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PatientDataset.Rmd
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---
title: "SARS-CoV-2 reactive T-cells in COVID-19 - Figure 5"
output:
html_document:
toc: yes
theme: united
code_folding: hide
pdf_document:
toc: yes
---
```{r, echo = F}
knitr::opts_chunk$set(warning = F, message = F)
knitr::opts_chunk$set(fig.pos = "!H", out.extra = "", fig.width = 9, fig.height = 6)
#options(digits = 3)
#knitr::opts_chunk$set(dpi=150,fig.width=7)
```
```{r load_libraries, echo = F, message = F, warning = F}
library(sjPlot)
library(sjmisc)
library(readxl)
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(reshape2)
library(broom)
library(stringr)
library(MASS) # for rlm
library(ggthemes)
library(lmerTest)
library(glmm)
library(ggstatsplot)
#library(patchwork)
#library(kableExtra)
#library(rmarkdown)
```
## Load data tables
```{r load_datasets}
# delta alpha clinical table
PNT <- read_excel("../2022_05_19_PNT_CT.xlsx") %>%
dplyr::select(contains("Studie 1"),
contains("PatID"),
contains("exclude"),
contains("Tag PSO"),
contains("PSO/PI"),
contains("Zeitraum"),
contains("Serum"),
contains("Dexa")) %>%
rename(PatID_Studie1 = `PatID`,
Studie1 = `Studie 1`,
exclude_immuno = `# exclude (immunosuppression, other)`,
exclude_B.1.1.7 = `#exclude for B1.1.7`,
exclude_B.1.617.2 = `#exclude for B.1.617.2`,
TagPSO = `Tag PSO`,
Timepoint = `Zeitraum`,
B.1.1.7 = `Serum ID50: Variant B.1.1.7`,
B.1.617.2 = `Serum ID50: Variant B.1.617.2`,
BA.2 = `Serum ID50: Variant BA.2`,
Dexa = `Dexamethason`) %>%
mutate(PatID_Studie1 = str_replace(PatID_Studie1, c("C19-CB-"), "")) %>%
mutate(PatID_Studie1 = as.character(as.numeric(PatID_Studie1))) %>%
mutate(TagPSO = as.numeric(TagPSO)) %>%
mutate(Studie1 = str_replace(Studie1, c("Pa-COVID-19"), "Pa-COVID")) %>%
mutate(PatID_Studie1 = paste(tolower(paste(substr(Studie1, 1, 2))),
tolower(paste(substr(Studie1, (nchar(Studie1)-1), nchar(Studie1)),
PatID_Studie1,
sep = "")),
sep = "")) %>%
mutate(exclude_immuno = as.numeric(replace_na(exclude_immuno, 0))) %>%
filter(exclude_immuno == 0) %>%
mutate(exclude_B.1.1.7 = as.numeric(replace_na(exclude_B.1.1.7, 0))) %>%
#mutate(Timepoint = str_replace_all(Timepoint, c("5" = "6", "W4" = "M1"))) %>%
mutate(exclude_B.1.617.2 = as.numeric(replace_na(exclude_B.1.617.2, 0))) %>%
mutate(Dexa = str_replace_all(Dexa, c("1" = "D+", "0" = "D-", "3" = "mild", "4" = "vaccine control")))
# FACS results
facs <- read_excel("../cleaned_FACS_mastertable 2022_04.xlsx") %>%
rename(ID = `Studie und PatID`,
Timepoint = `Zeitraum`) %>%
separate(ID, c("Studie1", "PatID_Studie1"), sep = " ") %>%
filter(!is.na(CEFX) | !is.na(S1) | !is.na(S2) | !is.na(SEB)) %>%
mutate(PatID_Studie1 = as.character(PatID_Studie1)) %>%
#filter(!(ausschliessen == 1)) %>%
mutate(Studie1 = str_replace(Studie1, c("Pa-COVID-19"), "Pa-COVID")) %>%
mutate(PatID_Studie1 = paste(tolower(paste(substr(Studie1, 1, 2))),
tolower(paste(substr(Studie1, (nchar(Studie1)-1), nchar(Studie1)),
PatID_Studie1,
sep = "")),
sep = "")) %>%
mutate(Timepoint = str_replace_all(Timepoint, c("W 2"="W2"))) %>%
mutate(Dexa = str_replace_all(Dexa, c("1" = "D+", "0" = "D-", "3" = "mild", "4" = "vaccine control")))
# meta data
meta <- read_excel("../metadata 2022 05 24 CT.xlsx") %>%
dplyr::select(#contains("sanity"),
contains("Studie 1"),
contains("Alter / Geschaetztes Alter bei Einschluss gr 3 Jahre"),
contains("Geschlecht bei der Geburt"),
contains("Dexamethason"),
contains("Immunosuppression"),
contains("Bemerkung"),
contains("Start Dexa"),
contains("Ende Dexa")) %>%
rename(PatID_Studie1 = `PatID Studie 1`,
Age = `Alter / Geschaetztes Alter bei Einschluss gr 3 Jahre`,
Sex = `Geschlecht bei der Geburt` ,
Studie1 = `Studie 1`,
Dexa = Dexamethason) %>%
mutate(PatID_Studie1 = as.character(PatID_Studie1)) %>%
mutate(Immunosuppression = as.numeric(replace_na(Immunosuppression, 0))) %>%
filter(Immunosuppression == 0) %>%
mutate(PatID_Studie1 = str_replace(PatID_Studie1, c("C19-CB-"), "")) %>%
mutate(PatID_Studie1 = as.character(as.numeric(PatID_Studie1))) %>%
mutate(Studie1 = str_replace(Studie1, c("Pa-COVID-19"), "Pa-COVID")) %>%
mutate(PatID_Studie1 = paste(tolower(paste(substr(Studie1, 1, 2))),
tolower(paste(substr(Studie1, (nchar(Studie1)-1), nchar(Studie1)),
PatID_Studie1,
sep = "")),
sep = "")) %>%
mutate(Dexa = str_replace_all(Dexa, c("1" = "D+", "0" = "D-", "3" = "mild", "4" = "vaccine control")))
# serology
sero <- read_excel("../2022_05_18_Serol_CT.xlsx") %>%
dplyr::select(contains("Studie 1"),
contains("Entnahmedatum"),
contains("Tag PSO"),
contains("Bemerkung"),
contains("Zeitraum"),
contains("IgG"),
contains("Dexa"),
contains("ausschliessen (Immunsuppression, other)"),
"PatID") %>%
rename(PatID_Studie1 = `PatID`,
Studie1 = `Studie 1`,
IgG = `IgG Value <7 set to 1`,
TagPSO = `Tag PSO`,
Timepoint = Zeitraum,
Dexa = `Dexamethason`,
ausschliessen_imm = `ausschliessen (Immunsuppression, other)`) %>%
mutate(ausschliessen_imm = replace_na(ausschliessen_imm, 0)) %>%
mutate(PatID_Studie1 = str_replace(PatID_Studie1, c("C19-CB-"), "")) %>%
mutate(PatID_Studie1 = as.character(as.numeric(PatID_Studie1))) %>%
mutate(IgG = as.numeric(str_replace(IgG, c(","), "\\."))) %>%
filter(!ausschliessen_imm == 1) %>%
mutate(Studie1 = str_replace(Studie1, c("Pa-COVID-19"), "Pa-COVID")) %>%
mutate(PatID_Studie1 = str_replace(PatID_Studie1, c("C19-CB-"), "")) %>%
mutate(PatID_Studie1 = as.character(as.numeric(PatID_Studie1))) %>%
mutate(PatID_Studie1 = paste(tolower(paste(substr(Studie1, 1, 2))),
tolower(paste(substr(Studie1, (nchar(Studie1)-1), nchar(Studie1)),
PatID_Studie1,
sep = "")),
sep = "")) %>%
mutate(Timepoint = str_replace_all(Timepoint, c("5" = "6"))) %>%
mutate(Dexa = str_replace_all(Dexa, c("1" = "D+", "0" = "D-", "3" = "mild", "4" = "vaccine control")))
SRT <- c("S1", "S2")
strain <- c("B.1.1.7", "B.1.617.2")
level_order <- c('1', '0', '3')
```
```{r}
facs_use <- facs %>%
filter(Marker == "CD4 CD40L+ CD137+" | Marker == "CD4 CD40L+ TNF-a+") %>%
dplyr::select(-c(CEFX, SEB, S2)) %>%
na.omit %>%
#rename(SRT_Timepoint = Timepoint) %>%
spread(Marker, S1)
PNT_use_B.1.1.7 <- PNT %>%
dplyr::select(-c(exclude_immuno, `PSO/PI`, exclude_B.1.617.2, B.1.617.2, BA.2, `PatID Studie 2`)) %>%
filter(exclude_B.1.1.7 == 0) %>%
dplyr::select(-c(exclude_B.1.1.7))
#%>%
# rename(B.1.1.7_Timepoint = Timepoint,
# B.1.1.7_TagPSO = TagPSO)
PNT_use_B.1.617.2 <- PNT %>%
dplyr::select(-c(exclude_immuno, `PSO/PI`, exclude_B.1.1.7, B.1.1.7, BA.2, `PatID Studie 2`)) %>%
filter(exclude_B.1.617.2 == 0) %>%
dplyr::select(-c(exclude_B.1.617.2)) #%>%
# rename(B.1.617.2_Timepoint = Timepoint,
# B.1.617.2_TagPSO = TagPSO)
PNT_use_BA.2 <- PNT %>%
dplyr::select(-c(exclude_immuno, `PSO/PI`, exclude_B.1.1.7, B.1.1.7, exclude_B.1.617.2, B.1.617.2, `PatID Studie 2`)) %>%
drop_na(BA.2) #%>%
# rename(BA.2_Timepoint = Timepoint,
# BA.2_TagPSO = TagPSO)
IgG_use <- sero %>%
dplyr::select(-c(Entnahmedatum, starts_with("Bemerkung"), `IgG Value BAU/ml >=7.1 reactive`,
`Tag post Dexa start`, starts_with("#"), Dexamethasone, ausschliessen_imm)) #%>%
# rename(IgG_Timepoint = Timepoint,
# IgG_TagPSO = TagPSO)
#
# merged_dfs <- list(facs_use, PNT_use_B.1.1.7, PNT_use_B.1.617.2, PNT_use_BA.2, IgG_use) %>%
# reduce(full_join, by = c("PatID_Studie1", "Studie1", "Dexa"))
```
```{r}
IgG_use_wide <- IgG_use %>%
pivot_wider(names_from = Timepoint, values_from = c(IgG, TagPSO), names_glue = "IgG_{.value}_{Timepoint}")
facs_use_137_wide <- facs_use %>%
dplyr::select(-`CD4 CD40L+ TNF-a+`) %>%
pivot_wider(names_from = Timepoint, values_from = `CD4 CD40L+ CD137+`, names_glue = "{.value}_{Timepoint}")
facs_use_TNF_wide <- facs_use %>%
dplyr::select(-`CD4 CD40L+ CD137+`) %>%
pivot_wider(names_from = Timepoint, values_from = `CD4 CD40L+ TNF-a+`, names_glue = "{.value}_{Timepoint}")
PNT_use_B.1.1.7_wide <- PNT_use_B.1.1.7 %>%
pivot_wider(names_from = Timepoint, values_from = c(B.1.1.7, TagPSO), names_glue = "B.1.1.7_{.value}_{Timepoint}")
PNT_use_B.1.617.2_wide <- PNT_use_B.1.617.2 %>%
pivot_wider(names_from = Timepoint, values_from = c(B.1.617.2, TagPSO), names_glue = "B.1.617.2_{.value}_{Timepoint}")
PNT_use_BA.2_wide <- PNT_use_BA.2 %>%
pivot_wider(names_from = Timepoint, values_from = c(BA.2, TagPSO), names_glue = "BA.2_{.value}_{Timepoint}")
```
```{r}
patients <- meta %>%
mutate(Immunosuppression = replace_na(Immunosuppression, 0)) %>%
filter(Immunosuppression != 1) %>%
dplyr::select(-c(starts_with("#"), Bemerkung, Immunosuppression, Age, Sex)) # 215 patients
#timepoint <- data.frame(Timepoint = c("W2", "M1", "M3", "M6", "PI"))
# pat_tp <- data.frame(PatID_Studie1 = lapply(patients, function(x) rep(x, 5)),
# Timepoint = rep(timepoint, length(patients)))
facs137 <- full_join(patients, facs_use_137_wide)
facsTNF <- left_join(facs137, facs_use_TNF_wide)
PNT_B.1.1.7 <- left_join(facsTNF, PNT_use_B.1.1.7_wide)
PNT_B.1.617.2 <- left_join(PNT_B.1.1.7, PNT_use_B.1.617.2_wide)
PNT_BA.2 <- left_join(PNT_B.1.617.2, PNT_use_BA.2_wide)
IgG <- left_join(PNT_BA.2, IgG_use_wide)
set.seed(1)
randomID <- data.frame(PatID_Studie1 = IgG$PatID_Studie1) %>%
mutate(RandID = sample(10000, size = nrow(.), replace = F))
write.table(randomID, "Randomly_generated_patient_ID.csv", sep = ";", row.names = F, quote = F, dec = ".")
patdata <- IgG %>%
rename(Study = Studie1, Group = Dexa) %>%
left_join(., randomID) %>%
relocate(RandID, .after = Study) %>%
dplyr::select(-c("PatID_Studie1")) %>%
rename(PatientID = RandID) %>%
dplyr::select(Study, PatientID, Group,
`CD4 CD40L+ CD137+_W2`, `CD4 CD40L+ CD137+_M1`, `CD4 CD40L+ CD137+_M3`, `CD4 CD40L+ CD137+_M6`, `CD4 CD40L+ CD137+_PI`,
`CD4 CD40L+ TNF-a+_W2`, `CD4 CD40L+ TNF-a+_M1`, `CD4 CD40L+ TNF-a+_M3`, `CD4 CD40L+ TNF-a+_M6`, `CD4 CD40L+ TNF-a+_PI`,
`IgG_IgG_W2`, `IgG_IgG_M1`, `IgG_IgG_M3`, `IgG_IgG_M6`, `IgG_IgG_PI`,
`IgG_TagPSO_W2`, `IgG_TagPSO_M1`, `IgG_TagPSO_M3`, `IgG_TagPSO_M6`, `IgG_TagPSO_PI`,
`B.1.1.7_B.1.1.7_W2`, `B.1.1.7_B.1.1.7_M1`, `B.1.1.7_B.1.1.7_M3`, `B.1.1.7_B.1.1.7_M6`, `B.1.1.7_B.1.1.7_PI`,
`B.1.1.7_TagPSO_W2`, `B.1.1.7_TagPSO_M1`, `B.1.1.7_TagPSO_M3`, `B.1.1.7_TagPSO_M6`, `B.1.1.7_TagPSO_PI`,
`B.1.617.2_B.1.617.2_W2`, `B.1.617.2_B.1.617.2_M1`, `B.1.617.2_B.1.617.2_M3`, `B.1.617.2_B.1.617.2_M6`, `B.1.617.2_B.1.617.2_PI`,
`B.1.617.2_TagPSO_W2`, `B.1.617.2_TagPSO_M1`, `B.1.617.2_TagPSO_M3`, `B.1.617.2_TagPSO_M6`, `B.1.617.2_TagPSO_PI`,
`BA.2_BA.2_PI`, `BA.2_TagPSO_PI`) %>%
mutate(across(where(is.numeric), ~ round(., 3)))
write.table(patdata, "Patient_dataset_SRT_IgG_PNT_singlerow.csv", sep = ";", row.names = F, quote = F, dec = ".")
```
```{r}
rand <- read.csv("Randomly_generated_patientID.csv") %>%
mutate(Study = IgG$Studie1) %>%
mutate(PatientID = substr(IgG$PatID_Studie1, 5,7)) %>%
rename(shortenedID = PatID_Studie1) %>%
relocate(shortenedID, .after = PatientID) %>%
relocate(RandID, .after = shortenedID)
write.table(rand, "Randomly_generated_patientID.csv", sep = ";", row.names = F, quote = F, dec = ".")
```
```{r}
rand <- read.csv("Randomly_generated_patientID.csv")
dat <- read_excel("Patient_dataset_SRT_IgG_PNT.xlsx")
rand_ID <- rand %>%
mutate(study_ID =
case_when(Study == "COVIMMUNIZE" ~ paste0("COVIZE", RandomID),
Study == "COVIMMUN" ~ paste0("COVIM", RandomID),
Study == "Pa-COVID" ~ paste0("PA-COV", RandomID),
Study == "EICOV" ~ paste0("EIC", RandomID),
TRUE ~ Study))
write.table(rand_ID, "Randomly_generated_patientID.csv", sep = ";", row.names = F, quote = F, dec = ".")
```