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3. scRNAseq.Rmd
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3. scRNAseq.Rmd
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---
title: "Mapping CAC susceptibility loci with single-cell resolution in carotid plaques."
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
number_sections: yes
theme: paper
toc: yes
toc_float:
collapsed: no
smooth_scroll: yes
mainfont: Helvetica
subtitle: A 'druggable-MI-targets' project
editor_options:
chunk_output_type: inline
---
```{r global_options, include=FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
eval = TRUE, warning = FALSE, message = FALSE)
```
_Clean the environment._
```{r ClearEnvironment, echo = FALSE}
rm(list = ls())
```
_Set locations, and the working directory ..._
```{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")
STORAGE_loc = "/Volumes/LaCie/PLINK"
### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RAWDATA = paste0(STORAGE_loc, "/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(STORAGE_loc, "/analyses/consortia/CHARGE_1000G_CAC")
RESULTS = paste0(STORAGE_loc, "/analyses/consortia/CHARGE_1000G_CAC/AESCRNA")
TARGET_loc = paste0(PROJECT_loc, "/targets")
### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="AESCRNA"
cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(RESULTS)),
dir.create(file.path(RESULTS)),
FALSE)
ANALYSIS_loc = paste0(RESULTS)
# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")),
dir.create(file.path(ANALYSIS_loc, "/PLOTS")),
FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")
# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")),
dir.create(file.path(PLOT_loc, "/QC")),
FALSE)
QC_loc = paste0(PLOT_loc,"/QC")
# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")),
dir.create(file.path(ANALYSIS_loc, "/OUTPUT")),
FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")
cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(RESULTS))
getwd()
list.files()
```
_... a package-installation function ..._
```{r Function: installations, echo=FALSE}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```
_... and load those packages._
```{r Setting: loading_packages, echo=FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("tidylog")
library("tidylog", warn.conflicts = FALSE)
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("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("EnhancedVolcano")
install.packages.auto("haven")
install.packages.auto("tableone")
# install.packages.auto("Seurat") # latest version
# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# Replace '2.3.4' with your desired version
# devtools::install_version(package = 'Seurat', version = package_version('2.3.4'))
# install.packages("Seurat")
library("Seurat")
```
_We will create a datestamp and define the Utrecht Science Park Colour Scheme_.
```{r Setting: Colors, echo=FALSE}
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")
#ggplot2 default color palette
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
### ----------------------------------------------------------------------------
```
# ERA-CVD 'druggable-MI-targets'
<!-- ![ERA-CVD logo]("Users/swvanderlaan/iCloud/Genomics/Projects/#Druggable-MI-Genes/Administration/ERA-CVD\ Logo_CMYK.jpg") -->
For the ERA-CVD 'druggable-MI-targets' project (grantnumber: 01KL1802) we will perform two related RNA sequencing (RNAseq) experiments:
1) conventional ('bulk') RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of `r Today.Report` all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.
2) single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of `r Today.Report` data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.
Plaque samples are derived from carotid endarterectomies as part of the [Athero-Express Biobank Study](http:www/atheroexpress.nl) which is an ongoing study in the UMC Utrecht.
# Background
Here we map the CHARGE Consortium 1000G GWAS on _coronary artery calcification (CAC)_ susceptibility loci to the single-cell carotid plaque data. These are given in:
- `IndSigSNPsforSander.xlsx`
- `GeneList_15042020.xlsx`
```{r CAC targets}
library(openxlsx)
CAC_gene_list <- read.xlsx(paste0(TARGET_loc, "/Genes.xlsx"))
CAC_variants <- read.xlsx(paste0(TARGET_loc, "/Variants.xlsx"))
DT::datatable(CAC_gene_list)
DT::datatable(CAC_variants)
```
We will construct a list of genes to map to our scRNAseq data.
```{r CAC targets for mapping}
target_genes <- unlist(CAC_gene_list$Gene)
target_genes
```
# Load data
First we will load the data:
- scRNAseq experimental data and rename the cell types.
- Athero-Express clinical data.
## AESCRNA: single-cell RNAseq from carotid plaques
Here we load the latest dataset from our Athero-Express Single Cell RNA experiment.
```{r LoadData}
# scRNAseqData <- readRDS(paste0(RAWDATA, "/Seuset_40_patients/20210217_PlaqView_38_pts.RDS"))
scRNAseqData <- readRDS(paste0(RAWDATA, "/Seuset_40_patients/Seuset_40_patients.RDS"))
scRNAseqData
N_GENES=18283
```
The naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as 'KIT+ Mast cells"-like cells. Likewise we refer to the cell clusters as 'communities' of cells that exihibit similar properties, _i.e._ similar defining markers (_e.g. KIT_).
We will rename the cell types to human readable names.
```{r Change cell cummunity names}
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")
unique(scRNAseqData@active.ident)
celltypes <- c("CD14+CD68+ Macrophages I" = "CD14+CD68+ M I",
"CD14+CD68+ Macrophages II" = "CD14+CD68+ M II",
"CD14+CD68+ Macrophages III" = "CD14+CD68+ M III",
"CD3+CD8+ T cells I" = "CD3+CD8A+ T I",
"CD3+CD8A+ T Cells II" = "CD3+CD8A+ T II",
"CD3+CD8 T cells III" = "CD3+CD8A+ T III",
"CD3+CD4+ T Cells I" = "CD3+CD4+ T I",
"CD3+CD4+ T Cells II" = "CD3+CD4+ T II",
"CD3+CD4+ T Cells III" = "CD3+ Treg",
"CD34+ Endothelial Cells I" = "CD34+ EC I",
"CD34+ Endothelial Cells II" = "CD34+ EC II",
"Mixed Cells I" = "Mixed I",
"Mixed Cells II" = "Mixed II",
"ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC",
"NCAM1+ Natural Killer Cells" = "NCAM1+ NK",
"KIT+ Mast Cells" = "KIT+ MC",
"CD79A+ B Cells I" = "CD79A+ B I",
"CD79A+ B Cells II" = "CD79A+ B II")
scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData,
celltypes)
```
```{r Change cell cummunity names - new plot}
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
```
## Athero-Express Biobank Study: clinical data
Loading Athero-Express clinical data.
```{r LoadAEDB}
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"))
```
### 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, message=FALSE, warning=FALSE}
# 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, message=FALSE, warning=FALSE}
# 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$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
```
We will also fix the _diabetes_ status variable.
```{r FixDiabetes, message=FALSE, warning=FALSE}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)
table(AEDB$DM.composite, AEDB$DiabetesStatus)
# 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, message=FALSE, warning=FALSE}
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, message=FALSE, warning=FALSE}
# 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$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
```
### Prepare baseline characteristics
We are interested in the following variables at baseline.
- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
- Never smokers
- Ex-smokers
- Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
- Asymptomatic
- Amaurosis fugax
- TIA
- Stroke
- eGFR (mL/min/1.73 m²)
- Testosterone (nmol/L)
```{r Baseline AEDB: preparation}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")
### Artery levels
# AEdata$Artery_summary:
# value label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1 carotid (left & right)
# USE - 2 femoral/iliac (left, right or both sides)
# NOT USE - 3 other carotid arteries (common, external)
# NOT USE - 4 carotid bypass and injury (left, right or both sides)
# NOT USE - 5 aneurysmata (carotid & femoral)
# NOT USE - 6 aorta
# NOT USE - 7 other arteries (renal, popliteal, vertebral)
# NOT USE - 8 femoral bypass, angioseal and injury (left, right or both sides)
### AEdata$informedconsent
# value label
# NOT USE - -999 missing
# NOT USE - 0 no, died
# USE - 1 yes
# USE - 2 yes, health treatment when possible
# USE - 3 yes, no health treatment
# USE - 4 yes, no health treatment, no commercial business
# NOT USE - 5 yes, no tissue, no commerical business
# NOT USE - 6 yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7 yes, no questionnaires, no health treatment, no commercial business
# USE - 8 yes, no questionnaires, health treatment when possible
# NOT USE - 9 yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10 yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12 yes, no questionnaires, no health treatment
# NOT USE - 13 yes, no tissue, no health treatment
# NOT USE - 14 yes, no tissue, no questionnaires
# NOT USE - 15 yes, no tissue, health treatment when possible
# NOT USE - 16 yes, no tissue
# USE - 17 yes, no commerical business
# USE - 18 yes, health treatment when possible, no commercial business
# USE - 19 yes, no medical info, no commercial business
# USE - 20 yes, no questionnaires
# NOT USE - 21 yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22 yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23 yes, no medical info
# USE - 24 yes, no questionnaires, no commercial business
# USE - 25 yes, no questionnaires, no health treatment, no medical info
# USE - 26 yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27 yes, no health treatment, no medical info
# NOT USE - 28 no, doesn't want to
# NOT USE - 29 no, unable to sign
# NOT USE - 30 no, no reaction
# NOT USE - 31 no, lost
# NOT USE - 32 no, too old
# NOT USE - 34 yes, no medical info, health treatment when possible
# NOT USE - 35 no (never asked for IC because there was no tissue)
# USE - 36 yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37 no, endpoint
# USE - 38 wil niets invullen, wel alles gebruiken
# USE - 39 second informed concents: yes, no commercial business
# NOT USE - 40 nooit geincludeerd
cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(AEDB$Gender)
ae.hospital <- to_factor(AEDB$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.artery <- to_factor(AEDB$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"))
rm(ae.gender, ae.hospital, ae.artery)
# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)
AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)
AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)
AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)
AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)
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)
require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)
AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)
AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)
AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)
AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
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)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)
AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)
AEDB$informedconsent <- to_factor(AEDB$informedconsent)
AEDB.CEA <- subset(AEDB,
(Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
informedconsent != "no, died" &
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no health treatment" &
informedconsent != "yes, no tissue, no questionnaires" &
informedconsent != "yes, no tissue, health treatment when possible" &
informedconsent != "yes, no tissue" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "no, doesn't want to" &
informedconsent != "no, unable to sign" &
informedconsent != "no, no reaction" &
informedconsent != "no, lost" &
informedconsent != "no, too old" &
informedconsent != "yes, no medical info, health treatment when possible" &
informedconsent != "no (never asked for IC because there was no tissue)" &
informedconsent != "no, endpoint" &
informedconsent != "nooit geincludeerd" &
!is.na(AsymptSympt2G))
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
```
```{r Baseline AEDB: creation}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")
# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
"Age", "Gender",
"TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time",
"macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
"neutrophils", "Mast_cells_plaque",
"IPH.bin", "vessel_density_averaged",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
basetable_bin = c("Gender",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```
### Athero-Express Biobank Study: baseline characteristics
Showing the baseline table of the whole Athero-Express Biobank.
```{r Baseline AEDB: Visualize}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Gender",
data = AEDB.CEA, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
```
## AESCRNA: baseline characteristics
```{r Baseline: creation}
metadata <- scRNAseqData@meta.data %>% as_tibble() %>% separate(orig.ident, c("Patient", NA))
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)
scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)
# Replace missing data
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)
na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A",
"Not Available", "Not available",
"missing",
"-999", "-99",
"No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.
scRNAseqDataMetaAE %>%
replace_with_na_all(condition = ~.x %in% na_strings)
cat("====================================================================================================")
cat("SELECTION THE SHIZZLE")
cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")
ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")
ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany")
rm(ae.gender, ae.hospital, ae.artery, ae.ic)
scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
(Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
informedconsent != "no, died" &
informedconsent != "yes, no tissue, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
informedconsent != "yes, no tissue, no health treatment" &
informedconsent != "yes, no tissue, no questionnaires" &
informedconsent != "yes, no tissue, health treatment when possible" &
informedconsent != "yes, no tissue" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
informedconsent != "no, doesn't want to" &
informedconsent != "no, unable to sign" &
informedconsent != "no, no reaction" &
informedconsent != "no, lost" &
informedconsent != "no, too old" &
informedconsent != "yes, no medical info, health treatment when possible" &
informedconsent != "no (never asked for IC because there was no tissue)" &
informedconsent != "no, endpoint" &
informedconsent != "nooit geincludeerd")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)
# DT::datatable(scRNAseqDataMetaAE.all)
```
Showing the baseline table.
```{r Baseline: Visualize}
cat("===========================================================================================")
cat("CREATE BASELINE TABLE")
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Gender",
data = scRNAseqDataMetaAE.all, includeNA = TRUE),
nonnormal = c(),
quote = FALSE, showAllLevels = TRUE,
format = "p",
contDigits = 3)[,1:2]
```
Writing the baseline table to Excel format.
```{r Baseline: write}
# Write basetable
require(openxlsx)
write.xlsx(file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.scRNAseq.xlsx"),
format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) ,
rowNames = TRUE, colNames = TRUE, overwrite = TRUE)
```
# AESCRNA
## Quality control
Here review the number of cells per sample, plate, and patients. And plot the ratio's per sample and study number.
```{r QualityControl}
## check stuff
cat("\nHow many cells per type ...?")
sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))
# cat("\n\nHow many cells per plate ...?")
# sort(table(scRNAseqData@meta.data$ID))
# cat("\n\nHow many cells per type per plate ...?")
# table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)
cat("\n\nHow many cells per patient ...?")
sort(table(scRNAseqData@meta.data$Patient))
cat("\n\nVisualizing these ratio's per study number and sample ...?")
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())
# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)),
# cex.axis = 1.0, cex.names = 0.5, las = 1,
# col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
# dev.off()
# barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)),
# cex.axis = 1.0, cex.names = 0.5, las = 2,
# col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
# dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
# dev.off()
```
## Visualisations
Let's project known cellular markers.
```{r Visualisation: tSNE Exploration}
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
repel = TRUE)
# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDN1"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("ACKR1"), cols = c("#ECECEC", "#DB003F"))
# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols = c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols = c("#ECECEC", "#DB003F"))
# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols = c("#ECECEC", "#DB003F"))