10]
dim(AEDB)
cat("* get Athero-Express Genomics Study keys...")
AEGS123.sampleList.keytable <- fread(paste0(AEGSQC_loc, "/QC/SELECTIONS/20200319.QC.AEGS123.sampleList.keytable.txt"))
dim(AEGS123.sampleList.keytable)
# AEGS123.sampleList.keytable[1:10, 1:10]
```
## Examine AEDB
We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class
(type) it has, and what the variable description is.
There is an excellent post on this: <https://www.r-bloggers.com/working-with-spss-labels-in-r/>.
```{r AEDB: describe}
AEDB %>% sjPlot::view_df(show.type = TRUE,
show.frq = TRUE,
show.prc = TRUE,
show.na = TRUE,
max.len = TRUE,
wrap.labels = 20,
verbose = FALSE,
use.viewer = FALSE,
file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html"))
```
## Fix clinical data
We need to be very strict in defining *symptoms.* Therefore we will fix a new variable that groups *symptoms* at
inclusion.
Coding of *symptoms* is as follows:
- missing -999\
- Asymptomatic 0\
- TIA 1\
- minor stroke 2\
- Major stroke 3\
- Amaurosis fugax 4\
- Four vessel disease 5\
- Vertebrobasilary TIA 7\
- Retinal infarction 8\
- Symptomatic, but aspecific symtoms 9
- Contralateral symptomatic occlusion 10\
- retinal infarction 11\
- armclaudication due to occlusion subclavian artery, CEA needed for bypass 12\
- retinal infarction + TIAs 13\
- Ocular ischemic syndrome 14\
- ischemisch glaucoom 15\
- subclavian steal syndrome 16\
- TGA 17
We will group as follows:
1. Asymptomatic \> 0
2. TIA \> 1, 7, 13
3. Stroke \> 2, 3
4. Ocular \> 4, 14, 15
5. Retinal infarction \> 8, 11
6. Other \> 5, 9, 10, 12, 16, 17
```{r FixSymptoms}
# Fix symptoms
attach(AEDB)
AEDB[,"Symptoms.5G"] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"
# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"
detach(AEDB)
# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
#
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# 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)
#
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
#
# rm(AEDB.temp)
```
We will also fix the *plaquephenotypes* variable.
Coding of symptoms is as follows:
- missing -999\
- not relevant -888
- fibrous 1\
- fibroatheromatous 2\
- atheromatous 3
```{r FixPlaquePhenotypes}
# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)
# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
```
We will also fix the *diabetes* status variable.
```{r FixDiabetes}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)
# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# 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)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
```
We will also fix the *smoking* status variable. We are interested in whether someone never, ever or is currently (at the
time of inclusion) smoking. This is based on the questionnaire.
- `diet801`: are you a smoker?
- `diet802`: did you smoke in the past?
We already have some variables indicating smoking status:
- `SmokingReported`: patient has reported to smoke.
- `SmokingYearOR`: smoking in the year of surgery?
- `SmokerCurrent`: currently smoking?
```{r FixSmoking}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)
# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
#
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))
cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)
cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Current smoker"))
cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
useNA = "ifany",
dnn = c("Updated smoking status"))
cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Updated smoking status", "Current smoker"))
# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# 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)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
```
We will also fix the *alcohol* status variable.
```{r FixAlcohol}
# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)
# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
```
We will also fix and inverse-rank normal transform the continuous (manually) scored plaque phenotypes.
```{r IRNT PlaquePhenotypes}
AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)
AEDB$MAC_rankNorm <- qnorm((rank(AEDB$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$macmean0)))
AEDB$SMC_rankNorm <- qnorm((rank(AEDB$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB$smcmean0)))
AEDB$Neutrophils_rankNorm <- qnorm((rank(AEDB$neutrophils, na.last = "keep") - 0.5) / sum(!is.na(AEDB$neutrophils)))
AEDB$MastCells_rankNorm <- qnorm((rank(AEDB$Mast_cells_plaque, na.last = "keep") - 0.5) / sum(!is.na(AEDB$Mast_cells_plaque)))
AEDB$VesselDensity_rankNorm <- qnorm((rank(AEDB$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB$vessel_density_averaged)))
```
```{r IRNT PlaquePhenotypes: Visualisation, message=FALSE, warning=FALSE}
library(labelled)
AEDB$Gender <- to_factor(AEDB$Gender)
ggpubr::gghistogram(AEDB, "macmean0",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% of macrophages (CD68)",
xlab = "% per region of interest",
ggtheme = theme_minimal())
ggpubr::gghistogram(AEDB, "MAC_rankNorm",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% of macrophages (CD68)",
xlab = "% per region of interest\ninverse-rank normalized number",
ggtheme = theme_minimal())
ggpubr::gghistogram(AEDB, "smcmean0",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% of smooth muscle cells (SMA)",
xlab = "% per region of interest",
ggtheme = theme_minimal())
ggpubr::gghistogram(AEDB, "SMC_rankNorm",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% of smooth muscle cells (SMA)",
xlab = "% per region of interest\ninverse-rank normalized number",
ggtheme = theme_minimal())
ggpubr::gghistogram(AEDB, "neutrophils",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of neutrophils (CD66b)",
xlab = "counts per plaque",
ggtheme = theme_minimal())
ggpubr::gghistogram(AEDB, "Neutrophils_rankNorm",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of neutrophils (CD66b)",
xlab = "counts per plaque\ninverse-rank normalized number",
ggtheme = theme_minimal())
ggpubr::gghistogram(AEDB, "Mast_cells_plaque",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of mast cells",
xlab = "counts per plaque",
ggtheme = theme_minimal())
ggpubr::gghistogram(AEDB, "MastCells_rankNorm",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of mast cells",
xlab = "counts per plaque\ninverse-rank normalized number",
ggtheme = theme_minimal())
ggpubr::gghistogram(AEDB, "vessel_density_averaged",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels",
xlab = "counts per 3-4 hotspots",
ggtheme = theme_minimal())
ggpubr::gghistogram(AEDB, "VesselDensity_rankNorm",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels",
xlab = "counts per 3-4 hotspots\ninverse-rank normalized number",
ggtheme = theme_minimal())
```
Here we calculate the *plaque instability/vulnerability* index
```{r Plaque Vulnerability}
# Plaque vulnerability
require(labelled)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
table(AEDB$Macrophages.bin)
table(AEDB$Fat.bin_10)
table(AEDB$Collagen.bin)
table(AEDB$SMC.bin)
table(AEDB$IPH.bin)
# SPSS code
#
# *** syntax- Plaque vulnerability**.
# COMPUTE Macro_instab = -999.
# IF macrophages.bin=2 Macro_instab=1.
# IF macrophages.bin=1 Macro_instab=0.
# EXECUTE.
#
# COMPUTE Fat10_instab = -999.
# IF Fat.bin_10=2 Fat10_instab=1.
# IF Fat.bin_10=1 Fat10_instab=0.
# EXECUTE.
#
# COMPUTE coll_instab=-999.
# IF Collagen.bin=2 coll_instab=0.
# IF Collagen.bin=1 coll_instab=1.
# EXECUTE.
#
#
# COMPUTE SMC_instab=-999.
# IF SMC.bin=2 SMC_instab=0.
# IF SMC.bin=1 SMC_instab=1.
# EXECUTE.
#
# COMPUTE IPH_instab=-999.
# IF IPH.bin=0 IPH_instab=0.
# IF IPH.bin=1 IPH_instab=1.
# EXECUTE.
#
# COMPUTE Instability=Macro_instab + Fat10_instab + coll_instab + SMC_instab + IPH_instab.
# EXECUTE.
# Fix plaquephenotypes
attach(AEDB)
# mac instability
AEDB[,"MAC_Instability"] <- NA
AEDB$MAC_Instability[Macrophages.bin == -999] <- NA
AEDB$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
AEDB$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1
# fat instability
AEDB[,"FAT10_Instability"] <- NA
AEDB$FAT10_Instability[Fat.bin_10 == -999] <- NA
AEDB$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
AEDB$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1
# col instability
AEDB[,"COL_Instability"] <- NA
AEDB$COL_Instability[Collagen.bin == -999] <- NA
AEDB$COL_Instability[Collagen.bin == "no/minor"] <- 1
AEDB$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0
# smc instability
AEDB[,"SMC_Instability"] <- NA
AEDB$SMC_Instability[SMC.bin == -999] <- NA
AEDB$SMC_Instability[SMC.bin == "no/minor"] <- 1
AEDB$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0
# iph instability
AEDB[,"IPH_Instability"] <- NA
AEDB$IPH_Instability[IPH.bin == -999] <- NA
AEDB$IPH_Instability[IPH.bin == "no"] <- 0
AEDB$IPH_Instability[IPH.bin == "yes"] <- 1
detach(AEDB)
table(AEDB$MAC_Instability, useNA = "ifany")
table(AEDB$FAT10_Instability, useNA = "ifany")
table(AEDB$COL_Instability, useNA = "ifany")
table(AEDB$SMC_Instability, useNA = "ifany")
table(AEDB$IPH_Instability, useNA = "ifany")
# creating vulnerability index
AEDB <- AEDB %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
)
table(AEDB$Plaque_Vulnerability_Index, useNA = "ifany")
# str(AEDB$Plaque_Vulnerability_Index)
```
## Prepare baseline summary
We are interested in the following variables at baseline.
- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)