1. TSP antibody experiment

1.1 TSP antibody model


antiTSP_mod_data <- read.csv('Figure 5A source data1 model.csv', stringsAsFactors = F) %>% 
  mutate(Infection = infection+0.01,
       anemone = paste(Anemone,hours,Treatment),
       Hours = as.factor(hours)) 

# run mixed effects model - nlme for contrast methods
antiTSP_model_test <- lme(log(Infection)~ Hours*Treatment,
                          random=~1|anemone,
                          data=antiTSP_mod_data)

plot(antiTSP_model_test)


sm_test <- summary(antiTSP_model_test)
anova(antiTSP_model_test, type='m')

1.2 TSP antibody model contrasts at time points

These are contrasts between the FSW and IGG controls and the anti-TSR treatment


antiTSP_lsmobj <- lsmeans(antiTSP_model_test, ~ Treatment|Hours)


summary(as.glht(pairs(antiTSP_lsmobj),by=NULL))
confint(pairs(antiTSP_lsmobj))

1.3 TSP antibody model plot

# summary file of means and SE per treatment and time-point
antiTSP <- read.csv('Figure 5A source data2.csv', stringsAsFactors = F)

antiTSP$Treatment[antiTSP$Treatment == 'TSP antibody'] <- 'Thrombospondin antibody'


ggf1 <- ggplot(antiTSP,aes(x=Hours,y=X..Infection,colour = Treatment, shape = Treatment)) + 
  geom_line() + 
  geom_point() +
  geom_pointrange(aes(ymin = X..Infection - se, ymax = X..Infection + se)) +
  geom_text(data=antiTSP,aes(x=Hours, y=X..Infection+se+0.5,label=c(NA,NA,NA,NA,NA,'**',NA,NA,'***',NA,NA,'***',NA,NA,'***')),show.legend = FALSE)+
  #scale_y_continuous(labels = percent)+
  scale_x_continuous(breaks = c(0,24,48,72,96,120))+
  ylab('') +
  scale_colour_manual('Thrombospondin antibody',values = c("#56B4E9","#E69F00",  "#009E73")) +
  scale_shape_discrete('Thrombospondin antibody')+
  theme_cowplot()+ 
  theme(legend.title = element_text(colour = NA),
        axis.title.x = element_text(colour = NA),
        axis.text.x = element_text(colour = NA))

2. Soluable TSP experiment

2.1 Soluable TSP model


tsp_soluble_mod_data <- read.csv('Figure 5B source data1 model.csv', stringsAsFactors = F) %>% 
  mutate(Infection = infection+0.01,
         anemone = paste(Anemone,hours,Treatment),
         Hours = as.factor(hours)) 

# run mixed effects model
tsp_soluble_model_test <- lme(log(Infection)~ Hours*Treatment,
                              random=~1|anemone,
                              data=tsp_soluble_mod_data)


plot(tsp_soluble_model_test)

# summaries
sm_test <- summary(tsp_soluble_model_test)
anova(tsp_soluble_model_test, type='m')

2.2 Soluable TSP model contrasts


tsp_soluble_lsmobj <- lsmeans(tsp_soluble_model_test, ~ Treatment|Hours)

summary(as.glht(pairs(tsp_soluble_lsmobj),by=NULL))
confint(pairs(tsp_soluble_lsmobj),by=NULL)

2.3 Soluable TSP plot

# summary file of means and SE per treatment and time-point
tsp_soluble <- read.csv('Figure 5B source data2.csv', stringsAsFactors = F)

tsp_soluble$Treatment[tsp_soluble$Treatment == 'Hs TSP-1'] <- 'Hs TSR'


ggf2 <- ggplot(tsp_soluble,aes(x=Hours, 
                       y=X..Infection, 
                       colour = Treatment, shape = Treatment)) + 
  geom_line() + 
  geom_point() +
  geom_pointrange(aes(ymin = X..Infection - se, ymax = X..Infection + se)) +
  geom_text(data=tsp_soluble,aes(x=Hours, y=X..Infection+se+c(rep(0.5,5),1.5,rep(0.5,4)),label=c(NA,NA,NA,'***',NA,NA,NA,'**',NA,NA)),show.legend = FALSE)+
  #scale_y_continuous(labels = percent)+
  scale_x_continuous(breaks = c(0,24,48,72,96,120))+
  ylab('% colonisation') +
  scale_colour_manual('Thrombospondin antibody',values = c("#56B4E9","#CC79A7")) +
  scale_shape_discrete('Thrombospondin antibody')+
  theme_cowplot()+ 
  theme(legend.title = element_text(colour = NA),
        axis.title.x = element_text(colour = NA),
        axis.text.x = element_text(colour = NA))

3 TSR peptide colonisation

3.1 TSR peptide colonisation

sm_test <- summary(peptide_model_test)
anova(peptide_model_test,type='marginal')

3.2 Peptide contrasts

                   
peptide_lsmobj <- lsmeans(peptide_model_test, ~ Treatment|Hours)

summary(as.glht(pairs(peptide_lsmobj),by=NULL))
confint(pairs(peptide_lsmobj), by=NULL)

3.3 Plot peptide colonisation

peptide_infection <- read.csv('Figure 5C source data2.csv')

ggf3 <- ggplot(peptide_infection,aes(x=Hours,y=X..Infection,colour = Treatment, shape = Treatment)) + 
  geom_line() + 
  geom_point() +
  geom_pointrange(aes(ymin = X..Infection - se, ymax = X..Infection + se)) +
  geom_text(data=peptide_infection,aes(x=Hours, y=X..Infection+se+0.5,label=c(NA,NA,NA,NA,NA,'***',NA,NA,'***',NA,NA,'***',NA,NA,NA)),show.legend = FALSE)+
  geom_text(data=peptide_infection,aes(x=Hours, y=X..Infection-se-c(rep(1,nrow(peptide_infection)-2),0.1,NA),label=c(NA,NA,NA,NA,'***',NA,NA,'***',NA,NA,'***',NA,NA,NA,NA)),nudge_y = -0.2,show.legend = FALSE)+
  #scale_y_continuous(labels = percent)+
  scale_x_continuous(breaks = c(24,48,72,96,120))+
  ylab('') +
  scale_colour_manual('Thrombospondin antibody',values = c("#56B4E9", "#0072B2", "#D55E00")) +
  scale_shape_discrete('Thrombospondin antibody')+
  theme_cowplot()+ 
  theme(legend.title = element_text(colour = NA))

4. Plots

all_figures <- cowplot::plot_grid(ggf1,ggf2,ggf3,ncol=1)
all_figures
ggsave(all_figures,filename = 'Figure 5.pdf',width = 6,height=8)

5. qPCR analysis

qPCR <- read.csv('Figure 6 source data.csv',
                 stringsAsFactors = F)

qPCR$Time <- as.factor(qPCR$Time)
qPCR$Symbiont <- as.factor(qPCR$Symbiont)

5.1 Ap_Sema5 analysis

Semamod<-lm(Sema~oral_disc+Symbiont*Time, data = qPCR)
anova(Semamod)

Semamod_lsmobj <- lsmeans(Semamod, ~ Symbiont|Time)
summary(as.glht(pairs(Semamod_lsmobj),by=NULL))
confint(pairs(Semamod_lsmobj),by=NULL)

5.2 Ap_Trypsin-like analysis


Trypsinmod<-lm(Trypsin~oral_disc+Symbiont*Time, data = qPCR)
anova(Trypsinmod)

Trypsinmod_lsmobj <- lsmeans(Trypsinmod, ~ Symbiont|Time)
summary(as.glht(pairs(Trypsinmod_lsmobj),by=NULL))
confint(pairs(Trypsinmod_lsmobj),by=NULL)

---
title: "Statistical analyses"
output:
  html_notebook: default
  html_document: default
  pdf_document: default
---


```{r, echo=FALSE}
knitr::opts_chunk$set(warning = F, message = F)
```

```{r, echo=FALSE}
# reproducibility
require(checkpoint)
checkpoint('2017-03-23')

# building the document
require(knitr)
require(rmarkdown)
require(formatR)

# data and statistics 
require(nlme)
require(lsmeans)
require(multcomp)
require(dplyr)

# plotting
require(lattice)
require(cowplot)


```

# 1. TSP antibody experiment

## 1.1 TSP antibody model
```{r TSP_antibody_model}

antiTSP_mod_data <- read.csv('Figure 5A source data1 model.csv', stringsAsFactors = F) %>% 
  mutate(Infection = infection+0.01,
       anemone = paste(Anemone,hours,Treatment),
       Hours = as.factor(hours)) 

# run mixed effects model - nlme for contrast methods
antiTSP_model_test <- lme(log(Infection)~ Hours*Treatment,
                          random=~1|anemone,
                          data=antiTSP_mod_data)

plot(antiTSP_model_test)

```

```{r summaries_TSP}

sm_test <- summary(antiTSP_model_test)
anova(antiTSP_model_test, type='m')
```

## 1.2 TSP antibody model contrasts at time points

These are contrasts between the FSW and IGG controls and the anti-TSR treatment

```{r contrasts_TSP}

antiTSP_lsmobj <- lsmeans(antiTSP_model_test, ~ Treatment|Hours)


summary(as.glht(pairs(antiTSP_lsmobj),by=NULL))
confint(pairs(antiTSP_lsmobj))

```

## 1.3 TSP antibody model plot

```{r generate_plot_TSP}
# summary file of means and SE per treatment and time-point
antiTSP <- read.csv('Figure 5A source data2.csv', stringsAsFactors = F)

antiTSP$Treatment[antiTSP$Treatment == 'TSP antibody'] <- 'Thrombospondin antibody'


ggf1 <- ggplot(antiTSP,aes(x=Hours,y=X..Infection,colour = Treatment, shape = Treatment)) + 
  geom_line() + 
  geom_point() +
  geom_pointrange(aes(ymin = X..Infection - se, ymax = X..Infection + se)) +
  geom_text(data=antiTSP,aes(x=Hours, y=X..Infection+se+0.5,label=c(NA,NA,NA,NA,NA,'**',NA,NA,'***',NA,NA,'***',NA,NA,'***')),show.legend = FALSE)+
  #scale_y_continuous(labels = percent)+
  scale_x_continuous(breaks = c(0,24,48,72,96,120))+
  ylab('') +
  scale_colour_manual('Thrombospondin antibody',values = c("#56B4E9","#E69F00",  "#009E73")) +
  scale_shape_discrete('Thrombospondin antibody')+
  theme_cowplot()+ 
  theme(legend.title = element_text(colour = NA),
        axis.title.x = element_text(colour = NA),
        axis.text.x = element_text(colour = NA))

```

# 2. Soluable TSP experiment

## 2.1 Soluable TSP model

```{r Soluable_TSP_model}

tsp_soluble_mod_data <- read.csv('Figure 5B source data1 model.csv', stringsAsFactors = F) %>% 
  mutate(Infection = infection+0.01,
         anemone = paste(Anemone,hours,Treatment),
         Hours = as.factor(hours)) 

# run mixed effects model
tsp_soluble_model_test <- lme(log(Infection)~ Hours*Treatment,
                              random=~1|anemone,
                              data=tsp_soluble_mod_data)


plot(tsp_soluble_model_test)
```

```{r sol_TSP_summaries}
# summaries
sm_test <- summary(tsp_soluble_model_test)
anova(tsp_soluble_model_test, type='m')
```

## 2.2 Soluable TSP model contrasts

```{r contrasts_sol_TSP}

tsp_soluble_lsmobj <- lsmeans(tsp_soluble_model_test, ~ Treatment|Hours)

summary(as.glht(pairs(tsp_soluble_lsmobj),by=NULL))
confint(pairs(tsp_soluble_lsmobj),by=NULL)

```

## 2.3 Soluable TSP plot

```{r generate_plot_sol_TSP}
# summary file of means and SE per treatment and time-point
tsp_soluble <- read.csv('Figure 5B source data2.csv', stringsAsFactors = F)

tsp_soluble$Treatment[tsp_soluble$Treatment == 'Hs TSP-1'] <- 'Hs TSR'


ggf2 <- ggplot(tsp_soluble,aes(x=Hours, 
                       y=X..Infection, 
                       colour = Treatment, shape = Treatment)) + 
  geom_line() + 
  geom_point() +
  geom_pointrange(aes(ymin = X..Infection - se, ymax = X..Infection + se)) +
  geom_text(data=tsp_soluble,aes(x=Hours, y=X..Infection+se+c(rep(0.5,5),1.5,rep(0.5,4)),label=c(NA,NA,NA,'***',NA,NA,NA,'**',NA,NA)),show.legend = FALSE)+
  #scale_y_continuous(labels = percent)+
  scale_x_continuous(breaks = c(0,24,48,72,96,120))+
  ylab('% colonisation') +
  scale_colour_manual('Thrombospondin antibody',values = c("#56B4E9","#CC79A7")) +
  scale_shape_discrete('Thrombospondin antibody')+
  theme_cowplot()+ 
  theme(legend.title = element_text(colour = NA),
        axis.title.x = element_text(colour = NA),
        axis.text.x = element_text(colour = NA))
```

# 3 TSR peptide colonisation

## 3.1 TSR peptide colonisation

```{r peptide_model}
peptide_mod_data <- read.csv('Figure 5C source data1 model.csv', stringsAsFactors = F) %>% 
  mutate(Infection = infection+0.01,
         anemone = paste(Anemone,hours,Treatment),
         Hours = as.factor(hours)) 

peptide_mod_data$Treatment <- as.factor(peptide_mod_data$Treatment)
peptide_mod_data$Hours <- as.factor(peptide_mod_data$Hours)

# run mixed effects model
peptide_model_test <- lme(log(Infection)~ Hours*Treatment,
                     random=~1|anemone,
                      data=peptide_mod_data)

plot(peptide_model_test)
```

```{r summaries}
sm_test <- summary(peptide_model_test)
anova(peptide_model_test,type='marginal')
```

## 3.2 Peptide contrasts

```{r peptide_contrasts}
                   
peptide_lsmobj <- lsmeans(peptide_model_test, ~ Treatment|Hours)

summary(as.glht(pairs(peptide_lsmobj),by=NULL))
confint(pairs(peptide_lsmobj), by=NULL)
                   
```

## 3.3 Plot peptide colonisation

```{r plot_peptides}
peptide_infection <- read.csv('Figure 5C source data2.csv')

ggf3 <- ggplot(peptide_infection,aes(x=Hours,y=X..Infection,colour = Treatment, shape = Treatment)) + 
  geom_line() + 
  geom_point() +
  geom_pointrange(aes(ymin = X..Infection - se, ymax = X..Infection + se)) +
  geom_text(data=peptide_infection,aes(x=Hours, y=X..Infection+se+0.5,label=c(NA,NA,NA,NA,NA,'***',NA,NA,'***',NA,NA,'***',NA,NA,NA)),show.legend = FALSE)+
  geom_text(data=peptide_infection,aes(x=Hours, y=X..Infection-se-c(rep(1,nrow(peptide_infection)-2),0.1,NA),label=c(NA,NA,NA,NA,'***',NA,NA,'***',NA,NA,'***',NA,NA,NA,NA)),nudge_y = -0.2,show.legend = FALSE)+
  #scale_y_continuous(labels = percent)+
  scale_x_continuous(breaks = c(24,48,72,96,120))+
  ylab('') +
  scale_colour_manual('Thrombospondin antibody',values = c("#56B4E9", "#0072B2", "#D55E00")) +
  scale_shape_discrete('Thrombospondin antibody')+
  theme_cowplot()+ 
  theme(legend.title = element_text(colour = NA))

```

# 4. Plots

```{r plots}
all_figures <- cowplot::plot_grid(ggf1,ggf2,ggf3,ncol=1)
all_figures
ggsave(all_figures,filename = 'Figure 5.pdf',width = 6,height=8)
```

# 5. qPCR analysis

```{r}
qPCR <- read.csv('Figure 6 source data.csv',
                 stringsAsFactors = F)

qPCR$Time <- as.factor(qPCR$Time)
qPCR$Symbiont <- as.factor(qPCR$Symbiont)


```

## 5.1 Ap_Sema5 analysis

```{r Sema}
Semamod<-lm(Sema~oral_disc+Symbiont*Time, data = qPCR)
anova(Semamod)

Semamod_lsmobj <- lsmeans(Semamod, ~ Symbiont|Time)
summary(as.glht(pairs(Semamod_lsmobj),by=NULL))
confint(pairs(Semamod_lsmobj),by=NULL)

```

## 5.2 Ap_Trypsin-like analysis

```{r Trypsin}

Trypsinmod<-lm(Trypsin~oral_disc+Symbiont*Time, data = qPCR)
anova(Trypsinmod)

Trypsinmod_lsmobj <- lsmeans(Trypsinmod, ~ Symbiont|Time)
summary(as.glht(pairs(Trypsinmod_lsmobj),by=NULL))
confint(pairs(Trypsinmod_lsmobj),by=NULL)

```


Statistical analyses