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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)
#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")))
# 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")))
# 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)) %>%
filter(!grepl(.$Bemerkung, pattern = "Immun", ignore.case = T)) %>%
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")))
# 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")))
SRT <- c("S1", "S2")
strain <- c("B.1.1.7", "B.1.617.2")
level_order <- c('1', '0', '3')
```
```{r models,echo = F}
meta_age_sex <- meta %>%
dplyr::select(PatID_Studie1, Age, Sex)
B117 <- PNT %>%
filter(exclude_B.1.1.7 == 0 & Timepoint != "PI") %>%
dplyr::select(PatID_Studie1, Dexa, B.1.1.7, Timepoint, TagPSO) %>%
left_join(., meta_age_sex) %>%
mutate(var_col = B.1.1.7)
B16172 <- PNT %>%
filter(exclude_B.1.617.2 == 0 & Timepoint != "PI") %>%
dplyr::select(PatID_Studie1, Dexa, B.1.617.2, Timepoint, TagPSO) %>%
left_join(., meta_age_sex) %>%
mutate(var_col = B.1.617.2)
sero_m <- sero %>% dplyr::left_join(meta_age_sex) %>%
filter(Timepoint != "PI")
srt_m <- facs %>%
left_join(meta_age_sex)
```
## Time series
### SRT time series
(No patient had more than one measurement in a single time point)
```{r Timeseries T-cells, PNT and IgG of patients with multiple measurements}
## SRT discrete time points
## CD4
for(i in SRT)
{
print(
facs %>%
#filter(Marker1 == "CD4") %>%
filter(!(Timepoint == "PI" | Timepoint == "VC")) %>%
filter(Marker == "CD4 CD40L+ CD137+" )#| Marker == "CD4 CD40L+ TNF-a+" | Marker == "CD4 CD40L+ IL-21+")
%>%
#left_join(SRT_2_tp, ., by = "PatID_Studie1") %>%
ggplot(., aes(x = factor(Timepoint,
levels = c('W2', 'M1', 'M3',
'M6')),
y = !!sym(i),
colour = Dexa)) +
#geom_jitter(width = 0.1, alpha = 0.5) +
geom_point(position = position_dodge(0.4), alpha = 0.7) +
scale_y_log10(minor = c(seq(0.001, 0.010, by = 0.001),
seq(0.010, 0.100, by = 0.01),
seq(0.100, 1.000, by = 0.1),
seq(1.000, 10, by = 1))) +
# coord_trans(y = "log10", ylim = c(0.001, 5)) +
# scale_y_continuous(breaks = c(0.001, 0.01, 0.1, 1, 2, 3, 4))+
#coord_cartesian(ylim = c(NA, 20)) +
labs(col = "Dexa") +
#geom_boxplot(aes(fill = factor(Dexa)), width = 0.5,
# colour = "gray47", alpha = 0.5, outlier.shape = NA) +
# scale_color_manual(breaks = c("D+", "D-", "mild"),
# values = c("orangered", "blue", "green3")) +
# scale_fill_manual(breaks = c("D+", "D-", "mild"),
# values = c("orangered", "blue", "green3")) +
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "lightslateblue", "black")) +
scale_fill_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "lightslateblue", "black")) +
facet_wrap(~factor(Marker, levels = c("CD4 CD40L+ CD137+"#,
#"CD4 CD40L+ TNF-a+","CD4 CD40L+ IL-21+"
)),
ncol = 3) +
geom_smooth(method = "rlm", fullrange = F,
aes(group = Dexa, fill = Dexa),
alpha = 0.1,
se = T, size = 0.4) +
# geom_line(position = position_dodge(0.4), aes(group = factor(PatID_Studie1)),
# alpha = 0.3, size = 0.2)+
#guides(fill = FALSE) +
xlab("Time point") +
ggtitle(paste0(i, " progression in CD4 cells")) +
theme_classic() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_line(),
panel.grid.minor = element_line(colour = "gray99"),
panel.border = element_blank(),
panel.background = element_blank())+
theme(axis.text = element_text(size = 11)) +
theme(strip.background =element_rect(fill="whitesmoke", colour = "white"),
strip.text.x = element_text(size = 11)))
ggsave(paste0("MSimages/", i, " progressionCD4.png"), width = 10, height = 10, units = "cm", dpi = 300)
ggsave(paste0("MSimages/", i, " progressionCD4.pdf"), width = 10, height = 10, units = "cm", dpi = 300)
#ggsave(paste0("MSimages/SRTtimeseries.png"), width = 20, height = 10, units = "cm", dpi = 300)
}
```
<!-- ```{r} -->
<!-- ## SRT TagPSO -->
<!-- ## CD4 -->
<!-- # for(i in SRT) -->
<!-- # { -->
<!-- # print( -->
<!-- # facs %>% -->
<!-- # filter(!(Timepoint == "PI" | Timepoint == "VC")) %>% -->
<!-- # filter(Marker == "CD4 CD40L+ CD137+" | Marker == "CD4 CD40L+ TNF-a+" | Marker == "CD4 CD40L+ IL-21+") %>% -->
<!-- # #left_join(SRT_2_tp, ., by = "PatID_Studie1") %>% -->
<!-- # ggplot(aes(x = TagPSO, y = !!sym(i), -->
<!-- # colour = Dexa)) + -->
<!-- # geom_point(position = position_dodge(0.4), alpha = 0.5) + -->
<!-- # geom_smooth(method = "rlm", -->
<!-- # fullrange = F, -->
<!-- # geom = "ribbon", -->
<!-- # aes(group = Dexa, fill = Dexa), -->
<!-- # alpha = 0.1, -->
<!-- # se = T, size = 0.4) + -->
<!-- # labs(col = "Dexa") + -->
<!-- # scale_y_log10(minor = c(seq(0.001, 0.010, by = 0.001), -->
<!-- # seq(0.010, 0.100, by = 0.01), -->
<!-- # seq(0.100, 1.000, by = 0.1), -->
<!-- # seq(1.000, 10, by = 1))) + -->
<!-- # # coord_trans(y = "log10", ylim = c(0.001, 5)) + -->
<!-- # # scale_y_continuous(breaks = c(0.001, 0.01, 0.1, 1, 2, 3, 4))+ -->
<!-- # scale_color_manual(breaks = c("D+", "D-", "mild"), -->
<!-- # values = c("orangered", "blue", "green3")) + -->
<!-- # scale_fill_manual(breaks = c("D+", "D-", "mild"), -->
<!-- # values = c("orangered", "blue", "green3")) + -->
<!-- # facet_wrap(~factor(Marker, levels = c("CD4 CD40L+ CD137+", -->
<!-- # "CD4 CD40L+ TNF-a+","CD4 CD40L+ IL-21+")), -->
<!-- # ncol = 3) + -->
<!-- # # geom_line(position = position_dodge(0.4), aes(group = factor(PatID_Studie1)), -->
<!-- # # alpha = 0.5, size = 0.2)+ -->
<!-- # xlab("Days PSO") + -->
<!-- # #guides(fill = FALSE) + -->
<!-- # ggtitle(paste0(i, " progression in CD4 cells")) + -->
<!-- # #theme(axis.text.x = element_text(angle = 90)) + -->
<!-- # theme_classic() + -->
<!-- # theme(axis.line = element_line(colour = "black"), -->
<!-- # panel.grid.major = element_line(), -->
<!-- # panel.grid.minor = element_line(colour = "gray99"), -->
<!-- # panel.border = element_blank(), -->
<!-- # panel.background = element_blank())+ -->
<!-- # theme(axis.text = element_text(size = 11)) + -->
<!-- # theme(strip.background =element_rect(fill="whitesmoke", colour = "white"), -->
<!-- # strip.text.x = element_text(size = 11))) -->
<!-- # ggsave(paste0("MSimages/", i, " progressionCD4_DaysPSO.png"), width = 20, height = 10, units = "cm", dpi= 300) -->
<!-- # } -->
<!-- ``` -->
<!-- ```{r} -->
<!-- for(i in SRT) -->
<!-- { -->
<!-- print( -->
<!-- facs %>% -->
<!-- filter(Marker1 == "CD4") %>% -->
<!-- filter(!(Timepoint == "PI" | Timepoint == "VC")) %>% -->
<!-- filter(Marker2 == "CD40L+ CD137+" | Marker2 == "CD40L+ TNF-a+" | Marker2 == "CD40L+ IL-21+") %>% -->
<!-- #left_join(SRT_2_tp, ., by = "PatID_Studie1") %>% -->
<!-- ggplot(., aes(x = factor(Timepoint, -->
<!-- levels = c('W2', 'M1', 'M3', -->
<!-- 'M6+')), -->
<!-- y = !!sym(i), -->
<!-- colour = Dexa, -->
<!-- group = factor(PatID_Studie1))) + -->
<!-- geom_point(position = position_dodge(0.4), alpha = 0.5) + -->
<!-- #geom_jitter(width = 0.1, alpha = 0.2) + -->
<!-- coord_trans(y = "log10", ylim = c(0.001, 5)) + -->
<!-- scale_y_continuous(breaks = c(0.001, 0.01, 0.1, 1, 2, 3, 4, 5))+ -->
<!-- labs(col = "Dexa") + -->
<!-- #geom_boxplot(aes(fill = factor(Dexa)), width = 0.5, -->
<!-- # colour = "gray47", alpha = 0.5, outlier.shape = NA) + -->
<!-- scale_color_manual(breaks = c("D+", "D-", "mild"), -->
<!-- values = c("orangered", "blue", "green3")) + -->
<!-- scale_fill_manual(breaks = c("D+", "D-", "mild"), -->
<!-- values = c("orangered", "blue", "green3")) + -->
<!-- geom_smooth(method = "lm", aes(group = Dexa, fill = Dexa), alpha = 0.1, -->
<!-- se = F, size = 0.4) + -->
<!-- geom_line(position = position_dodge(0.4), aes(group = factor(PatID_Studie1)), -->
<!-- alpha = 0.5, size = 0.2)+ -->
<!-- facet_grid(factor(Marker2, levels = c("CD40L+ CD137+", -->
<!-- "CD40L+ TNF-a+","CD40L+ IL-21+")) ~ factor(Dexa, levels = c('D+', 'D-', 'mild')), -->
<!-- #ncol = 3, -->
<!-- # labeller = labeller(Marker2 = as_labeller(marker.labels), -->
<!-- # Dexa = as_labeller(dexa.labels)), -->
<!-- scales = "free") + -->
<!-- xlab("Time point") + -->
<!-- ggtitle(paste0(i, " progression in CD4 cells")) + -->
<!-- theme_bw() + -->
<!-- theme(axis.text = element_text(size = 6.5)) + -->
<!-- theme(strip.background =element_rect(fill="aliceblue"))) -->
<!-- ggsave(paste0("MSimages/", i, " progressionCD4_byDexa.png"), width = 20, height = 15, units = "cm", dpi = 300) -->
<!-- } -->
<!-- ``` -->
<!-- ```{r} -->
<!-- for(i in SRT) -->
<!-- { -->
<!-- print( -->
<!-- facs %>% -->
<!-- filter(Marker1 == "CD4") %>% -->
<!-- filter(!(Timepoint == "PI" | Timepoint == "VC")) %>% -->
<!-- filter(Marker2 == "CD40L+ CD137+" | Marker2 == "CD40L+ TNF-a+" | Marker2 == "CD40L+ IL-21+") %>% -->
<!-- #left_join(SRT_2_tp, ., by = "PatID_Studie1") %>% -->
<!-- ggplot(., aes(x = TagPSO,y = !!sym(i), -->
<!-- colour = Dexa, -->
<!-- group = factor(PatID_Studie1))) + -->
<!-- theme(axis.text.x = element_text(angle = 90)) + -->
<!-- geom_point(position = position_dodge(0.4), alpha = 0.5) + -->
<!-- #geom_jitter(width = 0.1, alpha = 0.2) + -->
<!-- coord_trans(y = "log10", ylim = c(0.001, 5)) + -->
<!-- scale_y_continuous(breaks = c(0.001, 0.01, 0.1, 1, 2, 3, 4, 5))+ -->
<!-- labs(col = "Dexa") + -->
<!-- #geom_boxplot(aes(fill = factor(Dexa)), width = 0.5, -->
<!-- # colour = "gray47", alpha = 0.5, outlier.shape = NA) + -->
<!-- scale_color_manual(breaks = c("D+", "D-", "mild"), -->
<!-- values = c("orangered", "blue", "green3")) + -->
<!-- scale_fill_manual(breaks = c("D+", "D-", "mild"), -->
<!-- values = c("orangered", "blue", "green3")) + -->
<!-- geom_smooth(method = "lm", aes(group = Dexa, fill = Dexa), alpha = 0.1, -->
<!-- se = F, size = 0.5) + -->
<!-- geom_line(position = position_dodge(0.4), aes(group = factor(PatID_Studie1)), -->
<!-- alpha = 0.5, size = 0.2)+ -->
<!-- facet_grid(factor(Marker2, levels = c("CD40L+ CD137+", -->
<!-- "CD40L+ TNF-a+","CD40L+ IL-21+")) ~ factor(Dexa, levels = c('D+', 'D-', 'mild')), -->
<!-- #ncol = 3, -->
<!-- # labeller = labeller(Marker2 = as_labeller(marker.labels), -->
<!-- # Dexa = as_labeller(dexa.labels)), -->
<!-- scales = "free") + -->
<!-- xlab("Days PSO") + -->
<!-- ggtitle(paste0(i, " progression in CD4 cells")) + -->
<!-- theme_bw() + -->
<!-- theme(axis.text = element_text(size = 6.5)) + -->
<!-- theme(strip.background =element_rect(fill="aliceblue"))) -->
<!-- ggsave(paste0("MSimages/", i, " progressionCD4_DaysPSO_byDexa.png"), width = 20, height = 15, units = "cm", dpi = 300) -->
<!-- } -->
<!-- ``` -->
### PNT time series
(No patient had more than one measurement in one time point)
```{r PNT time series}
## PNT
# B.1.1.7
i = "B.1.1.7"
print(
PNT %>%
filter(!(Timepoint == "PI" | Timepoint == "VC")) %>%
filter(exclude_B.1.1.7 == 0) %>%
#mutate(exclude_col := "{exclude}_{{ i }}")
#filter("{exclude}_{{ i }}" == 0) %>%
#left_join(PNT_2_tp, ., by = "PatID_Studie1") %>%
ggplot(., aes(x = factor(Timepoint,
levels = c('W2', 'M1', 'M3',
'M6')),
y = !!sym(i),
colour = Dexa)) +
geom_jitter(width = 0.1, alpha = 0.5) +
labs(col = "Dexa") +
scale_y_log10(minor=c(1:10,
seq( 10, 100,by=10 ),
seq(100,1000,by=100),
seq(1000,10000,by=1000),
seq(10000,100000,by=10000))) +
#coord_trans(y = "log10", ylim = c(5, 37000)) +
#geom_boxplot(aes(fill = factor(Dexa)), width = 0.5,
# colour = "gray47", alpha = 0.5, outlier.shape = NA) +
#scale_y_continuous(breaks = c(10, 100, 1000,10000, 20000, 30000, 40000))+
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "lightslateblue", "black")) +
scale_fill_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "lightslateblue", "black")) +
# facet_wrap(~factor(Marker2, levels = c("CD40L+ CD137+","CD40L+ GrzB+",
# "CD40L+ IFN-g+","CD40L+ IL-21+",
# "CD40L+ TNF-a+","CD137+ GrzB+",
# "CD137+ IFN-g+","CD137+ IL-21+",
# "CD137+ TNF-a+","CD137+ GrzB-")),
# scales = "free") +
# facet_grid(. ~ factor(Dexa, levels = c('1', '0', '3')),
#ncol = 3,
#labeller = labeller(Dexa = as_labeller(dexa.labels))) +
geom_smooth(method = "rlm", aes(group = Dexa, fill = Dexa), alpha = 0.1,
se = T, size = 0.4) +
xlab("Time point") +
ggtitle(paste0(i, " neutralisation progression")) +
theme_classic() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_line(),
panel.grid.minor = element_line(colour = "gray99"),
panel.border = element_blank(),
panel.background = element_blank())+
theme(axis.text = element_text(size = 11)))
ggsave(paste0("MSimages/", i, " neutralisation progression.png"), width = 20, height = 15, units = "cm", dpi = 300)
ggsave(paste0("MSimages/", i, " neutralisation progression.pdf"), width = 20, height = 15, units = "cm", dpi = 300)
B117_age_sex_tp_dexa_minus <- lmerTest::lmer(data = B117, log10(B.1.1.7) ~ Timepoint + relevel(as.factor(Dexa), ref = "D-") + Age + Sex + (1|PatID_Studie1))
#tab_model(B117_age_sex_tp_dexa_minus)
B117_age_sex_tp_dexa_mild <- lmerTest::lmer(data = B117, log10(B.1.1.7) ~ Timepoint + relevel(as.factor(Dexa), ref = "mild") + Age + Sex + (1|PatID_Studie1))
tab_model(B117_age_sex_tp_dexa_minus, B117_age_sex_tp_dexa_mild)
```
```{r}
i = "B.1.617.2"
print(
PNT %>%
filter(!(Timepoint == "PI" | Timepoint == "VC")) %>%
filter(exclude_B.1.617.2 == 0) %>%
#mutate(exclude_col := "{exclude}_{{ i }}")
#filter("{exclude}_{{ i }}" == 0) %>%
#left_join(PNT_2_tp, ., by = "PatID_Studie1") %>%
ggplot(., aes(x = factor(Timepoint,
levels = c('W2','M1', 'M3',
'M6')),
y = !!sym(i),
colour = Dexa)) +
geom_jitter(width = 0.1, alpha = 0.5) +
labs(col = "Dexa") +
scale_y_log10(minor=c(1:10,
seq( 10, 100,by=10 ),
seq(100,1000,by=100),
seq(1000,10000,by=1000),
seq(10000,100000,by=10000))) +
#coord_trans(y = "log10", ylim = c(5, 37000)) +
#geom_boxplot(aes(fill = factor(Dexa)), width = 0.5,
# colour = "gray47", alpha = 0.5, outlier.shape = NA) +
#scale_y_continuous(breaks = c(10, 100, 1000,10000, 20000, 30000, 40000))+
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
scale_fill_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
# facet_wrap(~factor(Marker2, levels = c("CD40L+ CD137+","CD40L+ GrzB+",
# "CD40L+ IFN-g+","CD40L+ IL-21+",
# "CD40L+ TNF-a+","CD137+ GrzB+",
# "CD137+ IFN-g+","CD137+ IL-21+",
# "CD137+ TNF-a+","CD137+ GrzB-")),
# scales = "free") +
# facet_grid(. ~ factor(Dexa, levels = c('1', '0', '3')),
#ncol = 3,
#labeller = labeller(Dexa = as_labeller(dexa.labels))) +
geom_smooth(method = "rlm", aes(group = Dexa, fill = Dexa), alpha = 0.1,
se = T, size = 0.4) +
xlab("Time point") +
ggtitle(paste0(i, " neutralisation progression")) +
theme_classic() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_line(),
panel.grid.minor = element_line(colour = "gray99"),
panel.border = element_blank(),
panel.background = element_blank())+
theme(axis.text = element_text(size = 11)))
ggsave(paste0("MSimages/", i, " neutralisation progression.png"), width = 20, height = 15, units = "cm", dpi = 300)
ggsave(paste0("MSimages/", i, " neutralisation progression.pdf"), width = 20, height = 15, units = "cm", dpi = 300)
B16172_age_sex_tp_dexa_minus <- lmerTest::lmer(data = B16172, log10(B.1.617.2) ~ Timepoint + relevel(as.factor(Dexa), ref = "D-") + Age + Sex + (1|PatID_Studie1))
#tab_model(B117_age_sex_tp_dexa_minus)
B16172_age_sex_tp_dexa_mild <- lmerTest::lmer(data = B16172, log10(B.1.617.2) ~ Timepoint + relevel(as.factor(Dexa), ref = "mild") + Age + Sex + (1|PatID_Studie1))
tab_model(B16172_age_sex_tp_dexa_minus, B16172_age_sex_tp_dexa_mild)
```
```{r}
i = "B.1.1.7"
print(
PNT %>%
filter(!(Timepoint == "PI" | Timepoint == "VC")) %>%
filter(exclude_B.1.1.7 == 0) %>%
#left_join(PNT_2_tp, ., by = "PatID_Studie1") %>%
ggplot(., aes(x = TagPSO, y = !!sym(i),
colour = Dexa)) +
geom_jitter(width = 0.1, alpha = 0.5) +
labs(col = "Dexa") +
scale_y_log10(minor=c(1:10,
seq( 10, 100,by=10 ),
seq(100,1000,by=100),
seq(1000,10000,by=1000),
seq(10000,100000,by=10000))) +
#coord_trans(y = "log10", ylim = c(5, 37000)) +
#geom_boxplot(aes(fill = factor(Dexa)), width = 0.5,
# colour = "gray47", alpha = 0.5, outlier.shape = NA) +
#scale_y_continuous(breaks = c(10, 100, 1000,10000, 20000, 30000, 40000))+
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
scale_fill_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
# facet_wrap(~factor(Marker2, levels = c("CD40L+ CD137+","CD40L+ GrzB+",
# "CD40L+ IFN-g+","CD40L+ IL-21+",
# "CD40L+ TNF-a+","CD137+ GrzB+",
# "CD137+ IFN-g+","CD137+ IL-21+",
# "CD137+ TNF-a+","CD137+ GrzB-")),
# scales = "free") +
# facet_wrap(. ~ factor(Dexa, levels = c('D+', 'D-', 'mild')),
# #ncol = 3,
# scales = "free",
# labeller = labeller(Dexa = as_labeller(dexa.labels))) +
geom_smooth(method = "rlm", aes(group = Dexa, fill = Dexa), alpha = 0.1,
se = T, size = 0.4, fullrange = F) +
xlab("Days PSO") +
ggtitle(paste0(i, " neutralisation progression")) +
theme_classic() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_line(),
panel.grid.minor = element_line(colour = "gray99"),
panel.border = element_blank(),
panel.background = element_blank())+
theme(axis.text = element_text(size = 11)) +
theme(strip.background =element_rect(fill="whitesmoke", colour = "white"),
strip.text.x = element_text(size = 11)))
ggsave(paste0("MSimages/", i, " neutralisation progression DaysPSO.png"), width = 20, height = 15, units = "cm", dpi = 300)
B117_age_sex_tPSO_dexa_minus <- lmerTest::lmer(data = B117, log10(B.1.1.7) ~ TagPSO + relevel(as.factor(Dexa), ref = "D-") + Age + Sex + (1|PatID_Studie1))
#tab_model(B117_age_sex_tp_dexa_minus)
B117_age_sex_tPSO_dexa_mild <- lmerTest::lmer(data = B117, log10(B.1.1.7) ~ TagPSO + relevel(as.factor(Dexa), ref = "mild") + Age + Sex + (1|PatID_Studie1))
tab_model(B117_age_sex_tp_dexa_minus, B117_age_sex_tp_dexa_mild)
# https://stats.stackexchange.com/questions/435644/is-there-a-method-to-look-for-significant-difference-between-two-linear-regressi
# https://stackoverflow.com/questions/33192618/looking-for-differences-btw-linear-regression-lines-in-r
```
```{r}
i = "B.1.617.2"
print(
PNT %>%
filter(!(Timepoint == "PI" | Timepoint == "VC")) %>%
filter(exclude_B.1.617.2 == 0) %>%
#left_join(PNT_2_tp, ., by = "PatID_Studie1") %>%
ggplot(., aes(x = TagPSO, y = !!sym(i),
colour = Dexa)) +
geom_jitter(width = 0.1, alpha = 0.5) +
labs(col = "Dexa") +
scale_y_log10(minor=c(1:10,
seq( 10, 100,by=10 ),
seq(100,1000,by=100),
seq(1000,10000,by=1000),
seq(10000,100000,by=10000))) +
#coord_trans(y = "log10", ylim = c(5, 37000)) +
#geom_boxplot(aes(fill = factor(Dexa)), width = 0.5,
# colour = "gray47", alpha = 0.5, outlier.shape = NA) +
#scale_y_continuous(breaks = c(10, 100, 1000,10000, 20000, 30000, 40000))+
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
scale_fill_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
# facet_wrap(~factor(Marker2, levels = c("CD40L+ CD137+","CD40L+ GrzB+",
# "CD40L+ IFN-g+","CD40L+ IL-21+",
# "CD40L+ TNF-a+","CD137+ GrzB+",
# "CD137+ IFN-g+","CD137+ IL-21+",
# "CD137+ TNF-a+","CD137+ GrzB-")),
# scales = "free") +
# facet_wrap(. ~ factor(Dexa, levels = c('D+', 'D-', 'mild')),
# #ncol = 3,
# scales = "free",
# labeller = labeller(Dexa = as_labeller(dexa.labels))) +
geom_smooth(method = "rlm", aes(group = Dexa, fill = Dexa), alpha = 0.1,
se = T, size = 0.4, fullrange = F) +
xlab("Days PSO") +
ggtitle(paste0(i, " neutralisation progression")) +
theme_classic() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_line(),
panel.grid.minor = element_line(colour = "gray99"),
panel.border = element_blank(),
panel.background = element_blank())+
theme(axis.text = element_text(size = 11)) +
theme(strip.background =element_rect(fill="whitesmoke", colour = "white"),
strip.text.x = element_text(size = 11)))
ggsave(paste0("MSimages/", i, " neutralisation progression DaysPSO.png"), width = 20, height = 15, units = "cm", dpi = 300)
B16172_age_sex_tPSO_dexa_minus <- lmerTest::lmer(data = B16172, log10(B.1.617.2) ~ TagPSO + relevel(as.factor(Dexa), ref = "D-") + Age + Sex + (1|PatID_Studie1))
#tab_model(B117_age_sex_tp_dexa_minus)
B16172_age_sex_tPSO_dexa_mild <- lmerTest::lmer(data = B16172, log10(B.1.617.2) ~ TagPSO + relevel(as.factor(Dexa), ref = "mild") + Age + Sex + (1|PatID_Studie1))
tab_model(B16172_age_sex_tp_dexa_minus, B16172_age_sex_tp_dexa_mild)
```
```{r}
## PNT separated by dexa and joined by patients
i = "B.1.1.7"
print(
PNT %>%
filter(!(Timepoint == "PI" | Timepoint == "VC")) %>%
filter(exclude_B.1.1.7 == 0) %>%
ggplot(aes(x = factor(Timepoint,
levels = c('W2', 'M1', 'M3',
'M6')),
y = !!sym(i),
colour = Dexa,
group = factor(PatID_Studie1))) +
labs(col = "Dexa") +
theme(axis.text.x = element_text(angle = 90)) +
#geom_jitter(width = 0.1, alpha = 0.5) +
geom_point(position = position_dodge(0.1), alpha = 0.5) +
scale_y_log10(minor=c(5, 50, 500, 5000, 50000)) +
# minor=c(1:10,
# seq( 10, 100,by=10 ),
# seq(100,1000,by=100),
# seq(1000,10000,by=1000),
# seq(10000,100000,by=10000))
#coord_trans(y = "log10") +
#scale_y_continuous(breaks = c(10, 100, 1000,10000, 20000, 30000, 40000))+
#geom_boxplot(aes(fill = factor(Dexa)), width = 0.5,
# colour = "gray47", alpha = 0.5, outlier.shape = NA) +
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
scale_fill_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
# facet_wrap(~factor(Marker2, levels = c("CD40L+ CD137+","CD40L+ GrzB+",
# "CD40L+ IFN-g+","CD40L+ IL-21+",
# "CD40L+ TNF-a+","CD137+ GrzB+",
# "CD137+ IFN-g+","CD137+ IL-21+",
# "CD137+ TNF-a+","CD137+ GrzB-")),
# scales = "free") +
# facet_wrap(. ~ factor(Timepoint, levels = c('D+', 'D-', 'mild')),
# ncol = 3,
# scales = "free")+
#labeller = labeller(Dexa = as_labeller(dexa.labels))) +
geom_smooth(method = "rlm", aes(group = Dexa, fill = Dexa), alpha = 0.1,
se = T, size = 0.7) +
geom_line(position = position_dodge(0.1),
aes(group = factor(PatID_Studie1)), alpha = 0.5,
size = 0.2) +
xlab("Time point") +
ggtitle(paste0(i, " neutralisation progression")) +
theme_bw() +
#theme(legend.position = "none") +
theme(strip.background =element_rect(fill="aliceblue")))
ggsave(paste0("MSimages/", i, " neutralisation progression DaysPSO byPatient.png"), width = 20, height = 10, units = "cm", dpi = 300)
i = "B.1.617.2"
print(
PNT %>%
filter(!(Timepoint == "PI" | Timepoint == "VC")) %>%
filter(exclude_B.1.617.2 == 0) %>%
ggplot(aes(x = factor(Timepoint,
levels = c('W2', 'M1', 'M3',
'M6')),
y = !!sym(i),
colour = Dexa,
group = factor(PatID_Studie1))) +
labs(col = "Dexa") +
theme(axis.text.x = element_text(angle = 90)) +
#geom_jitter(width = 0.1, alpha = 0.5) +
geom_point(position = position_dodge(0.1), alpha = 0.5) +
scale_y_log10(minor=c(5, 50, 500, 5000, 50000)) +
# minor=c(1:10,
# seq( 10, 100,by=10 ),
# seq(100,1000,by=100),
# seq(1000,10000,by=1000),
# seq(10000,100000,by=10000))
#coord_trans(y = "log10") +
#scale_y_continuous(breaks = c(10, 100, 1000,10000, 20000, 30000, 40000))+
#geom_boxplot(aes(fill = factor(Dexa)), width = 0.5,
# colour = "gray47", alpha = 0.5, outlier.shape = NA) +
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
scale_fill_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
# facet_wrap(~factor(Marker2, levels = c("CD40L+ CD137+","CD40L+ GrzB+",
# "CD40L+ IFN-g+","CD40L+ IL-21+",
# "CD40L+ TNF-a+","CD137+ GrzB+",
# "CD137+ IFN-g+","CD137+ IL-21+",
# "CD137+ TNF-a+","CD137+ GrzB-")),
# scales = "free") +
# facet_wrap(. ~ factor(Timepoint, levels = c('D+', 'D-', 'mild')),
# ncol = 3,
# scales = "free")+
#labeller = labeller(Dexa = as_labeller(dexa.labels))) +
geom_smooth(method = "rlm", aes(group = Dexa, fill = Dexa), alpha = 0.1,
se = T, size = 0.7) +
geom_line(position = position_dodge(0.1),
aes(group = factor(PatID_Studie1)), alpha = 0.5,
size = 0.2) +
xlab("Time point") +
ggtitle(paste0(i, " neutralisation progression")) +
theme_bw() +
#theme(legend.position = "none") +
theme(strip.background =element_rect(fill="aliceblue")))
ggsave(paste0("MSimages/", i, " neutralisation progression DaysPSO byPatient.png"), width = 20, height = 10, units = "cm", dpi = 300)
```
### IgG time series
(No patient had more than one measurement in one time point)
```{r IgG time series}
sero %>%
filter(!(Timepoint == "PI" | Timepoint == "VC")) %>%
ggplot(., aes(x = factor(Timepoint,
levels = c('W2', 'M1', 'M3',
'M6')),
y = IgG,
colour = factor(Dexa))) +
theme(axis.text.x = element_text(angle = 90)) +
geom_jitter(width = 0.1, alpha = 0.5) +
labs(col = "Dexa") +
scale_y_log10(minor=c(1:10,
seq( 10, 100,by=10 ),
seq(100,1000,by=100),
seq(1000,10000,by=1000))) +
# coord_trans(y = "log10", ylim = c(5, 12000)) +
# scale_y_continuous(breaks = c(10, 100, 1000,10000))+
#geom_boxplot(aes(fill = factor(Dexa)), width = 0.5,
# colour = "gray47", alpha = 0.5, outlier.shape = NA) +
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "lightslateblue", "black")) +
scale_fill_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "lightslateblue", "black")) +
# facet_wrap(~factor(Marker2, levels = c("CD40L+ CD137+","CD40L+ GrzB+",
# "CD40L+ IFN-g+","CD40L+ IL-21+",
# "CD40L+ TNF-a+","CD137+ GrzB+",
# "CD137+ IFN-g+","CD137+ IL-21+",
# "CD137+ TNF-a+","CD137+ GrzB-")),
# scales = "free") +
geom_smooth(method = "rlm", aes(group = Dexa, fill = Dexa), alpha = 0.1,
se = T, size = 0.4) +
xlab("Time point") +
ggtitle("IgG progression") +
theme_classic() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_line(),
panel.grid.minor = element_line(colour = "gray99"),
panel.border = element_blank(),
panel.background = element_blank())+
theme(axis.text = element_text(size = 11)) +
theme(strip.background =element_rect(fill="whitesmoke", colour = "white"),
strip.text.x = element_text(size = 11))
ggsave(paste0("MSimages/IgG progression.png"), width = 20, height = 15, units = "cm", dpi = 300)
ggsave(paste0("MSimages/IgG progression.pdf"), width = 20, height = 15, units = "cm", dpi = 300)
IgG_age_sex_tp_dexa_minus <- lmerTest::lmer(data = sero_m, log10(IgG) ~ Timepoint + relevel(as.factor(Dexa), ref = "D-") + Age + Sex + (1|PatID_Studie1))
#tab_model(B117_age_sex_tp_dexa_minus)
IgG_age_sex_tp_dexa_mild <- lmerTest::lmer(data = sero_m, log10(IgG) ~ Timepoint + relevel(as.factor(Dexa), ref = "mild") + Age + Sex + (1|PatID_Studie1))
tab_model(IgG_age_sex_tp_dexa_minus, IgG_age_sex_tp_dexa_mild)
```
```{r}
sero %>%
filter(!(Timepoint == "PI" | Timepoint == "VC")) %>%
ggplot(aes(x = as.numeric(TagPSO),
y = IgG,
colour = Dexa)) +
theme(axis.text.x = element_text(angle = 90)) +
geom_jitter(width = 0.1, alpha = 0.5) +
labs(col = "Dexa") +
scale_y_log10(minor=c(1:10,
seq( 10, 100,by=10 ),
seq(100,1000,by=100),
seq(1000,10000,by=1000))) +
#coord_trans(y = "log10", ylim = c(5, 12000)) +
#scale_y_continuous(breaks = c(10, 100, 1000,10000))+
#geom_boxplot(aes(fill = factor(Dexa)), width = 0.5,
# colour = "gray47", alpha = 0.5, outlier.shape = NA) +
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
scale_fill_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
# facet_wrap(~factor(Marker2, levels = c("CD40L+ CD137+","CD40L+ GrzB+",
# "CD40L+ IFN-g+","CD40L+ IL-21+",
# "CD40L+ TNF-a+","CD137+ GrzB+",
# "CD137+ IFN-g+","CD137+ IL-21+",
# "CD137+ TNF-a+","CD137+ GrzB-")),
# scales = "free") +
geom_smooth(method = "rlm", aes(group = Dexa, fill = Dexa), alpha = 0.1,
se = T, size = 0.4) +
xlab("Days PSO") +
ggtitle("IgG progression") +
theme_classic() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_line(),
panel.grid.minor = element_line(colour = "gray99"),
panel.border = element_blank(),
panel.background = element_blank())+
theme(axis.text = element_text(size = 11)) +
theme(strip.background =element_rect(fill="whitesmoke", colour = "white"),
strip.text.x = element_text(size = 11))
ggsave(paste0("MSimages/IgG progression DaysPSO.png"), width = 20, height = 15, units = "cm", dpi = 300)
IgG_age_sex_tPSO_dexa_minus <- lmerTest::lmer(data = sero_m, log10(IgG) ~ TagPSO + relevel(as.factor(Dexa), ref = "D-") + Age + Sex + (1|PatID_Studie1))
#tab_model(B117_age_sex_tp_dexa_minus)
IgG_age_sex_tPSO_dexa_mild <- lmerTest::lmer(data = sero_m, log10(IgG) ~ TagPSO + relevel(as.factor(Dexa), ref = "mild") + Age + Sex + (1|PatID_Studie1))
tab_model(IgG_age_sex_tPSO_dexa_minus, IgG_age_sex_tPSO_dexa_mild)
```
```{r}
# sero by patient
sero %>%
filter(!(Timepoint == "PI" | Timepoint == "VC")) %>%
ggplot(aes(x = factor(Timepoint,
levels = c('W2', 'M1', 'M3',
'M6')),
y = IgG,
colour = Dexa,
group = factor(PatID_Studie1))) +
labs(col = "Dexa") +
theme(axis.text.x = element_text(angle = 90)) +
#geom_jitter(width = 0.1, alpha = 0.5) +
geom_point(position = position_dodge(0.1), alpha = 0.5) +
scale_y_log10(minor=c(5, 50, 500, 5000, 50000)) +
# minor=c(1:10,
# seq( 10, 100,by=10 ),
# seq(100,1000,by=100),
# seq(1000,10000,by=1000),
# seq(10000,100000,by=10000))
#coord_trans(y = "log10") +
#scale_y_continuous(breaks = c(10, 100, 1000,10000, 20000, 30000, 40000))+
#geom_boxplot(aes(fill = factor(Dexa)), width = 0.5,
# colour = "gray47", alpha = 0.5, outlier.shape = NA) +
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
scale_fill_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
# facet_wrap(~factor(Marker2, levels = c("CD40L+ CD137+","CD40L+ GrzB+",
# "CD40L+ IFN-g+","CD40L+ IL-21+",
# "CD40L+ TNF-a+","CD137+ GrzB+",
# "CD137+ IFN-g+","CD137+ IL-21+",
# "CD137+ TNF-a+","CD137+ GrzB-")),
# scales = "free") +
# facet_wrap(. ~ factor(Timepoint, levels = c('D+', 'D-', 'mild')),
# ncol = 3,
# scales = "free")+
#labeller = labeller(Dexa = as_labeller(dexa.labels))) +
geom_smooth(method = "rlm", aes(group = Dexa, fill = Dexa), alpha = 0.1,
se = T, size = 0.7) +
geom_line(position = position_dodge(0.1),
aes(group = factor(PatID_Studie1)), alpha = 0.5,
size = 0.2) +
xlab("Time point") +
ggtitle("IgG progression") +
theme_bw() +
#theme(legend.position = "none") +
theme(strip.background =element_rect(fill="aliceblue"))
ggsave(paste0("MSimages/IgG progression DaysPSO ByPatient.png"), width = 20, height = 15, units = "cm", dpi = 300)
```
# Correlations
(Plots are made in log scale but Spearman's rank correlations were performed on raw data)
### IgG vs PNT correlation
```{r Association of IgG and PNT at the respective timepoints}
Sero_PNT <- sero %>%
dplyr::select(PatID_Studie1, Dexa, Timepoint, IgG) %>%
full_join(., PNT, by = c("PatID_Studie1", "Dexa", "Timepoint"))
# IgG vs B.1.1.7
Sero_PNT %>%
filter(!Timepoint == "PI") %>%
#filter((Timepoint == "M3" | Timepoint == "M6")) %>%
ggplot(aes(x = B.1.1.7, y = IgG)) +
geom_point(alpha = 0.7, aes(colour = factor(Dexa))) +
labs(col = "Dexa") +
coord_trans(y = "log10") +
scale_y_continuous(breaks = c(10, 100, 1000))+
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
facet_wrap(~factor(Timepoint, levels = c('W2', 'M1', 'M3','M6')),
scales = "free_x",
drop = T) +
theme_classic() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_line(),
panel.grid.minor = element_line(colour = "gray99"),
panel.border = element_blank(),
panel.background = element_blank())+
theme(axis.text = element_text(size = 11)) +
theme(strip.background =element_rect(fill="whitesmoke", colour = "white"),
strip.text.x = element_text(size = 11)) +
ggtitle("IgG vs PNT measures at the same time point") +
xlab("B.1.1.7") + ylab("IgG BAU/ml") +
stat_cor(method = "spearman", size = 3)
#geom_smooth(method = "lm", se = T)
ggsave(paste0("MSimages/IgG vs B.1.1.7.png"), width = 20, height = 10, units = "cm", dpi = 300)
```
```{r}
Sero_PNT %>%
filter(!Timepoint == "PI") %>%
#filter((Timepoint == "M3" | Timepoint == "M6+"))%>%
ggplot(aes(x = B.1.1.7, y = IgG, colour = factor(Dexa))) +
geom_point(alpha = 0.7) +
coord_trans(y = "log10") +
scale_y_continuous(breaks = c(10, 100, 1000))+
labs(col = "Dexa") +
scale_color_manual(breaks = c("D+", "D-", "mild"),
values = c("orangered", "blue", "green3")) +
facet_wrap(~factor(Timepoint, levels = c('W2', 'M1', 'M3', 'M6')),
scales = "free_x",
ncol = 2) +
theme_classic() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_line(),
panel.grid.minor = element_line(colour = "gray99"),
panel.border = element_blank(),
panel.background = element_blank())+
theme(axis.text = element_text(size = 11)) +
theme(strip.background =element_rect(fill="whitesmoke", colour = "white"),
strip.text.x = element_text(size = 11)) +
ggtitle("IgG vs PNT measures at the same time point") +
xlab("B.1.1.7") + ylab("IgG BAU/ml") +
stat_cor(method = "spearman", size = 3) #+
#geom_smooth(method = "lm", se = T)
ggsave(paste0("MSimages/IgG vs B.1.1.7 slopeByDexa.png"), width = 20, height = 10, units = "cm")
```
```{r}
# IgG vs B.1.617.2
Sero_PNT %>%
filter(!Timepoint == "PI") %>%
#filter((Timepoint == "M3" | Timepoint == "M6+"))%>%
ggplot(aes(x = B.1.617.2, y = IgG)) +
geom_point(alpha = 0.7, aes(colour = factor(Dexa))) +
coord_trans(y = "log10") +
scale_y_continuous(breaks = c(10, 100, 1000))+
labs(col = "Dexa") +
scale_color_manual(breaks = c("D+", "D-", "mild"),