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RoVI_module2_shedding_antibodies_EED.Rmd
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RoVI_module2_shedding_antibodies_EED.Rmd
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---
title: "Analysis module 2: Maternal antibodies, study arm, and inflammatory markers by country and ORV response"
author: "RoVI study"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
flexdashboard::flex_dashboard:
self_contained: yes
theme: flatly
---
Sidebar {.sidebar}
=====================================
[Methods]
[Shedding by country/arm]
[Immunogenicity by country/arm]
[RV-IgA vs cumulative shedding]
[Maternal Abs by country/arm/exposure]
[Maternal Abs vs ORV outcome]
[EED biomarkers by country/arm/exposure]
[EED biomarkers vs ORV outcome]
[Neonatal exposure vs ORV outcome]
[Session info]
Methods {.hidden}
=====================================
#### Comparisons by country/arm
* For binary covariates (seroconversion, shedding, etc), comparisons are pairwise Fisher's exact tests with FDR adjustment.
* For continuous covariates (titre, inflammatory markers, etc), comparisons are ANOVAs of log-transformed values with post-hoc Tukey tests.
* \* p <0.05; \*\* p <0.005; \*\*\* p <0.0005.
#### Comparisons by ORV outcome
* For binary endpoints (seroconversion/shedding), comparisons are logistic regressions.
* For continuous endpoint (post-vaccination RV-IgA), comparisons are linear regressions of log-transformed RV-IgA concentration.
* See *Module 5* for full univariate and multivariate analyses of ORV outcome.
* \* p <0.05; \*\* p <0.005; \*\*\* p <0.0005.
Shedding by country/arm {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
```{r, message=FALSE}
# load RoVI packages and functions
source("input/packages_and_functions.R")
# import metadata, and create India-specific subset for analyses of study arm
s = read.csv("input/RoVI_metadata_by_family_03112020.csv", header=TRUE, stringsAsFactors=FALSE)
# updated coding for country
s$country_full = s$country
s$country[s$country=="India"] = "IND"
s$country[s$country=="Malawi"] = "MLW"
s$country[s$country=="UK"] = "UK"
# updated coding for country/arm
s$country_arm_full = s$country_arm
s$country_arm[s$country_arm=="India (OPV)"] = "IND (OPV)"
s$country_arm[s$country_arm=="India (IPV)"] = "IND (IPV)"
s$country_arm[s$country_arm=="Malawi"] = "MLW"
s$country_arm[s$country_arm=="UK"] = "UK"
# updated coding for country/neo+
s$country_exposure_full = s$country_exposure
s$country_exposure[s$country_exposure=="India (exposed)"] = "IND (neo+)"
s$country_exposure[s$country_exposure=="India (unexposed)"] = "IND (neo-)"
s$country_exposure[s$country_exposure=="Malawi"] = "MLW"
s$country_exposure[s$country_exposure=="UK"] = "UK"
sI = subset(s, country=="IND")
```
### Country
#### Summary of sample sizes
```{r}
s$n = 1
t = data.frame(
n = table(!is.na(s$n), s$country)[1,],
neonatal_shedding = table(!is.na(s$BS1_VP6_ct), s$country)[2,],
dose1_shedding = table(!is.na(s$dose1_shedding), s$country)[2,],
dose2_shedding = table(!is.na(s$dose2_shedding), s$country)[2,],
either_shedding = table(!is.na(s$either_shedding), s$country)[2,],
seroconv = table(!is.na(s$seroconv), s$country)[2,],
any_response = table(!is.na(s$any_response), s$country)[2,])
t
rm(t)
```
#### ORV shedding
##### Neonatal and vaccine shedding
```{r, fig.width=16, fig.height = 5}
p1 = fig_cat(s, "BS1_VP6_cat", title="neonatal RV shedding,\nweek of life 1") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 1.0625, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="***", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col)
p2 = fig_cat(s, "dose1_shedding", title="ORV shedding after dose 1,\nweek of life 7") + ylab(" ") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="***", y_position = 1.0625, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="***", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col)
p3 = fig_cat(s, "either_shedding", title="ORV shedding after either dose,\nweek of life 7/11") + ylab(" ") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="***", y_position = 1.0625, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="**", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col)
grid.arrange(p1,p2,p3, ncol=4)
rm(p1,p2,p3)
```
#### Raw data and p values
##### Neonatal shedding
```{r}
outcome_summary(s, "BS1_VP6_cat")
p_summary(s, "BS1_VP6_cat")
```
##### Dose 1 shedding
```{r}
outcome_summary(s, "dose1_shedding")
p_summary(s, "dose1_shedding")
```
##### Dose 2 shedding
```{r}
outcome_summary(s, "dose2_shedding")
p_summary(s, "dose2_shedding")
```
##### Shedding after either dose
```{r}
outcome_summary(s, "either_shedding")
p_summary(s, "either_shedding")
```
##### Shedding after both doses
```{r}
outcome_summary(s, "both_shedding")
p_summary(s, "both_shedding")
```
##### Rota shedding (VP6) by time-point
```{r, fig.width=25, fig.height = 5}
p1 = fig_cat(s, "MS1_VP6_cat", title="mother") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 1.125, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 1.0625, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="ns", y_position = 1, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p2 = fig_cat(s, "BS1_VP6_cat", title="week of life 1") + ylab(" ") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 1.0625, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="***", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col)
p3 = fig_cat(s, "BS2_VP6_cat", title="week of life 4") + ylab(" ") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="**", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 1.0625, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="*", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col)
p4 = fig_cat(s, "BS3_VP6_cat", title="week of life 6") + ylab(" ") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="*", y_position = 1.0625, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="*", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col)
p5 = fig_cat(s, "BS4_VP6_cat", title="week of life 7") + ylab(" ") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="***", y_position = 1.0625, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="***", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col)
p6 = fig_cat(s, "BS5_VP6_cat", title="week of life 10") + ylab(" ") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="***", y_position = 1.0625, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="**", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col)
p7 = fig_cat(s, "BS6_VP6_cat", title="week of life 11") + ylab(" ") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="***", y_position = 1.0625, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="*", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col)
grid.arrange(p1,p2,p3,p4,p5,p6,p7, ncol=7)
rm(p1,p2,p3,p4,p5,p6,p7)
```
From week of life 6 onwards, +2 weeks in the UK owing to later oral rotavirus vaccine schedule.
#### Raw data and p values
##### Maternal (MS1)
```{r}
outcome_summary(s, "MS1_VP6_cat")
p_summary(s, "MS1_VP6_cat")
```
##### Week 1 (BS1)
```{r}
outcome_summary(s, "BS1_VP6_cat")
p_summary(s, "BS1_VP6_cat")
```
##### Week 4 (BS2)
```{r}
outcome_summary(s, "BS2_VP6_cat")
p_summary(s, "BS2_VP6_cat")
```
##### Week 6/8 (BS3)
```{r}
outcome_summary(s, "BS3_VP6_cat")
p_summary(s, "BS3_VP6_cat")
```
##### Week 7/9 (BS4)
```{r}
outcome_summary(s, "BS4_VP6_cat")
p_summary(s, "BS4_VP6_cat")
```
##### Week 10/12 (BS5)
```{r}
outcome_summary(s, "BS5_VP6_cat")
p_summary(s, "BS5_VP6_cat")
```
##### Week 11/13 (BS6)
```{r}
outcome_summary(s, "BS6_VP6_cat")
p_summary(s, "BS6_VP6_cat")
```
##### Number of infants with birth shedding, dose 1 shedding, dose 2 shedding and post-vaccination IgA
```{r}
s1 = subset(s, !is.na(BS1_VP6_cat) & !is.na(dose1_shedding) & !is.na(dose2_shedding) & !is.na(BB2_IgA))
table(s1$country)
```
#### Minimum NSP2 Ct among ORV shedders
```{r, fig.width=6, fig.height=5}
s2 = subset(s1, either_shedding==1)
ggplot(s2, aes(factor(country), y=min_RV1_Ct, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.3, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape=NA) + ylab("minimum Ct") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) +
scale_y_continuous(limits=c(15,42.5), breaks=c(20,25,30,35,40)) + scale_y_reverse() +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = -10, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = -12.5, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="*", y_position = -15, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col) +
theme(text = element_text(size=18), axis.text = element_text(size=18), axis.title = element_text(size=20))
```
#### P values (pairwise Wilcoxon tests with FDR correction)
```{r}
t = data.frame(comp = c("IND-MLW", "UK-MLW", "IND-UK"),
p = c(wilcox.test(s2$min_RV1_Ct[s2$country!="UK"] ~ s2$country[s2$country!="UK"])$p.value,
wilcox.test(s2$min_RV1_Ct[s2$country!="IND"] ~ s2$country[s2$country!="IND"])$p.value,
wilcox.test(s2$min_RV1_Ct[s2$country!="MLW"] ~ s2$country[s2$country!="MLW"])$p.value))
t$fdr = p.adjust(t$p, method="fdr")
t
rm(t)
```
### Arm
#### Summary of sample sizes
```{r}
sI$n = 1
t = data.frame(
n = table(!is.na(sI$n), sI$country_arm)[1,],
neonatal_shedding = table(!is.na(sI$BS1_VP6_ct), sI$country_arm)[2,],
dose1_shedding = table(!is.na(sI$dose1_shedding), sI$country_arm)[2,],
dose2_shedding = table(!is.na(sI$dose2_shedding), sI$country_arm)[2,],
either_shedding = table(!is.na(sI$either_shedding), sI$country_arm)[2,],
seroconv = table(!is.na(sI$seroconv), sI$country_arm)[2,],
any_response = table(!is.na(sI$any_response), sI$country_arm)[2,])
t
rm(t)
```
##### Neonatal and vaccine shedding
```{r, fig.width=16, fig.height = 5}
p1 = fig_cat(sI, "BS1_VP6_cat", title="neonatal RV shedding,\nweek of life 1") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 0.75, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p2 = fig_cat(sI, "dose1_shedding", title="ORV shedding after dose 1,\nweek of life 7") + ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="***", y_position = 0.75, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_IPV_col)
p3 = fig_cat(sI, "either_shedding", title="ORV shedding after either dose,\nweek of life 7/11") + ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 0.75, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
grid.arrange(p1,p2,p3, ncol=4)
rm(p1,p2,p3)
```
#### Raw data and p values
##### Neonatal shedding
```{r}
outcome_summary(sI, "BS1_VP6_cat")
p_summary(sI, "BS1_VP6_cat")
```
##### Dose 1 shedding
```{r}
outcome_summary(sI, "dose1_shedding")
p_summary(sI, "dose1_shedding")
```
##### Dose 2 shedding
```{r}
outcome_summary(sI, "dose2_shedding")
p_summary(sI, "dose2_shedding")
```
##### Shedding after either dose
```{r}
outcome_summary(sI, "either_shedding")
p_summary(sI, "either_shedding")
```
##### Shedding after both doses
```{r}
outcome_summary(sI, "both_shedding")
p_summary(sI, "both_shedding")
```
##### Rota shedding (VP6) by time-point
```{r, fig.width=25, fig.height = 5}
p1 = fig_cat(sI, "MS1_VP6_cat", title="maternal") + ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 0.5, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p2 = fig_cat(sI, "BS1_VP6_cat", title="week of life 1") + ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 0.625, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p3 = fig_cat(sI, "BS2_VP6_cat", title="week of life 4") + ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 0.5, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p4 = fig_cat(sI, "BS3_VP6_cat", title="week of life 6") + ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 0.5, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p5 = fig_cat(sI, "BS4_VP6_cat", title="week of life 7") + ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="**", y_position = 0.5, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_IPV_col)
p6 = fig_cat(sI, "BS5_VP6_cat", title="week of life 10") + ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="*", y_position = 0.5, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_IPV_col)
p7 = fig_cat(sI, "BS6_VP6_cat", title="week of life 11") + ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="*", y_position = 0.5, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_OPV_col)
grid.arrange(p1,p2,p3,p4,p5,p6,p7, ncol=7)
rm(p1,p2,p3,p4,p5,p6,p7)
```
#### Raw data and p values
##### Maternal (MS1)
```{r}
outcome_summary(sI, "MS1_VP6_cat")
p_summary(sI, "MS1_VP6_cat")
```
##### Week 1 (BS1)
```{r}
outcome_summary(sI, "BS1_VP6_cat")
p_summary(sI, "BS1_VP6_cat")
```
##### Week 4 (BS2)
Not applicable - no shedding observed in IPV arm or OPV arm in week-4 samples.
##### Week 6/8 (BS3)
```{r}
outcome_summary(sI, "BS3_VP6_cat")
p_summary(sI, "BS3_VP6_cat")
```
##### Week 7/9 (BS4)
```{r}
outcome_summary(sI, "BS4_VP6_cat")
p_summary(sI, "BS4_VP6_cat")
```
##### Week 10/12 (BS5)
```{r}
outcome_summary(sI, "BS5_VP6_cat")
p_summary(sI, "BS5_VP6_cat")
```
##### Week 11/13 (BS6)
```{r}
outcome_summary(sI, "BS6_VP6_cat")
p_summary(sI, "BS6_VP6_cat")
```
##### Number of infants with birth shedding, dose 1 shedding, dose 2 shedding and post-vaccination IgA
```{r}
s1I = subset(sI, !is.na(BS1_VP6_cat) & !is.na(dose1_shedding) & !is.na(dose2_shedding) & !is.na(BB2_IgA))
table(s1I$country_arm)
```
#### Minimum NSP2 Ct among ORV shedders
```{r, fig.width=6, fig.height=5}
s2I = subset(s1I, either_shedding==1)
ggplot(s2I, aes(factor(country_arm), y=min_RV1_Ct, colour=factor(country_arm))) + geom_jitter(size = 1.5, alpha = 0.3, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape=NA) + ylab("minimum Ct") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c("IND (OPV)" = India_col, "IND (IPV)" = India_IPV_col)) +
scale_y_continuous(limits=c(15,42.5), breaks=c(20,25,30,35,40)) + scale_y_reverse() +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = -20, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
theme(text = element_text(size=18), axis.text = element_text(size=18), axis.title = element_text(size=20))
```
#### P value (pairwise Wilcoxon test)
```{r}
data.frame(comp = "OPV-IPV",
p = wilcox.test(s2I$min_RV1_Ct ~ s2I$country_arm)$p.value)
```
Immunogenicity by country/arm {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### Country
#### ORV immunogenicity - seroconversion and RV-IgA
```{r, fig.width=16, fig.height = 5}
p1 = fig_cat(s, "seroconv", title="\nseroconversion") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="**", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="**", y_position = 1.0625, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="ns", y_position = 1, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p2 = fig_cont_log10(subset(s, !is.na(BB1_IgA)), "BB1_IgA", "RV-IgA (IU/ml)") + geom_hline(yintercept = 20, linetype="dotted") + ggtitle("infant RV-IgA pre-ORV,\nweek of life 6") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 4, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 3.75, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 3.5, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col)
p3 = fig_cont_log10(subset(s, !is.na(BB2_IgA)), "BB2_IgA", "RV-IgA (IU/ml)") + geom_hline(yintercept = 20, linetype="dotted") + ggtitle("infant RV-IgA post-ORV,\nweek of life 14") +
ylab(" ") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 4, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="**", y_position = 3.75, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 3.5, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col)
grid.arrange(p1,p2,p3,ncol=4)
rm(p1,p2,p3)
```
#### ORV immunogenicity - seropositivity pre- and post-ORV
```{r, fig.width=8, fig.height = 5}
p4 = fig_cat(s, "BB1_IgA_cat", title="seropositive pre-ORV,\nweek of life 6") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 1.125, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 1.0625, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="***", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col)
p5 = fig_cat(s, "BB2_IgA_cat", title="seropositive post-ORV,\nweek of life 14") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 1.125, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="***", y_position = 1.0625, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("IND", "MLW")), annotations="***", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col)
grid.arrange(p4,p5,ncol=2)
rm(p4,p5)
```
#### Raw data and p values
##### Seroconversion
```{r}
outcome_summary(s, "seroconv")
p_summary(s, "seroconv")
```
##### Seropositivity, pre-ORV
```{r}
outcome_summary(s, "BB1_IgA_cat")
p_summary(s, "BB1_IgA_cat")
```
##### Seropositivity, post-ORV
```{r}
outcome_summary(s, "BB2_IgA_cat")
p_summary(s, "BB2_IgA_cat")
```
##### Infant RV-IgA pre-vaccination
```{r}
gmt_summary(s, "BB1_IgA")
p_summary_gmt(s, "BB1_IgA")
```
##### Infant RV-IgA post-vaccination
```{r}
gmt_summary(s, "BB2_IgA")
p_summary_gmt(s, "BB2_IgA")
```
##### Seropositivity by neonatal infection status in India
```{r}
table(sI$BS1_VP6_cat, sI$BB1_IgA_cat)
```
#### Pre-vaccination exposure by country (either seropositivity or week-1 shedding)
```{r}
table(s$pre_exposed, s$country)
```
#### ORV immunogenicity - final RV-IgA in seroconverters
```{r, fig.width=16, fig.height = 5}
s1 = subset(s, seroconv==1)
p1 = fig_cont_log10(subset(s1, !is.na(BB2_IgA)), "BB2_IgA", "RV-IgA (IU/ml)") + geom_hline(yintercept = 20, linetype="dotted") + ggtitle("infant RV-IgA post-ORV,\nweek of life 14 (sero+)") +
ylab(" ") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 4, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 3.75, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="ns", y_position = 3.5, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
grid.arrange(p1,ncol=4)
rm(p1)
```
##### Infant RV-IgA post-vaccination
```{r}
gmt_summary(s1, "BB2_IgA")
p_summary_gmt(s1, "BB2_IgA")
```
### Arm
#### ORV immunogenicity
```{r, fig.width=12, fig.height = 5}
p1 = fig_cat(sI, "seroconv", title="\nseroconversion") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 0.5, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p2 = fig_cont_log10(subset(sI, !is.na(BB1_IgA)), "BB1_IgA", "RV-IgA (IU/ml)") + geom_hline(yintercept = 20, linetype="dotted") + ggtitle("infant RV-IgA pre-ORV,\nweek of life 6") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 3.25, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p3 = fig_cont_log10(subset(sI, !is.na(BB2_IgA)), "BB2_IgA", "RV-IgA (IU/ml)") + geom_hline(yintercept = 20, linetype="dotted") + ggtitle("infant RV-IgA post-ORV,\nweek of life 14") +
ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 3.25, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
grid.arrange(p1,p2,p3,ncol=3)
rm(p1,p2,p3)
```
#### ORV immunogenicity - seropositivity pre- and post-ORV
```{r, fig.width=8, fig.height = 5}
p4 = fig_cat(sI, "BB1_IgA_cat", title="seropositive pre-ORV,\nweek of life 6") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 0.5, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p5 = fig_cat(sI, "BB2_IgA_cat", title="seropositive post-ORV,\nweek of life 14") +
ylab(" ") +
geom_signif(comparisons = list(c("IND (OPV)", "IND (IPV)")), annotations="ns", y_position = 0.75, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
grid.arrange(p4,p5,ncol=2)
rm(p4,p5)
```
#### Raw data and p values
##### Seroconversion
```{r}
outcome_summary(sI, "seroconv")
p_summary(sI, "seroconv")
```
##### Seropositivity, pre-ORV
```{r}
outcome_summary(sI, "BB1_IgA_cat")
p_summary(sI, "BB1_IgA_cat")
```
##### Seropositivity, post-ORV
```{r}
outcome_summary(sI, "BB2_IgA_cat")
p_summary(sI, "BB2_IgA_cat")
```
##### Infant RV-IgA pre-vaccination
```{r}
gmt_summary(sI, "BB1_IgA")
p_summary_gmt(sI, "BB1_IgA")
```
##### Infant RV-IgA post-vaccination
```{r}
gmt_summary(sI, "BB2_IgA")
p_summary_gmt(sI, "BB2_IgA")
```
RV-IgA vs cumulative shedding {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
##### Number of infants with birth shedding, dose 1 shedding, dose 2 shedding, pre-vaccination IgA and post-vaccination IgA
```{r}
s1 = subset(s, !is.na(BS1_VP6_cat) & !is.na(dose1_shedding) & !is.na(dose2_shedding) & !is.na(BB1_IgA) & !is.na(BB2_IgA))
table(s1$country)
```
#### Cumulative effect of birth exposure and vaccination on IgA titre
Exposure defined as week-1 shedding or IgA positivity pre-vaccination. Shedding subgroups with <5 infants excluded.
```{r, fig.width=8, fig.height=3}
# create new variable for birth/vaccine shedding group
s1$shedding_group = NA
s1$shedding_group[(s1$BS1_VP6_cat==0 & s1$BB1_IgA_cat==0) & s1$either_shedding==0] = "--"
s1$shedding_group[(s1$BS1_VP6_cat==0 & s1$BB1_IgA_cat==0) & s1$either_shedding==1] = "-+"
s1$shedding_group[(s1$BS1_VP6_cat==1 | s1$BB1_IgA_cat==1) & s1$either_shedding==0] = "+-"
s1$shedding_group[(s1$BS1_VP6_cat==1 | s1$BB1_IgA_cat==1) & s1$either_shedding==1] = "++"
table(s1$country, s1$shedding_group)
```
```{r, fig.width=12, fig.height=5}
# create plot for India, including all shedding subgroups with ≥5 infants
s1I = subset(s1, country=="IND" & !is.na(shedding_group) & !is.na(BB2_IgA))
p1 = ggplot(s1I, aes(factor(shedding_group), y=BB2_IgA, colour=factor(shedding_group))) + geom_jitter(size = 1.5, alpha = 0.3, width=0.3, colour=India_col) +
geom_boxplot(alpha = 0.5, colour=India_col, outlier.shape=NA) + ylab("RV-IgA (IU/ml)") + xlab("") +
theme(legend.position = "none", strip.background = element_blank()) + geom_hline(yintercept = 20, linetype="dotted") +
# scale_colour_manual(values = c("#d4b9da","#df65b0", "#ce1256", "#91003f")) +
scale_y_continuous(trans="log10", breaks = c(1,10,100,1000), limits = c(1,35000), labels = trans_format("log10", math_format(10^.x))) +
geom_signif(comparisons = list(c("++", "--")), annotations="***", y_position = 4.5, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col) +
geom_signif(comparisons = list(c("++", "-+")), annotations="***", y_position = 4.25, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col) +
geom_signif(comparisons = list(c("++", "+-")), annotations="**", y_position = 4, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col) +
geom_signif(comparisons = list(c("+-", "--")), annotations="***", y_position = 3.75, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col) +
geom_signif(comparisons = list(c("+-", "-+")), annotations="***", y_position = 3.5, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=India_col) +
geom_signif(comparisons = list(c("-+", "--")), annotations="ns", y_position = 3.25, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
ggtitle("IND") + theme(plot.title = element_text(size=18, hjust = 0.5)) +
theme(text = element_text(size=18), axis.text = element_text(size=18), axis.title = element_text(size=20))
# create plot for Malawi, including all shedding subgroups with ≥5 infants
s1M = subset(s1, country=="MLW" & (shedding_group=="--" | shedding_group=="-+") & !is.na(BB2_IgA))
p2 = ggplot(s1M, aes(factor(shedding_group), y=BB2_IgA, colour=factor(shedding_group))) + geom_jitter(size = 1.5, alpha = 0.3, width=0.3, colour = Malawi_col) +
geom_boxplot(alpha = 0.5, colour = Malawi_col, outlier.shape=NA) + ylab("") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) + geom_hline(yintercept = 20, linetype="dotted") +
scale_y_continuous(trans="log10", breaks = c(1,10,100,1000), limits = c(1,35000), labels = trans_format("log10", math_format(10^.x))) +
geom_signif(comparisons = list(c("-+", "--")), annotations="*", y_position = 3.5, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col) +
ggtitle("MLW")+ theme(plot.title = element_text(size=18, hjust = 0.5)) +
theme(text = element_text(size=18), axis.text = element_text(size=18), axis.title = element_text(size=20))
# create plot for UK, including all shedding subgroups with ≥5 infants
s1U = subset(s1, country=="UK" & shedding_group=="-+" & !is.na(BB2_IgA))
p3 = ggplot(s1U, aes(factor(shedding_group), y=BB2_IgA, colour=factor(shedding_group))) + geom_jitter(size = 1.5, alpha = 0.3, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape=NA) + ylab("") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) + geom_hline(yintercept = 20, linetype="dotted") +
scale_colour_manual(values = c(UK_col)) +
scale_y_continuous(trans="log10", breaks = c(1,10,100,1000), limits = c(1,35000), labels = trans_format("log10", math_format(10^.x))) +
ggtitle("UK") +theme(plot.title = element_text(size=18, hjust = 0.5)) +
theme(text = element_text(size=18), axis.text = element_text(size=18), axis.title = element_text(size=20))
grid.arrange(p1,p2,p3, widths=c(4,2.5,1.5), ncol=3)
rm(p1,p2,p3)
```
#### P values
##### India
```{r}
TukeyHSD(x=aov(log10(s1I$BB2_IgA) ~ s1I$shedding_group), "s1I$shedding_group", conf.level=0.95)[1]
```
##### Malawi
```{r}
TukeyHSD(x=aov(log10(s1M$BB2_IgA) ~ s1M$shedding_group), "s1M$shedding_group", conf.level=0.95)[1]
rm(s1I, s1M, s1U)
```
Maternal Abs by country/arm/exposure {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### Country
#### Number of Indian infants with MB IgA, MB IgG, CB IgG, BB1 IgG and no pre-vaccination RV exposure
```{r}
t = subset(s, country=="IND" & !is.na(MB1_IgA) & !is.na(MB1_IgG) & !is.na(CB1_IgG) & !is.na(BB1_IgG) & pre_exposed==0)
nrow(t)
```
#### Pearson's correlation coefficient for MB RV-IgA and MB RV-IgG (log transformed)
```{r}
cor.test(log(t$MB1_IgA), log(t$MB1_IgG), method="pearson")
```
#### Pearson's correlation coefficient for MB RV-IgA and BM RV-IgA (log transformed)
```{r}
cor.test(log(t$MB1_IgA), log(t$BM1_IgA), method="pearson")
```
#### Maternal IgA antibody levels
```{r, fig.width=12, fig.height = 5}
p1 = fig_cont_log10(subset(s, !is.na(MB1_IgA)), "MB1_IgA", "IU/ml") + geom_hline(yintercept = 20, linetype="dotted") +
ggtitle("maternal RV-IgA,\ninfant week of life 1") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 4, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="*", y_position = 3.75, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col) +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 3.5, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col)
p2 = fig_cont_log10(subset(s, !is.na(BM1_IgA)), "BM1_IgA", " ") + geom_hline(yintercept = 20, linetype="dotted") +
ggtitle("breastmilk RV-IgA,\ninfant week of life 1") +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 4, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 3.75, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 3.5, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col)
s1 = subset(s, !is.na(MB1_IgA) & !is.na(BM1_IgA))
s1$mat_ratio = log(s1$BM1_IgA)/log(s1$MB1_IgA)
p3 = ggplot(s1, aes(factor(country), y=mat_ratio, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.3, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape=NA) + ylab("log(IgA) ratio") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c(India_col, Malawi_col, UK_col)) +
scale_y_continuous(trans="log10", breaks = c(0.1,1,10), limits=c(0.1,20)) +
ggtitle("breastmilk/serum ratio,\nmaternal RV-IgA") + theme(plot.title = element_text(size=18, hjust = 0.5)) +
theme(plot.title = element_text(size=18, hjust = 0.5)) +
theme(text = element_text(size=18), axis.text = element_text(size=18), axis.title = element_text(size=20)) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 1.3, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=UK_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 1.15, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey") +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 1, tip_length=0, vjust=0.5, size = 0.5, textsize=8, color=Malawi_col)
grid.arrange(p1,p2,p3,ncol=3)
#rm(p1,p2)
```
#### Raw data and p values
##### Maternal RV-IgA
```{r}
gmt_summary(s, "MB1_IgA")
p_summary_gmt(s, "MB1_IgA")
```
##### Breastmilk RV-IgA
```{r}
gmt_summary(s, "BM1_IgA")
p_summary_gmt(s, "BM1_IgA")
```
##### Serum/breastmilk RV-IgA ratio
```{r}
gmt_summary(s1, "mat_ratio")
dunnTest(mat_ratio ~ country, data = s1, method="bh")
```
### Arm
#### Antibody levels
```{r, fig.width=16, fig.height = 5}
p1 = fig_cont_log10(subset(sI, !is.na(MB1_IgA)), "MB1_IgA", "IU/ml") + geom_hline(yintercept = 20, linetype="dotted") +
ggtitle("maternal RV-IgA,\ninfant week of life 1") +
geom_signif(comparisons = list(c("IND (IPV)", "IND (OPV)")), annotations="ns", y_position = 4, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p2 = fig_cont_log10(subset(sI, !is.na(BM1_IgA)), "BM1_IgA", " ") + geom_hline(yintercept = 20, linetype="dotted") +
ggtitle("breastmilk RV-IgA,\ninfant week of life 1") +
geom_signif(comparisons = list(c("IND (IPV)", "IND (OPV)")), annotations="ns", y_position = 4, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p3 = fig_cont_log10(subset(sI, !is.na(MB1_IgG)), "MB1_IgG", " ") +
ggtitle("maternal RV-IgG,\ninfant week of life 1") +
scale_y_continuous(trans="log10", breaks = c(0,1,10,100,1000,10000), labels = trans_format("log10", math_format(10^.x))) +
geom_signif(comparisons = list(c("IND (IPV)", "IND (OPV)")), annotations="ns", y_position = 5, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
p4 = fig_cont_log10(subset(sI, !is.na(CB1_IgG)), "CB1_IgG", " ") +
ggtitle("\ncord blood RV-IgG,\ninfant week of life 0") +
scale_y_continuous(trans="log10", breaks = c(0,1,10,100,1000,10000), labels = trans_format("log10", math_format(10^.x))) +
geom_signif(comparisons = list(c("IND (IPV)", "IND (OPV)")), annotations="ns", y_position = 5, tip_length=0, vjust=0, size = 0.5, textsize=5, color="grey")
grid.arrange(p1,p2,p3,p4,ncol=4)
rm(p1,p2,p3,p4)
```
#### Raw data and p values
##### Maternal RV-IgA
```{r}
gmt_summary(sI, "MB1_IgA")
p_summary_gmt(sI, "MB1_IgA")
```
##### Breastmilk RV-IgA
```{r}
gmt_summary(sI, "BM1_IgA")
p_summary_gmt(sI, "BM1_IgA")
```
##### Maternal RV-IgG
```{r}
gmt_summary(sI, "MB1_IgG")
p_summary_gmt(sI, "MB1_IgG")
```
##### Cord-blood RV-IgG
```{r}
gmt_summary(sI, "CB1_IgG")
p_summary_gmt(sI, "CB1_IgG")
```
### Exposure status
#### Antibody levels
```{r, fig.width=16, fig.height = 5}
sI = subset(sI, pre_exposed==1 | pre_exposed==0)
p1 = fig_cont_log10(subset(sI, !is.na(MB1_IgA)), "MB1_IgA", "IU/ml", exposure=TRUE) + geom_hline(yintercept = 20, linetype="dotted") +
ggtitle("maternal RV-IgA,\ninfant week of life 1") +
geom_signif(comparisons = list(c("IND (neo+)", "IND (neo-)")), annotations="ns", y_position = 4, tip_length=0, vjust=0, size = 0.3, textsize=5, color="grey")
p2 = fig_cont_log10(subset(sI, !is.na(BM1_IgA)), "BM1_IgA", " ", exposure=TRUE) + geom_hline(yintercept = 20, linetype="dotted") +
ggtitle("breastmilk RV-IgA,\ninfant week of life 1") +
geom_signif(comparisons = list(c("IND (neo+)", "IND (neo-)")), annotations="ns", y_position = 4, tip_length=0, vjust=0, size = 0.3, textsize=5, color="grey")
p3 = fig_cont_log10(subset(sI, !is.na(MB1_IgG)), "MB1_IgG", " ", exposure=TRUE) +
ggtitle("maternal RV-IgG,\ninfant week of life 1") +
scale_y_continuous(trans="log10", breaks = c(0,1,10,100,1000,10000), labels = trans_format("log10", math_format(10^.x))) +
geom_signif(comparisons = list(c("IND (neo+)", "IND (neo-)")), annotations="ns", y_position = 5, tip_length=0, vjust=0, size = 0.3, textsize=5, color="grey")
p4 = fig_cont_log10(subset(sI, !is.na(CB1_IgG)), "CB1_IgG", " ", exposure=TRUE) +
ggtitle("cord blood RV-IgG,\ninfant week of life 0") +
scale_y_continuous(trans="log10", breaks = c(0,1,10,100,1000,10000), labels = trans_format("log10", math_format(10^.x))) +
geom_signif(comparisons = list(c("IND (neo+)", "IND (neo-)")), annotations="ns", y_position = 5, tip_length=0, vjust=0, size = 0.3, textsize=5, color="grey")
grid.arrange(p1,p2,p3,p4,ncol=4)
rm(p1,p2,p3,p4)
```
#### Raw data and p values
##### Maternal RV-IgA
```{r}
gmt_summary(sI, "MB1_IgA", exposure=TRUE)
p_summary_gmt(sI, "MB1_IgA", exposure=TRUE)
```
##### Breastmilk RV-IgA
```{r}
gmt_summary(sI, "BM1_IgA", exposure=TRUE)
p_summary_gmt(sI, "BM1_IgA", exposure=TRUE)
```
##### Maternal RV-IgG
```{r}
gmt_summary(sI, "MB1_IgG", exposure=TRUE)
p_summary_gmt(sI, "MB1_IgG", exposure=TRUE)
```
##### Cord-blood RV-IgG
```{r}
gmt_summary(sI, "CB1_IgG", exposure=TRUE)
p_summary_gmt(sI, "CB1_IgG", exposure=TRUE)
```
Maternal Abs vs ORV outcome {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### Summary
See *Module 5* for full univariate and multivariate analyses.
### Correlation networks
#### India
#### Correlation between of MB1 IgA and CB IgG
```{r}
s_sub = subset(s, country=="IND")
cor.test(log(s_sub$MB1_IgA), log(s_sub$CB1_IgG), method="pearson")
```
#### Correlation between of MB1 IgA and BM IgA
```{r}
cor.test(log(s_sub$MB1_IgA), log(s_sub$BM1_IgA), method="pearson")
```
#### Full correlation network
```{r, fig.width=8, fig.height=8, warning=FALSE}
s_Ig = s_sub[,c("MB1_IgG", "CB1_IgG", "MB1_IgA", "BM1_IgA", "BB1_IgA", "BB2_IgA", "BS1_VP6_quant", "BS4_nsp2_quant")]
s_Ig = s_Ig[complete.cases(s_Ig),]
col = colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
p.mat = cor.mtest(s_Ig, method="spearman")
m = cor(s_Ig, method="spearman")
colnames(m) = rownames(m) = c("maternal RV-IgG","cord blood RV-IgG","maternal RV-IgA","breastmilk RV-IgA","infant RV-IgA, pre-ORV", "infant RV-IgA, post-ORV","RV shedding, week of life 1","post-ORV shedding, dose 1")
col2 <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE", "#4393C3", "#2166AC", "#053061"))
corrplot(m, method="color", type = "upper", order = "original", tl.col="black", col=col2(200),
p.mat = p.mat[[1]], insig = "label_sig", tl.cex = 1.2, cl.cex = 1.1, pch.cex = 1.5, diag = FALSE,
cl.length = 5, addgrid.col = "white",
sig.level = c(.0005, .005, .05), pch.col = "black")
#rm(s_sub, p.mat, m)
```
Number with complete antibody data = `r nrow(s_Ig)`
#### India (exposed subset)
```{r, fig.width=8, fig.height=8, warning=FALSE}
s_sub = subset(s, country=="IND" & pre_exposed==1)
s_Ig = s_sub[,c("MB1_IgG", "CB1_IgG", "MB1_IgA", "BM1_IgA", "BB1_IgA", "BB2_IgA", "BS1_VP6_quant", "BS4_nsp2_quant")]
s_Ig = s_Ig[complete.cases(s_Ig),]
p.mat = cor.mtest(s_Ig, method="spearman")
m = cor(s_Ig, method="spearman")
colnames(m) = rownames(m) = c("maternal RV-IgG","cord blood RV-IgG","maternal RV-IgA","breastmilk RV-IgA","infant RV-IgA, pre-ORV", "infant RV-IgA, post-ORV","RV shedding, week of life 1","post-ORV shedding, dose 1")
corrplot(m, method="color", type = "upper", order = "original", tl.col="black", col=col2(200),
p.mat = p.mat[[1]], insig = "label_sig", tl.cex = 1.2, cl.cex = 1.1, pch.cex = 1.5, diag = FALSE,
cl.length = 5, addgrid.col = "white",
sig.level = c(.0005, .005, .05), pch.col = "black")
rm(s_sub, p.mat, m)
```
Number with complete antibody data = `r nrow(s_Ig)`
#### India (unexposed subset)
```{r, fig.width=8, fig.height=8, warning=FALSE}
s_sub = subset(s, country=="IND" & pre_exposed==0)
s_Ig = s_sub[,c("MB1_IgG", "CB1_IgG", "MB1_IgA", "BM1_IgA", "BB1_IgA", "BB2_IgA", "BS4_nsp2_quant")]
s_Ig = s_Ig[complete.cases(s_Ig),]
p.mat = cor.mtest(s_Ig, method="spearman")
m = cor(s_Ig, method="spearman")
colnames(m) = rownames(m) = c("maternal RV-IgG","cord blood RV-IgG","maternal RV-IgA","breastmilk RV-IgA","infant RV-IgA, pre-ORV", "infant RV-IgA, post-ORV", "post-ORV shedding, dose 1")
corrplot(m, method="color", type = "upper", order = "original", tl.col="black", col=col2(200),
p.mat = p.mat[[1]], insig = "label_sig", tl.cex = 1.2, cl.cex = 1.1, pch.cex = 1.5, diag = FALSE,
cl.length = 5, addgrid.col = "white",
sig.level = c(.0005, .005, .05), pch.col = "black")
rm(s_sub, p.mat, m)
```
Number with complete antibody data = `r nrow(s_Ig)`
#### Malawi
#### Full correlation network
```{r, fig.width=7, fig.height=6.5, warning=FALSE}
s_sub = subset(s, country=="MLW")
s_Ig = s_sub[,c("MB1_IgA","BM1_IgA", "BB1_IgA","BB2_IgA","BS4_nsp2_quant")]
s_Ig = s_Ig[complete.cases(s_Ig),]
p.mat = cor.mtest(s_Ig, method="spearman")
m = cor(s_Ig, method="spearman")
colnames(m) = rownames(m) = c("maternal RV-IgA","breastmilk RV-IgA","infant RV-IgA, pre-ORV","infant RV-IgA, post-ORV","post-ORV shedding, dose 1")
corrplot(m, method="color", type = "upper", order = "original", tl.col="black", col=col2(200),
p.mat = p.mat[[1]], insig = "label_sig", tl.cex = 1.2, cl.cex = 1.1, pch.cex = 1.5, diag = FALSE,
cl.length = 5, addgrid.col = "white",
sig.level = c(.0005, .005, .05), pch.col = "black")
```
#### Correlation between of MB1 IgA and BM IgA
```{r}
cor.test(log(s_sub$MB1_IgA), log(s_sub$BM1_IgA), method="pearson")
rm(s_sub, p.mat, m)
```
Number with complete antibody data = `r nrow(s_Ig)`
#### UK
#### Full correlation network
```{r, fig.width=6, fig.height=6, warning=FALSE}
s_sub = subset(s, country=="UK")
s_Ig = s_sub[,c("MB1_IgA", "BB1_IgA","BB2_IgA","BS4_nsp2_quant")]
s_Ig = s_Ig[complete.cases(s_Ig),]
p.mat = cor.mtest(s_Ig, method="spearman")
m = cor(s_Ig, method="spearman")
colnames(m) = rownames(m) = c("maternal RV-IgA","infant RV-IgA, pre-ORV","infant RV-IgA, post-ORVV","post-ORV shedding, dose 1")
corrplot(m, method="color", type = "upper", order = "original", tl.col="black", col=col2(200),
p.mat = p.mat[[1]], insig = "label_sig", tl.cex = 1.2, cl.cex = 1.1, pch.cex = 1.5, diag = FALSE,
cl.length = 5, addgrid.col = "white",
sig.level = c(.0005, .005, .05), pch.col = "black")
```
Number with complete antibody data = `r nrow(s_Ig)` (breastmilk IgA data excluded as this reduced the number of complete cases from 40 to 22).
#### Correlation between of MB1 IgA and BM IgA
```{r}
cor.test(log(s_sub$MB1_IgA), log(s_sub$BM1_IgA), method="pearson")
rm(s_sub, p.mat, m)
```
### Continuous outcomes
#### Pearson correlation coefficients: maternal antibodies vs pre-vaccination RV-IgA
```{r, fig.width=2.5, fig.height=4}
results = data.frame(India_neo_pos = corr_by_country("BB1_IgA", "IND (neo+)", "corr"))
results = mutate_all(results, function(x) as.numeric(as.character(x)))
pvals = data.frame(India_neo_pos = corr_by_country("BB1_IgA", "IND (neo+)", "p"))
pvals = mutate_all(pvals, function(x) as.numeric(as.character(x)))
cor_n = data.frame(India_neo_pos = corr_by_country("BB2_IgA", "IND (neo+)", "n"))
cor_n = mutate_all(cor_n, function(x) as.numeric(as.character(x)))
rownames(results) = rownames(pvals) = c("MB1 RV-IgA", "BM1 RV-IgA", "BB1 RV-IgA", "MB1 RV-IgG", "CB1 RV-IgG", "BB1 RV-IgG")
pheatmap(results, cluster_rows = F, cluster_cols = F, color = col2(200), breaks = seq(-1, 1, by = 0.01))
```
##### Pearson correlation coefficients
```{r}