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RoVI_module4_16S_vs_ORV.Rmd
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RoVI_module4_16S_vs_ORV.Rmd
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---
title: "Analysis module 4: 16S vs ORV outcome"
author: "RoVI study"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
flexdashboard::flex_dashboard:
self_contained: yes
theme: flatly
---
Sidebar {.sidebar}
=====================================
[Sample summary]
[Alpha diversity]
[Beta diversity]
[Differential taxa]
[Summary figures]
[Longitudinal abundance comparisons]
[Session info]
Sample summary {.hidden}
=====================================
```{r, message=FALSE}
# variable to define whether to run analyses in full (TRUE) or not (FALSE)
run_rf_full = FALSE # set as TRUE to run Random Forests in full
run_nb_full = FALSE # set as TRUE to run negative binomial models in full
iter = 20 # number of cross-validation iterations to run
# create output folders
if ("output_module4" %in% list.files()==FALSE) {
system("mkdir output_module4")
system("mkdir output_module4/RF_output_stats")
system("mkdir output_module4/RF_output_importance")
}
# load packages and functions
source("input/packages_and_functions.R")
source("input/RF_functions.R")
# load genus/rsv-level data - full and rarefied
load("phyloseq_files/ps7_genus.RData")
load("phyloseq_files/ps7_l_t_pr_d_dd_25k.RData")
load("phyloseq_files/ps8_genus.RData")
load("phyloseq_files/ps8_l_t_pr_d_dd_25k_rarefied.RData")
# select BS1, BS2, BS3, BS5 and MS1 samples within each phyloseq object
ps7_genus = subset_samples(ps7_genus, (!is.na(dose1_shedding) | !is.na(seroconv)) & (sample_type=="BS1" | sample_type=="BS2" | sample_type=="BS3" | sample_type=="BS5" | sample_type=="MS1")) %>% prune_taxa(taxa_sums(.) > 0, .)
ps7 = subset_samples(ps7_l_t_pr_d_dd_25k, (!is.na(dose1_shedding) | !is.na(seroconv)) & (sample_type=="BS1" | sample_type=="BS2" | sample_type=="BS3" | sample_type=="BS5" | sample_type=="MS1")) %>% prune_taxa(taxa_sums(.) > 0, .)
ps8_genus = subset_samples(ps8_genus, (!is.na(dose1_shedding) | !is.na(seroconv)) & (sample_type=="BS1" | sample_type=="BS2" | sample_type=="BS3" | sample_type=="BS5" | sample_type=="MS1")) %>% prune_taxa(taxa_sums(.) > 0, .)
ps8 = subset_samples(ps8_l_t_pr_d_dd_25k_rarefied, (!is.na(dose1_shedding) | !is.na(seroconv)) & (sample_type=="BS1" | sample_type=="BS2" | sample_type=="BS3" | sample_type=="BS5" | sample_type=="MS1")) %>% prune_taxa(taxa_sums(.) > 0, .)
#all(sample_names(ps7_genus)==sample_names(ps8_genus))
# amend taxa names to be consistent across ps7 and ps8 levels
taxa_names(ps7_genus) = data.frame(as(tax_table(ps7_genus), "matrix"))$Genus
taxa_names(ps7) = data.frame(as(tax_table(ps7), "matrix"))$tax_id
taxa_names(ps8_genus) = data.frame(as(tax_table(ps8_genus), "matrix"))$Genus
taxa_names(ps8) = data.frame(as(tax_table(ps8), "matrix"))$tax_id
# transform to relative abundances
rps7_genus = transform_sample_counts(ps7_genus, function(x) {x/sum(x)})
rps7 = transform_sample_counts(ps7, function(x) {x/sum(x)})
rps8_genus = transform_sample_counts(ps8_genus, function(x) {x/sum(x)})
rps8 = transform_sample_counts(ps8, function(x) {x/sum(x)})
# add log E/B ratio
rps8_family = tax_glom(rps8, "Family", NArm = TRUE)
t = data.frame(sample_data(rps8_family))
df_family = data.frame(t(otu_table(rps8_family)))
if (all(colnames(df_family)==rownames(tax_table(rps8_family)))) { colnames(df_family) = as(tax_table(rps8_family), "matrix")[,"Family"] } else { print("error: rownames do not match")}
if (all(t$sample_ID_full==rownames(df_family))) {
t$Bacteroidaceae = df_family$Bacteroidaceae
t$Enterobacteriaceae = df_family$Enterobacteriaceae
}
t$log_Entero_Bac_ratio = log((t$Enterobacteriaceae+0.001)/(t$Bacteroidaceae+0.001))
if (all(sample_data(rps8)$sample_ID_full == t$sample_ID_full)) {
sample_data(rps8) = sample_data(rps8_genus) = sample_data(rps7) = sample_data(rps7_genus) = t
sample_data(ps8) = sample_data(ps8_genus) = sample_data(ps7) = sample_data(ps7_genus) = t
}
```
#### Input data: infant stools (BS1, BS2, BS3, and BS5) and maternal stool (MS1)
* n samples = `r nsamples(ps7_genus)`
* n taxa: genera = `r ntaxa(ps7_genus)`; variants = `r sum(taxa_sums(ps7)>0)`
* n taxa (rarefied): genera = `r ntaxa(ps8_genus)`; variants = `r sum(taxa_sums(ps8)>0)`
#### Statistical methods for mixed-effects models
* Linear mixed-effects models fit with family ID as a random effect
* Association with ORV outcome determined by comparing fit of model with country/age vs model with age alone
Alpha diversity {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### Seroconversion
#### Longitudinal plots - Shannon
```{r, fig.width=10, fig.height=3.5}
t = data.frame(sample_data(rps8_genus))
write.csv(t, "final_analysis_set_metadata.csv")
# updated coding for week
t$week_fig[t$week_fig=="week 1"] = "week of life 1"
t$week_fig[t$week_fig=="week 4"] = "week of life 4"
t$week_fig[t$week_fig=="week 6/8"] = "week of life 6"
t$week_fig[t$week_fig=="week 10/12"] = "week of life 10"
t$week_fig = factor(t$week_fig, levels = c("week of life 1","week of life 4","week of life 6","week of life 10"))
# update coding for outcome
#t$seroconv[t$seroconv==0] = "-"
#t$seroconv[t$seroconv==1] = "+"
# updated coding for country
t$country[t$country=="India"] = "IND"
t$country[t$country=="Malawi"] = "MLW"
t$country[t$country=="UK"] = "UK"
t$country_exposure[t$country_exposure=="India (exposed)"] = "IND (neo+)"
t$country_exposure[t$country_exposure=="India (unexposed)"] = "IND (neo-)"
t$country_exposure[t$country_exposure=="Malawi"] = "MLW"
t1 = subset(t, !is.na(seroconv) & sample_type!="MS1")
# collate dataframes for plot
t2 = subset(t1, country_exposure=="IND (neo+)" | country_exposure=="IND (neo-)")
t2$country = t2$country_exposure
t_plot = rbind(t1,t2)
t_plot$country = factor(t_plot$country, levels = c("IND", "MLW", "UK", "IND (neo+)", "IND (neo-)"))
# generate plots
sero_fig_A = alpha_longitudinal(t_plot, "Shannon", "Shannon", "seroconv", "seroconversion", exposure=FALSE) + ylim(0,3) +
ggtitle("alpha diversity") + theme(plot.title = element_text(size=16, hjust=0.5))
sero_fig_A
```
#### Longitudinal plots - richness
```{r, fig.width=10, fig.height=3.5}
# generate plots
sero_fig_A_alt = alpha_longitudinal(t_plot, "Observed", "richness", "seroconv", "seroconversion", exposure=FALSE) + ylim(0,30) +
ggtitle("alpha diversity") + theme(plot.title = element_text(size=16, hjust=0.5))
sero_fig_A_alt
```
#### Longitudinal plots - E/B ratio
```{r, fig.width=10, fig.height=3.5}
# generate plots
sero_fig_A_alt = alpha_longitudinal(t_plot, "log_Entero_Bac_ratio", "log(Ent/Bac)", "seroconv", "seroconversion", exposure=FALSE) +
ggtitle("alpha diversity") + theme(plot.title = element_text(size=16, hjust=0.5))
sero_fig_A_alt
```
Blue = seroconversion+, Orange = seroconversion-.
#### P values - E/B ratio
##### IND
```{r}
t2 = subset(t1, country=="IND" & sample_type=="BS3")
wilcox.test(log_Entero_Bac_ratio ~ seroconv, data = t2)
```
##### MLW
```{r}
t2 = subset(t1, country=="MLW" & sample_type=="BS3")
wilcox.test(log_Entero_Bac_ratio ~ seroconv, data = t2)
```
##### UK
```{r}
t2 = subset(t1, country=="UK" & sample_type=="BS3")
wilcox.test(log_Entero_Bac_ratio ~ seroconv, data = t2)
```
##### IND neo+
```{r}
t2 = subset(t1, country_exposure=="IND (neo+)" & sample_type=="BS3")
wilcox.test(log_Entero_Bac_ratio ~ seroconv, data = t2)
```
##### IND neo-
```{r}
t2 = subset(t1, country_exposure=="IND (neo-)" & sample_type=="BS3")
wilcox.test(log_Entero_Bac_ratio ~ seroconv, data = t2)
```
#### N by country
```{r}
table(t_plot$seroconv, t_plot$sample_type, t_plot$country)
```
#### P values, cross-sectional analayses
See *Module 5* for full univariate and multivariate analyses.
#### P values, longitudinal mixed-effects models
```{r}
longitudinal_results(t1, alpha1="Shannon", alpha2="log(Observed)", outcome="seroconv", with_UK=TRUE)
```
#### Sensitivity - exclusively breastfed
```{r, fig.width=5, fig.height=3.5}
t_s = subset(t, !is.na(seroconv) & sample_type!="MS1" & country_exposure=="IND (neo-)" & breastfed_child=="exclusive") %>% droplevels()
t_s$country = "IND (neo-), exclusively breastfed"
# generate plots
sero_fig_A_bf = alpha_longitudinal(t_s, "Shannon", "Shannon", "seroconv", "seroconversion", exposure=FALSE) #+ ylim(0,30)
sero_fig_A_bf
```
Blue = seroconversion+, Orange = seroconversion-.
#### N by country
```{r}
table(t_s$seroconv, t_s$sample_type, t_s$country)
```
#### P values, longitudinal mixed-effects models
```{r}
lme_alpha_ORV(subset(t_s, country_exposure=="IND (neo-)"), "Shannon", "seroconv")
```
#### P values, cross-sectional models
```{r}
p_1 = summary(glm(seroconv ~ Shannon, data = subset(t_s, sample_type=="BS1"), family = binomial))$coefficients[2,4]
p_2 = summary(glm(seroconv ~ Shannon, data = subset(t_s, sample_type=="BS2"), family = binomial))$coefficients[2,4]
p_3 = summary(glm(seroconv ~ Shannon, data = subset(t_s, sample_type=="BS3"), family = binomial))$coefficients[2,4]
p_4 = summary(glm(seroconv ~ Shannon, data = subset(t_s, sample_type=="BS5"), family = binomial))$coefficients[2,4]
data.frame(week = c(1,4,6,10), p = round(c(p_1, p_2, p_3, p_4), 4))
```
#### Sensitivity - vaginal delivery
```{r, fig.width=5, fig.height=3.5}
t_s = subset(t, !is.na(seroconv) & sample_type!="MS1" & country_exposure=="IND (neo-)" & mode_delivery=="vaginal") %>% droplevels()
t_s$country = "IND (neo-), vaginal delivery"
# generate plots
sero_fig_A_vd = alpha_longitudinal(t_s, "Shannon", "Shannon", "seroconv", "seroconversion", exposure=FALSE)
sero_fig_A_vd
```
Blue = seroconversion+, Orange = seroconversion-.
#### N by country
```{r}
table(t_s$seroconv, t_s$sample_type, t_s$country)
```
#### P values, longitudinal mixed-effects models
```{r}
lme_alpha_ORV(subset(t_s, country_exposure=="IND (neo-)"), "Shannon", "seroconv")
```
#### P values, cross-sectional models
```{r}
p_1 = summary(glm(seroconv ~ Shannon, data = subset(t_s, sample_type=="BS1"), family = binomial))$coefficients[2,4]
p_2 = summary(glm(seroconv ~ Shannon, data = subset(t_s, sample_type=="BS2"), family = binomial))$coefficients[2,4]
p_3 = summary(glm(seroconv ~ Shannon, data = subset(t_s, sample_type=="BS3"), family = binomial))$coefficients[2,4]
p_4 = summary(glm(seroconv ~ Shannon, data = subset(t_s, sample_type=="BS5"), family = binomial))$coefficients[2,4]
data.frame(week = c(1,4,6,10), p = round(c(p_1, p_2, p_3, p_4), 4))
```
#### Combined plot
```{r, fig.width=8, fig.height=3.5}
plot_grid(sero_fig_A_bf, sero_fig_A_vd)
```
### Post-ORV RV-IgA
#### Week of life 1
```{r, fig.width=2.5, fig.height=2}
# collate Pearson correlation coefficients: alpha vs post-ORV RV-IgA
results = data.frame(
India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS1", "corr"),
India_exposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS1", "corr"),
India_unexposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS1", "corr"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS1", "corr"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS1", "corr")
)
results = mutate_all(results, function(x) as.numeric(as.character(x)))
# collate corresponding p values
pvals = data.frame(
India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS1", "p"),
India_exposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS1", "p"),
India_unexposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS1", "p"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS1", "p"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS1", "p"))
pvals = mutate_all(pvals, function(x) as.numeric(as.character(x)))
# collate n
cor_n = data.frame(India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS1", "n"),
India_neo_pos = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS1", "n"),
India_neo_neg = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS1","n"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS1","n"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS1", "n"))
cor_n = mutate_all(cor_n, function(x) as.numeric(as.character(x)))
# plot heatmap of results
col2 <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE", "#4393C3", "#2166AC", "#053061"))
rownames(results) = rownames(pvals) = c("Shannon", "log(richness)")
pheatmap(results, cluster_rows = F, cluster_cols = F, color = col2(200), breaks = seq(-1, 1, by = 0.01))
```
##### Correlation coefficients values
```{r}
data.frame(results)
```
##### P values
```{r}
data.frame(pvals)
```
##### N
```{r}
data.frame(cor_n)
```
#### Week of life 4
```{r, fig.width=2.5, fig.height=2}
# collate Pearson correlation coefficients: alpha vs post-ORV RV-IgA
results = data.frame(
India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS2", "corr"),
India_exposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS2", "corr"),
India_unexposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS2", "corr"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS2", "corr"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS2", "corr")
)
results = mutate_all(results, function(x) as.numeric(as.character(x)))
# collate corresponding p values
pvals =data.frame(
India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS2", "p"),
India_exposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS2", "p"),
India_unexposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS2", "p"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS2", "p"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS2", "p"))
pvals = mutate_all(pvals, function(x) as.numeric(as.character(x)))
# collate n
cor_n = data.frame(India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS2", "n"),
India_neo_pos = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS2", "n"),
India_neo_neg = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS2","n"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS2","n"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS2", "n"))
cor_n = mutate_all(cor_n, function(x) as.numeric(as.character(x)))
# plot heatmap of results
rownames(results) = rownames(pvals) = c("Shannon", "log(richness)")
pheatmap(results, cluster_rows = F, cluster_cols = F, color = col2(200), breaks = seq(-1, 1, by = 0.01))
```
##### Correlation coefficients values
```{r}
data.frame(results)
```
##### P values
```{r}
data.frame(pvals)
```
##### N
```{r}
data.frame(cor_n)
```
#### Week of life 6
```{r, fig.width=2.5, fig.height=2}
# collate Pearson correlation coefficients: alpha vs post-ORV RV-IgA
results = data.frame(
India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS3", "corr"),
India_exposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS3", "corr"),
India_unexposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS3", "corr"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS3", "corr"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS3", "corr")
)
results = mutate_all(results, function(x) as.numeric(as.character(x)))
# collate corresponding p values
pvals =data.frame(
India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS3", "p"),
India_exposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS3", "p"),
India_unexposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS3", "p"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS3", "p"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS3", "p"))
pvals = mutate_all(pvals, function(x) as.numeric(as.character(x)))
# collate n
cor_n = data.frame(India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS3", "n"),
India_neo_pos = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS3", "n"),
India_neo_neg = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS3","n"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS3","n"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS3", "n"))
cor_n = mutate_all(cor_n, function(x) as.numeric(as.character(x)))
# plot heatmap of results
col2 <- colorRampPalette(c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FFFFFF", "#D1E5F0", "#92C5DE", "#4393C3", "#2166AC", "#053061"))
rownames(results) = rownames(pvals) = c("Shannon", "log(richness)")
pheatmap(results, cluster_rows = F, cluster_cols = F, color = col2(200), breaks = seq(-1, 1, by = 0.01))
```
##### Correlation coefficients values
```{r}
data.frame(results)
```
##### P values
```{r}
data.frame(pvals)
```
##### N
```{r}
data.frame(cor_n)
```
#### Week of life 10
```{r, fig.width=2.5, fig.height=2}
# collate Pearson correlation coefficients: alpha vs post-ORV RV-IgA
results = data.frame(
India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS5", "corr"),
India_exposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS5", "corr"),
India_unexposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS5", "corr"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS5", "corr"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS5", "corr")
)
results = mutate_all(results, function(x) as.numeric(as.character(x)))
# collate corresponding p values
pvals =data.frame(
India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS5", "p"),
India_exposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS5", "p"),
India_unexposed = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS5", "p"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS5", "p"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS5", "p")
)
pvals = mutate_all(pvals, function(x) as.numeric(as.character(x)))
# collate n
cor_n = data.frame(India = corr_by_country_alpha(t1, "BB2_IgA", "IND", sample_subset="BS5", "n"),
India_neo_pos = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo+)", sample_subset="BS5", "n"),
India_neo_neg = corr_by_country_alpha(t1, "BB2_IgA", "IND (neo-)", sample_subset="BS5","n"),
Malawi = corr_by_country_alpha(t1, "BB2_IgA", "MLW", sample_subset="BS5","n"),
UK = corr_by_country_alpha(t1, "BB2_IgA", "UK", sample_subset="BS5", "n"))
cor_n = mutate_all(cor_n, function(x) as.numeric(as.character(x)))
# plot heatmap of results
rownames(results) = rownames(pvals) = c("Shannon", "log(richness)")
pheatmap(results, cluster_rows = F, cluster_cols = F, color = col2(200), breaks = seq(-1, 1, by = 0.01))
```
##### Correlation coefficients values
```{r}
data.frame(results)
```
##### P values
```{r}
data.frame(pvals)
```
##### N
```{r}
data.frame(cor_n)
```
### Dose 1 shedding
#### Longitudinal plots - Shannon
```{r, fig.width=8, fig.height=3.5}
t1 = subset(t, !is.na(dose1_shedding) & country!="UK" & sample_type!="MS1" & sample_type!="BS5")
# collate dataframes for plot
t2 = subset(t1, country_exposure=="IND (neo+)" | country_exposure=="IND (neo-)")
t2$country = t2$country_exposure
t_plot = rbind(t1,t2)
t_plot$country = factor(t_plot$country, levels = c("IND", "MLW", "UK", "IND (neo+)", "IND (neo-)"))
# generate plots
dose1_fig_A = alpha_longitudinal(t_plot, "Shannon", "Shannon", "dose1_shedding", "dose 1 shedding", exposure=FALSE) + ylim(0,3.5) +
ggtitle("alpha diversity") + theme(plot.title = element_text(size=16, hjust=0.5))
dose1_fig_A
```
#### Longitudinal plots - richness
```{r, fig.width=8, fig.height=3.5}
# generate plots
dose1_fig_A_alt = alpha_longitudinal(t_plot, "Observed", "richness", "dose1_shedding", "dose 1 shedding", exposure=FALSE) + ylim(0,30) +
ggtitle("alpha diversity") + theme(plot.title = element_text(size=16, hjust=0.5))
dose1_fig_A_alt
```
#### N by country
```{r}
table(t_plot$dose1_shedding, t_plot$sample_type, t_plot$country)
```
#### P values, cross-sectional analayses
See *Module 5* for full univariate and multivariate analyses.
#### P values, longitudinal mixed-effects models
```{r}
longitudinal_results(t1, alpha1="Shannon", alpha2="log(Observed)", outcome="dose1_shedding")
```
Beta diversity {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### Unweighted
#### Seroconversion
```{r, fig.height=3, fig.width=8}
collated_beta_seroconv = collated_beta_function("seroconv", unweighted_logical=TRUE)
collated_beta_seroconv$outcome = "seroconversion"
collated_beta_seroconv
```
```{r, fig.width=10, fig.height=3.5}
sero_fig_B = ggplot(collated_beta_seroconv, aes(x = R2, y=week, fill = country)) + geom_bar(stat = "identity") +
ylab("week of life") + xlab(bquote(R^2~"(%)")) +
scale_x_continuous(limits=c(0,10), breaks=c(0,4,8)) + facet_grid(.~country)+
ggtitle("Bray-Curtis PERMANOVA") +
coord_flip() + theme(legend.position = "none") + scale_fill_manual(values = c(IND=India_col,MLW=Malawi_col,UK=UK_col,"IND (neo+)"=India_col, "IND (neo-)"=India_col)) +
theme(plot.title = element_text(size=16, hjust=0.5), axis.text = element_text(size=16), axis.title = element_text(size=16), strip.text = element_text(size=16)) +
theme(strip.background = element_blank())
sero_fig_B
```
#### RV-IgA
```{r}
collated_beta_IgA = collated_beta_function("BB2_IgA", unweighted_logical=TRUE)
collated_beta_IgA$outcome = "RV-IgA (post-ORV)"
collated_beta_IgA
```
```{r, fig.width=10, fig.height=3.5}
IgA_fig_B = ggplot(collated_beta_IgA, aes(x = R2, y=week, fill = country)) + geom_bar(stat = "identity") +
ylab("week of life") + xlab(bquote(R^2~"(%)")) +
scale_x_continuous(limits=c(0,10), breaks=c(0,4,8)) + facet_grid(.~country)+
ggtitle("Bray-Curtis PERMANOVA") +
coord_flip() + theme(legend.position = "none") + scale_fill_manual(values = c(IND=India_col,MLW=Malawi_col,UK=UK_col,"IND (neo+)"=India_col, "IND (neo-)"=India_col)) +
theme(plot.title = element_text(size=16, hjust=0.5), axis.text = element_text(size=16), axis.title = element_text(size=16), strip.text = element_text(size=16)) +
theme(strip.background = element_blank())
IgA_fig_B
```
#### Dose 1 shedding
```{r}
collated_beta_dose1 = collated_beta_function("dose1_shedding", unweighted_logical=TRUE)
collated_beta_dose1$outcome = "dose 1 shedding"
collated_beta_dose1 = subset(collated_beta_dose1, country!="UK" & week!=10)
collated_beta_dose1
```
```{r, fig.height=3, fig.width=8}
dose1_fig_B = ggplot(collated_beta_dose1, aes(x = R2, y=week, fill = country)) + geom_bar(stat = "identity") +
ylab("week of life") + xlab(bquote(R^2~"(%)")) +
scale_x_continuous(limits=c(0,10), breaks=c(0,4,8)) + facet_grid(.~country)+
ggtitle("Bray-Curtis PERMANOVA") +
coord_flip() + theme(legend.position = "none") + scale_fill_manual(values = c(IND=India_col,MLW=Malawi_col,UK=UK_col,"IND (neo+)"=India_col, "IND (neo-)"=India_col)) +
theme(plot.title = element_text(size=16, hjust=0.5), axis.text = element_text(size=16), axis.title = element_text(size=16), strip.text = element_text(size=16)) +
theme(strip.background = element_blank())
dose1_fig_B
```
### Weighted
#### Seroconversion
```{r, fig.height=3, fig.width=8}
collated_beta_seroconv = collated_beta_function("seroconv", unweighted_logical=FALSE)
collated_beta_seroconv$outcome = "seroconversion"
collated_beta_seroconv
```
#### RV-IgA
```{r}
collated_beta_IgA = collated_beta_function("BB2_IgA", unweighted_logical=FALSE)
collated_beta_IgA$outcome = "RV-IgA (post-ORV)"
collated_beta_IgA
```
#### Dose 1 shedding
```{r}
collated_beta_dose1 = collated_beta_function("dose1_shedding", unweighted_logical=FALSE)
collated_beta_dose1$outcome = "dose 1 shedding"
collated_beta_dose1 = subset(collated_beta_dose1, country!="UK" & week!=10)
collated_beta_dose1
```
Differential taxa {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### Seroconversion
#### Genus- and RSV-level models
```{r}
### Genus level
set.seed(12345)
# select input data
rps = rps8_genus
ps = ps7_genus
input_m = data.frame(sample_data(rps))
# specify sample types and country subsets to include in analysis
subset_list = c("BS1", "BS2", "BS3", "BS5", "MS1")
country_list = c("India", "India (exposed)", "India (unexposed)", "Malawi", "UK")
# select outcome, then run RF using collate_rf_binary function
outcome = "seroconv"
if (run_rf_full) { collate_rf_binary("crossval_collated_genus_seroconv.csv") }
collated_rf = read.csv("output_module4/crossval_collated_genus_seroconv.csv", row.names=1)
# remove MLW samples from BS5 owing to insufficient responders (n <10)
collated_rf = subset(collated_rf, !(country=="Malawi" & sample_subset=="BS5") & sample_subset!="MS1") %>% droplevels()
collated_rf$country = factor(collated_rf$country, levels=c("India", "Malawi", "UK", "India (exposed)", "India (unexposed)"))
collated_rf$level = "genus"
```
```{r}
### RSV level
set.seed(12345)
# select input data
rps = rps8
ps = ps7
input_m = data.frame(sample_data(rps))
# specify sample types and country subsets to include in analysis
subset_list = c("BS1", "BS2", "BS3", "BS5", "MS1")
country_list = c("India", "India (exposed)", "India (unexposed)", "Malawi", "UK")
# select outcome, then run RF using collate_rf_binary function
outcome = "seroconv"
if (run_rf_full) { collate_rf_binary("crossval_collated_rsv_seroconv.csv") }
collated_rf_rsv = read.csv("output_module4/crossval_collated_rsv_seroconv.csv", row.names=1)
# remove MLW samples from BS5 owing to insufficient responders (n <10)
collated_rf_rsv = subset(collated_rf_rsv, !(country=="Malawi" & sample_subset=="BS5") & sample_subset!="MS1") %>% droplevels()
collated_rf_rsv$country = factor(collated_rf_rsv$country, levels=c("India", "Malawi", "UK", "India (exposed)", "India (unexposed)"))
collated_rf_rsv$level = "RSV"
```
#### Summary plot
```{r, fig.width=10, fig.height=4.75}
# collate and plot
full = rbind(collated_rf, collated_rf_rsv)
# plot summary figures
sero_fig_C = summary_rf_plot_binary(full, "Random Forests") + scale_y_continuous(limits = c(0.3,1), breaks = c(0.4,0.6,0.8,1)) + xlab("week of life")
sero_fig_C
```
#### Differential genera - Fisher's test or Aldex2 (FDR p <0.05)
```{r, fig.width=10, fig.height=6}
summary = rbind(
summary_abund_plot_ORV("seroconv", "India", "IND", "genus"),
summary_abund_plot_ORV("seroconv", "Malawi", "MLW", "genus"),
summary_abund_plot_ORV("seroconv", "UK", "UK", "genus"),
summary_abund_plot_ORV("seroconv", "India (exposed)", "IND (neo+)", "genus"),
summary_abund_plot_ORV("seroconv", "India (unexposed)", "IND (neo-)", "genus")
)
# generate plot
ggplot(summary, aes(x=week, y=count, label=count, colour=enriched, group=enriched)) +
geom_point(size=3, alpha=0.8) + geom_line(alpha=0.5, show.legend = FALSE) +
geom_text(vjust = 0, nudge_y = 2, show.legend = FALSE, size=5) +
# geom_text(aes(y = rep(ymax,8), label=total), colour="grey", show.legend = FALSE, fontface = "italic", size=5) +
xlab("week of life") + facet_grid(enriched~country) + #ylim(0,100) +
scale_colour_manual(values=c(nr_col, r_col, "grey")) +
theme(legend.position="none", strip.background = element_blank()) +
ylab("number of genera") + labs(colour='enriched country') +
theme(axis.text = element_text(size=16), axis.title = element_text(size=16), plot.title = element_text(size=16, hjust = 0.5), strip.text = element_text(size=16),
legend.text = element_text(size=16), legend.title = element_text(size=16))
```
#### Differential RSVs - Fisher's test or Aldex2 (FDR p <0.05)
```{r, fig.width=10, fig.height=6}
summary = rbind(
summary_abund_plot_ORV("seroconv", "India", "IND", "rsv"),
summary_abund_plot_ORV("seroconv", "Malawi", "MLW", "rsv"),
summary_abund_plot_ORV("seroconv", "UK", "UK", "rsv"),
summary_abund_plot_ORV("seroconv", "India (exposed)", "IND (neo+)", "rsv"),
summary_abund_plot_ORV("seroconv", "India (unexposed)", "IND (neo-)", "rsv")
)
# generate plot
ggplot(summary, aes(x=week, y=count, label=count, colour=enriched, group=enriched)) +
geom_point(size=3, alpha=0.8) + geom_line(alpha=0.5, show.legend = FALSE) +
geom_text(vjust = 0, nudge_y = 2, show.legend = FALSE, size=5) +
# geom_text(aes(y = rep(ymax,8), label=total), colour="grey", show.legend = FALSE, fontface = "italic", size=5) +
xlab("week of life") + facet_grid(enriched~country) + #ylim(0,350) +
scale_colour_manual(values=c(nr_col, r_col, "grey")) +
theme(legend.position="none", strip.background = element_blank()) +
ylab("number of RSVs") + labs(colour='enriched country') +
theme(axis.text = element_text(size=16), axis.title = element_text(size=16), plot.title = element_text(size=16, hjust = 0.5), strip.text = element_text(size=16),
legend.text = element_text(size=16), legend.title = element_text(size=16))
```
#### RV-IgA
#### Genus- and RSV-level models
```{r}
### Genus level
set.seed(23456)
# select input data
rps = rps8_genus
ps = ps7_genus
input_m = data.frame(sample_data(rps))
# specify country subsets to include in analysis
country_list = c("India", "India (exposed)", "India (unexposed)", "Malawi", "UK")
outcome = "BB2_IgA"
level = "genus"
# run RF using collate_rf_IgA function
if (run_rf_full) { collate_rf_IgA("crossval_collated_genus_IgA.csv") }
collated_rf = read.csv("output_module4/crossval_collated_genus_IgA.csv", row.names=1)
# remove MS1 samples
collated_rf = subset(collated_rf, sample_subset!="MS1") %>% droplevels()
collated_rf$country = factor(collated_rf$country, levels=c("India", "Malawi", "UK", "India (exposed)", "India (unexposed)"))
collated_rf$level = "genus"
```
```{r}
### RSV level
set.seed(23456)
# select input data
rps = rps8
ps = ps7
input_m = data.frame(sample_data(rps))
# specify country subsets to include in analysis
country_list = c("India", "India (exposed)", "India (unexposed)", "Malawi", "UK")
outcome = "BB2_IgA"
level = "rsv"
# run RF using collate_rf_IgA function
if (run_rf_full) { collate_rf_IgA("crossval_collated_rsv_IgA.csv") }
collated_rf_rsv = read.csv("output_module4/crossval_collated_rsv_IgA.csv", row.names=1)
# remove MS1 samples
collated_rf_rsv = subset(collated_rf_rsv, sample_subset!="MS1") %>% droplevels()
collated_rf_rsv$country = factor(collated_rf_rsv$country, levels=c("India", "Malawi", "UK", "India (exposed)", "India (unexposed)"))
collated_rf_rsv$level = "RSV"
```
#### Summary plot
```{r, fig.width=10, fig.height=4.75}
# collate and plot
full = rbind(collated_rf, collated_rf_rsv)
full$week = revalue(as.factor(full$sample_subset), c("BS1" = "1", "BS2" = "4", "BS3" = "6", "BS5" = "10"))
full$country = revalue(as.factor(full$country), c("Malawi" = "MLW", "India" = "IND", "India (exposed)" = "IND (neo+)", "India (unexposed)" = "IND (neo-)"))
# plot summary figures
IgA_fig_C = ggplot(full, aes(y = median_tst_lm_R2*100, x = factor(week), colour=factor(country))) +
geom_point(size = 5, alpha=0.8) +
geom_errorbar(aes(ymin=q1_tst_lm_R2*100, ymax=q3_tst_lm_R2*100), width=0) +
geom_hline(yintercept=0, linetype="dotted") + xlab("week of life") +
ylab(bquote("CV"~R^2~"(%)")) +
scale_y_continuous(limits=c(-10, 50), oob=rescale_none, breaks=c(0,25,50)) +
scale_colour_manual(values = c(IND = India_col, "IND (neo+)" = India_col, "IND (neo-)" = India_col, MLW = Malawi_col, UK = UK_col)) +
theme_bw() + theme(legend.position = "", strip.background = element_blank()) +
ggtitle("Random Forests") + facet_grid(level~country) +
theme(axis.text = element_text(size = 16), axis.title = element_text(size = 16), plot.title = element_text(size = 16, hjust = 0.5), strip.text = element_text(size = 16))
IgA_fig_C
```
#### Differential genera - Fisher's test or Aldex2 (FDR p <0.05)
```{r, fig.width=10, fig.height=6}
summary = rbind(
summary_abund_plot_IgA("India", "IND", "genus"),
summary_abund_plot_IgA("Malawi", "MLW", "genus"),
summary_abund_plot_IgA("UK", "UK", "genus"),
summary_abund_plot_IgA("India (exposed)", "IND (neo+)", "genus"),
summary_abund_plot_IgA("India (unexposed)", "IND (neo-)", "genus")
)
# generate plot
ggplot(summary, aes(x=week, y=count, label=count, colour=enriched, group=enriched)) +
geom_point(size=3, alpha=0.8) + geom_line(alpha=0.5, show.legend = FALSE) +
geom_text(vjust = 0, nudge_y = 2, show.legend = FALSE, size=5) +
# geom_text(aes(y = rep(ymax,8), label=total), colour="grey", show.legend = FALSE, fontface = "italic", size=5) +
xlab("week of life") + facet_grid(enriched~country) + #ylim(0,100) +
scale_colour_manual(values=c(nr_col, r_col, "grey")) +
theme(legend.position="none", strip.background = element_blank()) +
ylab("number of genera") + labs(colour='enriched country') +
theme(axis.text = element_text(size=16), axis.title = element_text(size=16), plot.title = element_text(size=16, hjust = 0.5), strip.text = element_text(size=16),
legend.text = element_text(size=16), legend.title = element_text(size=16))
```
#### Differential RSVs - Fisher's test or Aldex2 (FDR p <0.05)
```{r, fig.width=10, fig.height=6}
summary = rbind(
summary_abund_plot_IgA("India", "IND", "rsv"),
summary_abund_plot_IgA("Malawi", "MLW", "rsv"),
summary_abund_plot_IgA("UK", "UK", "rsv"),
summary_abund_plot_IgA("India (exposed)", "IND (neo+)", "rsv"),
summary_abund_plot_IgA("India (unexposed)", "IND (neo-)", "rsv")
)
# generate plot
ggplot(summary, aes(x=week, y=count, label=count, colour=enriched, group=enriched)) +
geom_point(size=3, alpha=0.8) + geom_line(alpha=0.5, show.legend = FALSE) +
geom_text(vjust = 0, nudge_y = 2, show.legend = FALSE, size=5) +
# geom_text(aes(y = rep(ymax,8), label=total), colour="grey", show.legend = FALSE, fontface = "italic", size=5) +
xlab("week of life") + facet_grid(enriched~country) + #ylim(0,200) +
scale_colour_manual(values=c(nr_col, r_col, "grey")) +
theme(legend.position="none", strip.background = element_blank()) +
ylab("number of RSVs") + labs(colour='enriched country') +
theme(axis.text = element_text(size=16), axis.title = element_text(size=16), plot.title = element_text(size=16, hjust = 0.5), strip.text = element_text(size=16),
legend.text = element_text(size=16), legend.title = element_text(size=16))
```
### Dose 1 shedding
#### Genus- and RSV-level models
```{r}
### Genus level
set.seed(34567)
# select input data
rps = rps8_genus
ps = ps7_genus
input_m = data.frame(sample_data(rps))
# specify sample types and country subsets to include in analysis
country_list = c("India", "India (exposed)", "India (unexposed)", "Malawi")
# select outcome, then run RF using collate_rf_binary function
outcome = "dose1_shedding"
if (run_rf_full) { collate_rf_binary("crossval_collated_genus_dose1_shedding.csv") }
collated_rf = read.csv("output_module4/crossval_collated_genus_dose1_shedding.csv", row.names=1)
# remove MS1
collated_rf = subset(collated_rf, sample_subset!="MS1" & sample_subset!="BS5") %>% droplevels()
collated_rf$country = factor(collated_rf$country, levels=c("India", "Malawi", "UK", "India (exposed)", "India (unexposed)"))
collated_rf$level = "genus"
```
```{r}
### RSV level
set.seed(34567)
# select input data
rps = rps8
ps = ps7
input_m = data.frame(sample_data(rps))
# specify sample types and country subsets to include in analysis
country_list = c("India", "India (exposed)", "India (unexposed)", "Malawi")
# select outcome, then run RF using collate_rf_binary function
outcome = "dose1_shedding"
if (run_rf_full) { collate_rf_binary("crossval_collated_rsv_dose1_shedding.csv") }
collated_rf_rsv = read.csv("output_module4/crossval_collated_rsv_dose1_shedding.csv", row.names=1)
# remove MS1
collated_rf_rsv = subset(collated_rf, sample_subset!="MS1" & sample_subset!="BS5") %>% droplevels()
collated_rf_rsv$country = factor(collated_rf_rsv$country, levels=c("India", "Malawi", "UK", "India (exposed)", "India (unexposed)"))
collated_rf_rsv$level = "RSV"
```
#### Summary plot
```{r, fig.width=8, fig.height=4.75}
# collate and plot
full = rbind(collated_rf, collated_rf_rsv)
# plot summary figures
dose1_fig_C = summary_rf_plot_binary(full, "Random Forests") + scale_y_continuous(limits = c(0.3,1), breaks = c(0.4,0.6,0.8,1)) + xlab("week of life")
dose1_fig_C
```
#### Differential genera - Fisher's test or Aldex2 (FDR p <0.05)
```{r, fig.width=10, fig.height=6}
summary = rbind(
summary_abund_plot_ORV("dose1_shedding", "India", "IND", "genus"),
summary_abund_plot_ORV("dose1_shedding", "Malawi", "MLW", "genus"),
summary_abund_plot_ORV("dose1_shedding", "India (exposed)", "IND (neo+)", "genus"),
summary_abund_plot_ORV("dose1_shedding", "India (unexposed)", "IND (neo-)", "genus")
)
# generate plot
ggplot(summary, aes(x=week, y=count, label=count, colour=enriched, group=enriched)) +
geom_point(size=3, alpha=0.8) + geom_line(alpha=0.5, show.legend = FALSE) +
geom_text(vjust = 0, nudge_y = 2, show.legend = FALSE, size=5) +
# geom_text(aes(y = rep(ymax,8), label=total), colour="grey", show.legend = FALSE, fontface = "italic", size=5) +
xlab("week of life") + facet_grid(enriched~country) + #ylim(0,100) +
scale_colour_manual(values=c(nr_col, r_col, "grey")) +
theme(legend.position="none", strip.background = element_blank()) +
ylab("number of genera") + labs(colour='enriched country') +
theme(axis.text = element_text(size=16), axis.title = element_text(size=16), plot.title = element_text(size=16, hjust = 0.5), strip.text = element_text(size=16),
legend.text = element_text(size=16), legend.title = element_text(size=16))
```
#### Differential RSVs - Fisher's test or Aldex2 (FDR p <0.05)
```{r, fig.width=10, fig.height=6}
summary = rbind(
summary_abund_plot_ORV("dose1_shedding", "India", "IND", "rsv"),
summary_abund_plot_ORV("dose1_shedding", "Malawi", "MLW", "rsv"),
summary_abund_plot_ORV("dose1_shedding", "India (exposed)", "IND (neo+)", "rsv"),
summary_abund_plot_ORV("dose1_shedding", "India (unexposed)", "IND (neo-)", "rsv")
)
# generate plot
ggplot(summary, aes(x=week, y=count, label=count, colour=enriched, group=enriched)) +
geom_point(size=3, alpha=0.8) + geom_line(alpha=0.5, show.legend = FALSE) +
geom_text(vjust = 0, nudge_y = 2, show.legend = FALSE, size=5) +
# geom_text(aes(y = rep(ymax,8), label=total), colour="grey", show.legend = FALSE, fontface = "italic", size=5) +
xlab("week of life") + facet_grid(enriched~country) + #ylim(0,350) +
scale_colour_manual(values=c(nr_col, r_col, "grey")) +
theme(legend.position="none", strip.background = element_blank()) +
ylab("number of RSVs") + labs(colour='enriched country') +
theme(axis.text = element_text(size=16), axis.title = element_text(size=16), plot.title = element_text(size=16, hjust = 0.5), strip.text = element_text(size=16),
legend.text = element_text(size=16), legend.title = element_text(size=16))
```
Summary figures {.hidden}
=====================================
#### Seroconversion
```{r, fig.width=10, fig.height=11}
plot_grid(sero_fig_A,sero_fig_B,sero_fig_C, ncol=1, align = "v", axis="lr", rel_heights=c(1,1,1.5))
```
#### RV-IgA
```{r, fig.width=10, fig.height=8}
plot_grid(IgA_fig_B,IgA_fig_C, ncol=1, align = "v", axis="lr", rel_heights=c(1,1.5))
```
#### Dose 1 shedding
```{r, fig.width=8.25, fig.height=11}
plot_grid(dose1_fig_A,dose1_fig_B, dose1_fig_C, ncol=1, align = "v", axis="lr", rel_heights=c(1,1,1.5))
```
Longitudinal abundance comparisons {.hidden}
=====================================
```{r}
# append sequence of most common rsv to each genus
t = data.frame(as(tax_table(ps7_genus), "matrix"))
t_rsv = data.frame(as(tax_table(ps7_l_t_pr_d_dd_25k), "matrix"))
t$sequence = NA
for (i in 1:nrow(t)) {
genus = as.character(t$Genus[i]) # pick out genus for each row in turn
t_rsv_sub = subset(t_rsv, Genus==genus) # find matching RSVs
t$sequence[i] = as.character(t_rsv_sub$sequence[1]) # pick out top sequence from subset (which have been ordered by overall abundance)
}
```
#### Immunogenicity
#### Number of infants/taxa included in longitudinal models:
```{r}
# pick out analysis subsets
ps7_I = subset_samples(ps7_genus, sample_type!="MS1" & country=="India" & !is.na(seroconv)) %>%
prune_taxa(rowSums(otu_table(.)>0)/nsamples(.)>=0.20, .)
ps7_Ie = subset_samples(ps7_genus, sample_type!="MS1" & country_exposure=="India (exposed)" & !is.na(seroconv)) %>%
prune_taxa(rowSums(otu_table(.)>0)/nsamples(.)>=0.20, .)
ps7_Iu = subset_samples(ps7_genus, sample_type!="MS1" & country_exposure=="India (unexposed)" & !is.na(seroconv)) %>%