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RoVI_module3_16S_vs_country.Rmd
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RoVI_module3_16S_vs_country.Rmd
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---
title: "Analysis module 3: Geographic differences in 16S microbiota composition"
author: "RoVI study"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
flexdashboard::flex_dashboard:
self_contained: yes
theme: flatly
---
Sidebar {.sidebar}
=====================================
[Sample summary]
[Taxon abundance profile]
[Alpha diversity]
[Longitudinal abundance plots]
[Beta diversity]
[Differential taxa]
[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
adonis_iter = 999
# create output folders
if ("output_module3" %in% list.files()==FALSE) {
system("mkdir output_module3")
system("mkdir output_module3/RF_output_stats")
system("mkdir output_module3/RF_output_importance")
system("mkdir output_module3/RF_output_importance_reduced")
}
# 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
# select BS1, BS2, BS3, BS5 and MS1 samples within each phyloseq object
ps7_genus_infant = subset_samples(ps7_genus, (!is.na(dose1_shedding) | !is.na(seroconv)) & (sample_type=="BS1" | sample_type=="BS2" | sample_type=="BS3" | sample_type=="BS5")) %>% prune_taxa(taxa_sums(.) > 0, .)
ps7_infant = 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")) %>% prune_taxa(taxa_sums(.) > 0, .)
# 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)})
```
#### 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 (infant): genera = `r ntaxa(ps7_genus)`; variants = `r sum(taxa_sums(ps7)>0)`
* n taxa (rarefied): genera = `r ntaxa(ps7_genus_infant)`; variants = `r sum(taxa_sums(ps7_infant)>0)`
* mean read count = `r round(mean(sample_sums(ps7_genus)),0)`, s.d. = `r round(sd(sample_sums(ps7_genus)),0)`
#### Statistical methods for mixed-effects models
* Linear mixed-effects models fit with family ID as a random effect
* Effect of country determined by comparing: (i) model with country/age vs (ii) model with age alone
* Pairwise comparisons between countries (M vs I, U vs I, U vs M) corrected by FDR to control family error rate
* OPV/IPV arms from India combined in primary comparison
* Diversity in maternal samples compared by country via ANOVA with post-hoc Tukey test
Taxon abundance profile {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### Genus-level
#### India - infant
```{r, fig.width=4, fig.height=4}
# for each sample subset, create prevalence vs abundance plot using taxon_abundance_function
p = taxon_abundance_function(rps8_genus, genus=TRUE, input_country="India", input_colour=India_col)
ggMarginal(p, type = "density", fill=India_col, alpha=0.5, colour=NA, size=8)
```
N: `r nrow(subset(sample_data(rps8_genus), country=="India" & sample_type!="MS1"))`
#### Malawi - infant
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8_genus, genus=TRUE, input_country="Malawi", input_colour=Malawi_col)
ggMarginal(p, type = "density", fill=Malawi_col, alpha=0.5, colour=NA, size=8)
```
N: `r nrow(subset(sample_data(rps8_genus), country=="Malawi" & sample_type!="MS1"))`
#### UK - infant
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8_genus, genus=TRUE, input_country="UK", input_colour=UK_col)
ggMarginal(p, type = "density", fill=UK_col, alpha=0.5, colour=NA, size=8)
```
N: `r nrow(subset(sample_data(rps8_genus), country=="UK" & sample_type!="MS1"))`
#### India - maternal
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8_genus, genus=TRUE, input_country="India", input_colour=India_col, MS1=TRUE)
ggMarginal(p, type = "density", fill=India_col, alpha=0.5, colour=NA, size=8)
```
#### Malawi - maternal
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8_genus, genus=TRUE, input_country="Malawi", input_colour=Malawi_col, MS1=TRUE)
ggMarginal(p, type = "density", fill=Malawi_col, alpha=0.5, colour=NA, size=8)
```
#### UK - maternal
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8_genus, genus=TRUE, input_country="UK", input_colour=UK_col, MS1=TRUE)
ggMarginal(p, type = "density", fill=UK_col, alpha=0.5, colour=NA, size=8)
```
### RSV-level
#### India - infant
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8, genus=FALSE, input_country="India", input_colour=India_col)
ggMarginal(p, type = "density", fill=India_col, alpha=0.5, colour=NA, size=8)
```
#### Malawi - infant
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8, genus=FALSE, input_country="Malawi", input_colour=Malawi_col)
ggMarginal(p, type = "density", fill=Malawi_col, alpha=0.5, colour=NA, size=8)
```
#### UK - infant
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8, genus=FALSE, input_country="UK", input_colour=UK_col)
ggMarginal(p, type = "density", fill=UK_col, alpha=0.5, colour=NA, size=8)
```
#### India - maternal
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8, genus=FALSE, input_country="India", input_colour=India_col, MS1=TRUE)
ggMarginal(p, type = "density", fill=India_col, alpha=0.5, colour=NA, size=8)
```
#### Malawi - maternal
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8, genus=FALSE, input_country="Malawi", input_colour=Malawi_col, MS1=TRUE)
ggMarginal(p, type = "density", fill=Malawi_col, alpha=0.5, colour=NA, size=8)
```
#### UK - maternal
```{r, fig.width=4, fig.height=4}
p = taxon_abundance_function(rps8, genus=FALSE, input_country="UK", input_colour=UK_col, MS1=TRUE)
ggMarginal(p, type = "density", fill=UK_col, alpha=0.5, colour=NA, size=8)
```
Alpha diversity {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### Genus-level
#### India vs Malawi vs UK - Shannon
```{r, fig.width=7, fig.height=3}
# create dataframe of infant samples
t = data.frame(sample_data(rps8_genus))
#t_BS3 = subset(t, sample_type == "BS3" & country!="UK")
#write.csv(t_BS3, "t_BS3.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"))
# updated coding for country
t$country[t$country=="India"] = "IND"
t$country[t$country=="Malawi"] = "MLW"
t$country[t$country=="UK"] = "UK"
# select infant samples
t1 = subset(t, sample_type!="MS1")
# add-ons for longitudinal plots
gg_add = function(plot) { plot +
geom_jitter(size = 1, alpha = 0.2, width=0.25) +
geom_boxplot(alpha = 0.5, outlier.shape=NA) +
scale_color_manual("country", values = c(India_col, Malawi_col, UK_col)) +
xlab("") + theme(legend.position = "none") + facet_grid(.~week_fig) + theme(strip.background = element_blank()) +
theme(axis.text = element_text(size = 13), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
}
# create longitudinal plots for alpha diversity by country
#p1 = gg_add(ggplot(t1, aes(x = factor(week_fig), y = Shannon_rsv, group=family_ID, colour=factor(country))) + ylab("Shannon (variant)") + ylim(0,5))
gg_add(ggplot(t1, aes(x = factor(country), y = Shannon, colour=factor(country))) + ylab("Shannon"))
#grid.arrange(p1, p2, ncol=2)
```
#### India vs Malawi vs UK - richness
```{r, fig.width=7, fig.height=3}
# create longitudinal plots for alpha diversity by country
#p1 = gg_add(ggplot(t1, aes(x = factor(week_fig), y = Shannon_rsv, group=family_ID, colour=factor(country))) + ylab("Shannon (variant)") + ylim(0,5))
gg_add(ggplot(t1, aes(x = factor(country), y = Observed, colour=factor(country))) + ylab("richness")) + ylim(0,30)
#grid.arrange(p1, p2, ncol=2)
```
#### India vs Malawi vs UK with cross-sectional p values - Shannon
```{r, fig.width=8, fig.height=3}
p1 = ggplot(subset(t1, week_fig=="week of life 1"), aes(factor(country), y=Shannon, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.2, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) + ylab("Shannon") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) + facet_grid(.~week_fig) + ylim(0,3.5) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 3.25, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="***", y_position = 3, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 2.75, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
theme(axis.text = element_text(size = 13), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
p2 = ggplot(subset(t1, week_fig=="week of life 4"), aes(factor(country), y=Shannon, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.2, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) + ylab("") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) + facet_grid(.~week_fig) + ylim(0,3.5) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 3.25, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 3, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 2.75, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
theme(axis.text = element_text(size = 13), axis.text.y = element_text(color="white"), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
p3 = ggplot(subset(t1, week_fig=="week of life 6"), aes(factor(country), y=Shannon, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.2, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) + ylab("") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) + facet_grid(.~week_fig) + ylim(0,3.5) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 3.25, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 3, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="ns", y_position = 2.75, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
theme(axis.text = element_text(size = 13), axis.text.y = element_text(color="white"), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
p4 = ggplot(subset(t1, week_fig=="week of life 10"), aes(factor(country), y=Shannon, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.2, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) + ylab("") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) + facet_grid(.~week_fig) + ylim(0,3.5) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 3.25, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 3, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="ns", y_position = 2.75, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
theme(axis.text = element_text(size = 13), axis.text.y = element_text(color="white"), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
grid.arrange(p1, p2, p3, p4, ncol=4, padding= unit(0, "line"))
```
#### India vs Malawi vs UK with cross-sectional p values - richness
```{r, fig.width=8, fig.height=3}
p1 = ggplot(subset(t1, week_fig=="week of life 1"), aes(factor(country), y=Observed, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.2, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) + ylab("richness") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) + facet_grid(.~week_fig) + ylim(0,30) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 30, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="***", y_position = 27.5, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 25, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
theme(axis.text = element_text(size = 13), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
p2 = ggplot(subset(t1, week_fig=="week of life 4"), aes(factor(country), y=Observed, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.2, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) + ylab("") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) + facet_grid(.~week_fig) + ylim(0,30) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 30, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="*", y_position = 27.5, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 25, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
theme(axis.text = element_text(size = 13), axis.text.y = element_text(color="white"), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
p3 = ggplot(subset(t1, week_fig=="week of life 6"), aes(factor(country), y=Observed, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.2, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) + ylab("") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) + facet_grid(.~week_fig) + ylim(0,30) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 30, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 27.5, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 25, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
theme(axis.text = element_text(size = 13), axis.text.y = element_text(color="white"), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
p4 = ggplot(subset(t1, week_fig=="week of life 10"), aes(factor(country), y=Observed, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.2, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) + ylab("") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) + facet_grid(.~week_fig) + ylim(0,30) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="ns", y_position = 30, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="ns", y_position = 27.5, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="***", y_position = 25, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
theme(axis.text = element_text(size = 13), axis.text.y = element_text(color="white"), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
grid.arrange(p1, p2, p3, p4, ncol=4, padding= unit(0, "line"))
```
#### N by timepoint
```{r}
table(t1$sample_type, t1$country)
```
#### p values, longitudinal mixed-effects models (FDR-adjusted, 4 decimal places)
```{r}
# calculate lme models for pairwise comparisons using lme_alpha
p_shan = c(lme_alpha(subset(t1, country!="UK"), "Shannon"), lme_alpha(subset(t1, country!="MLW"), "Shannon"), lme_alpha(subset(t1, country!="IND"), "Shannon"))
p_obs = c(lme_alpha(subset(t1, country!="UK"), "log(Observed)"), lme_alpha(subset(t1, country!="MLW"), "log(Observed)"), lme_alpha(subset(t1, country!="IND"), "log(Observed)"))
results = data.frame(round(p.adjust(p_shan, method="fdr"),4), round(p.adjust(p_obs, method="fdr"),4))
names(results) = c("Shannon", "Richness")
rownames(results) = c("MLW-IND", "UK-IND", "UK-MLW")
results
```
#### p values, India (OPV) vs India (IPV)
```{r}
# calculate lme models for pairwise comparisons using lme_alpha
p_shan = lme_alpha(subset(t1, country=="IND"), "Shannon", comparison="country_arm")
p_obs = lme_alpha(subset(t1, country=="IND"), "log(Observed)", comparison="country_arm")
results = data.frame(round(p_shan,4), round(p_obs,4))
names(results) = c("Shannon", "Richness")
rownames(results) = c("India (OPV)-India (IPV)")
results
```
#### p values, cross-sectional models
##### week of life 1
```{r}
t1 = subset(t, sample_type=="BS1")
p_shan = TukeyHSD(x=aov(t1$Shannon ~ t1$country), "t1$country", conf.level=0.95)$`t1$country`[,4]
p_obs = TukeyHSD(x=aov(log(t1$Observed) ~ t1$country), "t1$country", conf.level=0.95)$`t1$country`[,4]
results = data.frame(round(p_shan, 4), round(p_obs, 4))
names(results) = c("Shannon", "Richness")
results
```
##### week of life 4
```{r}
t1 = subset(t, sample_type=="BS2")
p_shan = TukeyHSD(x=aov(t1$Shannon ~ t1$country), "t1$country", conf.level=0.95)$`t1$country`[,4]
p_obs = TukeyHSD(x=aov(log(t1$Observed) ~ t1$country), "t1$country", conf.level=0.95)$`t1$country`[,4]
results = data.frame(round(p_shan, 4), round(p_obs, 4))
names(results) = c("Shannon", "Richness")
results
```
##### week of life 6
```{r}
t1 = subset(t, sample_type=="BS3")
p_shan = TukeyHSD(x=aov(t1$Shannon ~ t1$country), "t1$country", conf.level=0.95)$`t1$country`[,4]
p_obs = TukeyHSD(x=aov(log(t1$Observed) ~ t1$country), "t1$country", conf.level=0.95)$`t1$country`[,4]
results = data.frame(round(p_shan, 4), round(p_obs, 4))
names(results) = c("Shannon", "Richness")
results
```
##### week of life 10
```{r}
t1 = subset(t, sample_type=="BS5")
p_shan = TukeyHSD(x=aov(t1$Shannon ~ t1$country), "t1$country", conf.level=0.95)$`t1$country`[,4]
p_obs = TukeyHSD(x=aov(log(t1$Observed) ~ t1$country), "t1$country", conf.level=0.95)$`t1$country`[,4]
results = data.frame(round(p_shan, 4), round(p_obs, 4))
names(results) = c("Shannon", "Richness")
results
```
#### Maternal samples - Shannon
```{r, fig.width=3, fig.height=4}
t1 = subset(t, sample_type=="MS1")
ggplot(t1, aes(factor(country), y=Shannon, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.2, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape=NA) + ylab("Shannon") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) + #ylim(0,90) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 4.5, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=India_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="**", y_position = 4.25, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="ns", y_position = 4, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
# scale_y_continuous(limits=c(0,225), breaks=c(0,50,100,150,200)) +
theme(axis.text = element_text(size = 13), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
```
#### Maternal samples - richness
```{r, fig.width=3, fig.height=4}
t1 = subset(t, sample_type=="MS1")
ggplot(t1, aes(factor(country), y=Observed, colour=factor(country))) + geom_jitter(size = 1.5, alpha = 0.2, width=0.3) +
geom_boxplot(alpha = 0.5, outlier.shape=NA) + ylab("richness") + xlab("") + theme(legend.position = "none", strip.background = element_blank()) + ylim(0,90) +
scale_colour_manual(values = c("IND" = India_col, "MLW" = Malawi_col, "UK" = UK_col)) +
geom_signif(comparisons = list(c("IND", "UK")), annotations="***", y_position = 87.5, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=India_col) +
geom_signif(comparisons = list(c("MLW", "UK")), annotations="***", y_position = 81.25, tip_length=0, vjust=0.5, size = 0.3, textsize=6, color=Malawi_col) +
geom_signif(comparisons = list(c("MLW", "IND")), annotations="ns", y_position = 75, tip_length=0, vjust=0, size = 0.3, textsize=4, color="grey") +
# scale_y_continuous(limits=c(0,225), breaks=c(0,50,100,150,200)) +
theme(axis.text = element_text(size = 13), strip.text = element_text(size = 13), axis.title = element_text(size = 13))
```
#### p values, India vs Malawi vs UK
```{r}
t1 = subset(t, sample_type=="MS1")
p_shan = TukeyHSD(x=aov(t1$Shannon ~ t1$country), "t1$country", conf.level=0.95)$`t1$country`[,4]
p_obs = TukeyHSD(x=aov(t1$Observed ~ t1$country), "t1$country", conf.level=0.95)$`t1$country`[,4]
results = data.frame(round(p_shan, 4), round(p_obs, 4))
names(results) = c("Shannon", "Richness")
results
```
#### p values, India (OPV) vs India (IPV)
```{r}
t1 = subset(t, sample_type=="MS1" & country=="IND")
p_shan = TukeyHSD(x=aov(t1$Shannon ~ t1$country_arm), "t1$country_arm", conf.level=0.95)$`t1$country_arm`[,4]
p_obs = TukeyHSD(x=aov(t1$Observed ~ t1$country_arm), "t1$country_arm", conf.level=0.95)$`t1$country_arm`[,4]
results = data.frame(round(p_shan, 4), round(p_obs, 4))
names(results) = c("Shannon", "Richness")
results
```
Longitudinal abundance plots {.hidden}
=====================================
#### Infants - India vs UK vs Malawi
```{r, fig.width=8, fig.height=4}
g = data.frame(t(otu_table(rps8_genus)))
# add metadata for sample type and country
if (all(rownames(g)==sample_names(rps8_genus))) {
g$sample_type = as.factor(sample_data(rps8_genus)$sample_type)
g$country_arm = as.factor(sample_data(rps8_genus)$country_arm)
} else { print("error: rownames do not match")}
# collate data
g_means_full = data.frame(
BS1_I = genus_mean_cal(g, "BS1", "India"),
BS2_I = genus_mean_cal(g, "BS2", "India"),
BS3_I = genus_mean_cal(g, "BS3", "India"),
BS5_I = genus_mean_cal(g, "BS5", "India"),
BS1_M = genus_mean_cal(g, "BS1", "Malawi"),
BS2_M = genus_mean_cal(g, "BS2", "Malawi"),
BS3_M = genus_mean_cal(g, "BS3", "Malawi"),
BS5_M = genus_mean_cal(g, "BS5", "Malawi"),
BS1_U = genus_mean_cal(g, "BS1", "UK"),
BS2_U = genus_mean_cal(g, "BS2", "UK"),
BS3_U = genus_mean_cal(g, "BS3", "UK"),
BS5_U = genus_mean_cal(g, "BS5", "UK"),
BS1_II = genus_mean_cal(g, "BS1", "India (IPV)"),
BS2_II = genus_mean_cal(g, "BS2", "India (IPV)"),
BS3_II = genus_mean_cal(g, "BS3", "India (IPV)"),
BS5_II = genus_mean_cal(g, "BS5", "India (IPV)"),
BS1_IO = genus_mean_cal(g, "BS1", "India (OPV)"),
BS2_IO = genus_mean_cal(g, "BS2", "India (OPV)"),
BS3_IO = genus_mean_cal(g, "BS3", "India (OPV)"),
BS5_IO = genus_mean_cal(g, "BS5", "India (OPV)"),
MS1_I = genus_mean_cal(g, "MS1", "India"),
MS1_M = genus_mean_cal(g, "MS1", "Malawi"),
MS1_U = genus_mean_cal(g, "MS1", "UK")
)
# create subset for Indian, Malawian and UK infant samples (columns 1:12)
g_means = g_means_full[,1:12]
# calculate means, pick genera present with mean abundance of 5% during at least one timepoint
g_means$max = apply(g_means, 1, max)
g_means$include = g_means$max>=0.05
g_means_reduced = subset(g_means, include==TRUE)[,1:(ncol(g_means)-2)]
taxa_list = rownames(g_means_reduced)
g_means_reduced["Other",] = 1-colSums(g_means_reduced)
g_means_reduced = data.frame(t(g_means_reduced))
# collate data for each taxon in taxa_list
g_collated = rbind(data.frame(Genus = rep("Other",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Other"]),
data.frame(Genus = rep("Parabacteroides",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Parabacteroides"]),
data.frame(Genus = rep("Prevotella 9",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Prevotella_9"]),
data.frame(Genus = rep("Klebsiella",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Klebsiella"]),
data.frame(Genus = rep("Lactobacillus",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Lactobacillus"]),
data.frame(Genus = rep("Veillonella",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Veillonella"]),
data.frame(Genus = rep("Bacteroides",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Bacteroides"]),
data.frame(Genus = rep("Staphylococcus",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Staphylococcus"]),
data.frame(Genus = rep("Streptococcus",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Streptococcus"]),
data.frame(Genus = rep("Escherichia/Shigella",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Escherichia.Shigella"]),
data.frame(Genus = rep("Bifidobacterium",12), week = rep(c("1", "4", "6", "10"),3),
country = c(rep("IND",4), rep("MLW",4), rep("UK",4)), abundance = g_means_reduced[,"Bifidobacterium"]))
# recode week
g_collated$week_factor <- factor(g_collated$week, levels = c("1", "4", "6", "10"))
g_collated$week <- as.numeric(g_collated$week_factor)
names(g_collated)[1] = "genus"
# create plot
ggplot(g_collated, aes(x=week, y=abundance, fill=genus)) +
geom_area() + facet_grid(.~country) +
scale_x_continuous(breaks = 1:4, labels = c("1", "4", "6", "10")) +
scale_fill_manual(values = c("light grey",as.vector(GetColors(11,"smooth rainbow")))) +
ylab("mean relative abundance") + xlab("week of life") +
scale_y_continuous(breaks=c(0,0.2,0.4,0.6,0.8,1)) +
theme(strip.background = element_blank()) +
theme(axis.text = element_text(size=13), strip.text = element_text(size=13), axis.title=element_text(size=13),
legend.title = element_text(size=13), legend.text = element_text(size=13))
```
#### N by timepoint
```{r}
table(sample_data(rps8_genus)$sample_type, sample_data(rps8_genus)$country)
```
#### Infants - India (OPV) vs India (IPV)
```{r, fig.width=6, fig.height=4}
# create subset for Indian OPV and IPV arms (columns 13:20)
g_means = g_means_full[,13:20]
g_means_reduced = g_means[taxa_list,]
g_means_reduced["Other",] = 1-colSums(g_means_reduced)
g_means_reduced = data.frame(t(g_means_reduced))
# collate data for each taxon in taxa_list
g_collated = rbind(data.frame(Genus = rep("Other",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Other"]),
data.frame(Genus = rep("Parabacteroides",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Parabacteroides"]),
data.frame(Genus = rep("Prevotella 9",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Prevotella_9"]),
data.frame(Genus = rep("Klebsiella",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Klebsiella"]),
data.frame(Genus = rep("Lactobacillus",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Lactobacillus"]),
data.frame(Genus = rep("Veillonella",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Veillonella"]),
data.frame(Genus = rep("Bacteroides",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Bacteroides"]),
data.frame(Genus = rep("Staphylococcus",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Staphylococcus"]),
data.frame(Genus = rep("Streptococcus",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Streptococcus"]),
data.frame(Genus = rep("Escherichia/Shigella",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Escherichia.Shigella"]),
data.frame(Genus = rep("Bifidobacterium",8), week = rep(c("1", "4", "6", "10"),2),
country = c(rep("IND (IPV)",4), rep("IND (OPV)",4)), abundance = g_means_reduced[,"Bifidobacterium"]))
# recode week
g_collated$week_factor <- factor(g_collated$week, levels = c("1", "4", "6", "10"))
g_collated$week <- as.numeric(g_collated$week_factor)
names(g_collated)[1] = "genus"
# create plot
ggplot(g_collated, aes(x=week, y=abundance, fill=genus)) +
geom_area() + facet_grid(.~country) +
scale_x_continuous(breaks = 1:4, labels = c("1", "4", "6", "10")) +
scale_y_continuous(breaks=c(0,0.2,0.4,0.6,0.8,1)) +
scale_fill_manual(values = c("light grey",as.vector(GetColors(11,"smooth rainbow")))) +
ylab("mean relative abundance") +
theme(strip.background = element_blank()) +
theme(axis.text = element_text(size=13), strip.text = element_text(size=13), axis.title=element_text(size=13),
legend.title = element_text(size=13), legend.text = element_text(size=13))
```
#### Maternal samples (with genera corresponding to infant plots included)
```{r, fig.width=5, fig.height=4}
# create subset for maternal samples (columns 21:23)
g_means = g_means_full[,21:23]
g_means_reduced = g_means[taxa_list,]
g_means_reduced["Other",] = 1-colSums(g_means_reduced)
g_means_reduced = data.frame(t(g_means_reduced))
# collate data for each taxon in taxa_list
g_collated = rbind(data.frame(Genus = rep("Other",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Other"]),
data.frame(Genus = rep("Parabacteroides",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Parabacteroides"]),
data.frame(Genus = rep("Prevotella 9",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Prevotella_9"]),
data.frame(Genus = rep("Klebsiella",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Klebsiella"]),
data.frame(Genus = rep("Lactobacillus",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Lactobacillus"]),
data.frame(Genus = rep("Veillonella",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Veillonella"]),
data.frame(Genus = rep("Bacteroides",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Bacteroides"]),
data.frame(Genus = rep("Staphylococcus",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Staphylococcus"]),
data.frame(Genus = rep("Streptococcus",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Streptococcus"]),
data.frame(Genus = rep("Escherichia/Shigella",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Escherichia.Shigella"]),
data.frame(Genus = rep("Bifidobacterium",3), week = rep(c("mother"),3), country = c("IND", "MLW", "UK"), abundance = g_means_reduced[,"Bifidobacterium"]))
names(g_collated)[1] = "genus"
# create plot
ggplot(g_collated, aes(x=country, y=abundance, fill=genus)) + geom_bar(stat = "identity") + xlab("") +
scale_fill_manual(values = c("light grey",as.vector(GetColors(11,"smooth rainbow")))) +
ylab("mean relative abundance") +
scale_y_continuous(breaks=c(0,0.2,0.4,0.6,0.8,1)) +
theme(axis.text = element_text(size=13), strip.text = element_text(size=13), axis.title=element_text(size=13),
legend.title = element_text(size=13), legend.text = element_text(size=13))
```
Beta diversity {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### PERMANOVA - unweighted
##### Inter-individual differences among all infant samples
```{r}
# inter-individual
ps_t = subset_samples(rps8_genus, sample_type=="BS1" | sample_type=="BS2" | sample_type=="BS3" | sample_type=="BS5")
t1 = data.frame(sample_data(ps_t))
otu_table(ps_t)[otu_table(ps_t)<0.001] = 0
beta_dist = phyloseq::distance(ps_t, method = "bray", binary=TRUE)
adon_BS = adonis(beta_dist ~ family_ID, data = t1, permutations = adonis_iter)$aov.tab
adon_BS
```
##### Differences by country stratified by age
```{r}
# BS1
ps_t = subset_samples(rps8_genus, sample_type=="BS1")
t1 = data.frame(sample_data(ps_t))
otu_table(ps_t)[otu_table(ps_t)<0.001] = 0
beta_dist = phyloseq::distance(ps_t, method = "bray", binary=TRUE)
adon_BS1 = adonis(beta_dist ~ country, data = t1, permutations = adonis_iter)$aov.tab
# BS2
ps_t = subset_samples(rps8_genus, sample_type=="BS2")
t2 = data.frame(sample_data(ps_t))
otu_table(ps_t)[otu_table(ps_t)<0.001] = 0
beta_dist = phyloseq::distance(ps_t, method = "bray", binary=TRUE)
adon_BS2 = adonis(beta_dist ~ country, data = t2, permutations = adonis_iter)$aov.tab
# BS3
ps_t = subset_samples(rps8_genus, sample_type=="BS3")
t3 = data.frame(sample_data(ps_t))
otu_table(ps_t)[otu_table(ps_t)<0.001] = 0
beta_dist = phyloseq::distance(ps_t, method = "bray", binary=TRUE)
adon_BS3 = adonis(beta_dist ~ country, data = t3, permutations = adonis_iter)$aov.tab
# BS5
ps_t = subset_samples(rps8_genus, sample_type=="BS5")
t5 = data.frame(sample_data(ps_t))
otu_table(ps_t)[otu_table(ps_t)<0.001] = 0
beta_dist = phyloseq::distance(ps_t, method = "bray", binary=TRUE)
adon_BS5 = adonis(beta_dist ~ country, data = t5, permutations = adonis_iter)$aov.tab
# MS1
ps_t = subset_samples(rps8_genus, sample_type=="MS1")
tm = data.frame(sample_data(ps_t))
otu_table(ps_t)[otu_table(ps_t)<0.001] = 0
beta_dist = phyloseq::distance(ps_t, method = "bray", binary=TRUE)
adon_MS1 = adonis(beta_dist ~ country, data = tm, permutations = adonis_iter)$aov.tab
collated = data.frame(
week = c("week of life 1", "week of life 4", "week of life 6", "week of life 10", "mother"),
R2 = c(adon_BS1$R2[1], adon_BS2$R2[1], adon_BS3$R2[1], adon_BS5$R2[1], adon_MS1$R2[1]),
p = c(adon_BS1$`Pr(>F)`[1], adon_BS2$`Pr(>F)`[1], adon_BS3$`Pr(>F)`[1], adon_BS5$`Pr(>F)`[1], adon_MS1$`Pr(>F)`[1]),
n = c(nrow(t1), nrow(t2), nrow(t3), nrow(t5), nrow(tm))
)
collated
```
#### Summary plot
```{r, fig.height=3, fig.width=4}
collated$week = factor(collated$week, levels = c("week of life 1", "week of life 4", "week of life 6", "week of life 10", "mother"))
ggplot(collated, aes(y = R2*100, x=week, fill = week )) + geom_bar(stat = "identity") +
scale_x_discrete(limits = rev(levels(collated$week))) + xlab("") + ylab(bquote(R^2~"(%)")) +
scale_y_continuous(limits=c(0,25), breaks=c(0,10,20)) +
coord_flip() + theme(legend.position = "none") + scale_fill_manual(values = rep("#0072B2",5)) +
theme(plot.title = element_text(size=15), axis.text = element_text(size=15), axis.title = element_text(size=15), strip.text = element_text(size=15)) +
theme(strip.background = element_blank())
```
### PERMANOVA - weighted
##### Inter-individual differences among all infant samples
```{r}
# inter-individual
ps_t = subset_samples(rps8_genus, sample_type=="BS1" | sample_type=="BS2" | sample_type=="BS3" | sample_type=="BS5")
t1 = data.frame(sample_data(ps_t))
beta_dist = phyloseq::distance(ps_t, method = "bray")
adon_BS = adonis(beta_dist ~ family_ID, data = t1, permutations = adonis_iter)$aov.tab
adon_BS
```
##### Differences by country stratified by age
```{r}
# BS1
ps_t = subset_samples(rps8_genus, sample_type=="BS1")
t1 = data.frame(sample_data(ps_t))
beta_dist = phyloseq::distance(ps_t, method = "bray")
adon_BS1 = adonis(beta_dist ~ country, data = t1, permutations = adonis_iter)$aov.tab
# BS2
ps_t = subset_samples(rps8_genus, sample_type=="BS2")
t2 = data.frame(sample_data(ps_t))
beta_dist = phyloseq::distance(ps_t, method = "bray")
adon_BS2 = adonis(beta_dist ~ country, data = t2, permutations = adonis_iter)$aov.tab
# BS3
ps_t = subset_samples(rps8_genus, sample_type=="BS3")
t3 = data.frame(sample_data(ps_t))
beta_dist = phyloseq::distance(ps_t, method = "bray")
adon_BS3 = adonis(beta_dist ~ country, data = t3, permutations = adonis_iter)$aov.tab
# BS5
ps_t = subset_samples(rps8_genus, sample_type=="BS5")
t5 = data.frame(sample_data(ps_t))
beta_dist = phyloseq::distance(ps_t, method = "bray")
adon_BS5 = adonis(beta_dist ~ country, data = t5, permutations = adonis_iter)$aov.tab
# MS1
ps_t = subset_samples(rps8_genus, sample_type=="MS1")
tm = data.frame(sample_data(ps_t))
beta_dist = phyloseq::distance(ps_t, method = "bray")
adon_MS1 = adonis(beta_dist ~ country, data = tm, permutations = adonis_iter)$aov.tab
collated = data.frame(
week = c("week of life 1", "week of life 4", "week of life 6", "week of life 10", "mother"),
R2 = c(adon_BS1$R2[1], adon_BS2$R2[1], adon_BS3$R2[1], adon_BS5$R2[1], adon_MS1$R2[1]),
p = c(adon_BS1$`Pr(>F)`[1], adon_BS2$`Pr(>F)`[1], adon_BS3$`Pr(>F)`[1], adon_BS5$`Pr(>F)`[1], adon_MS1$`Pr(>F)`[1]),
n = c(nrow(t1), nrow(t2), nrow(t3), nrow(t5), nrow(tm))
)
collated
```
#### Summary plot
```{r, fig.height=3, fig.width=4}
collated$week = factor(collated$week, levels = c("week of life 1", "week of life 4", "week of life 6", "week of life 10", "mother"))
ggplot(collated, aes(y = R2*100, x=week, fill = week )) + geom_bar(stat = "identity") +
scale_x_discrete(limits = rev(levels(collated$week))) + xlab("") + ylab(bquote(R^2~"(%)")) +
scale_y_continuous(limits=c(0,25), breaks=c(0,10,20)) +
coord_flip() + theme(legend.position = "none") + scale_fill_manual(values = rep("#0072B2",5)) +
theme(plot.title = element_text(size=15), axis.text = element_text(size=15), axis.title = element_text(size=15), strip.text = element_text(size=15)) +
theme(strip.background = element_blank())
```
#### Turnover, paired vs random (weighted)
For week 1, distance from maternal sample plotted. For subsequent samples, distance from prior infant sample plotted.
```{r, fig.width=9, fig.height=7}
# create dataframe, and recode week
t1 = subset(t, sample_type!="MS1")
within = data.frame(sample_ID_full = t1$sample_ID_full, country = t1$country, week_fig = t1$week_fig, turnover=t1$turnover_w, within = rep("paired", nrow(t1)))
between = data.frame(sample_ID_full = t1$sample_ID_full, country = t1$country, week_fig = t1$week_fig, turnover=t1$turnover_randompair_w, within = rep("random", nrow(t1)))
full = rbind(within, between)
full$week_fig = factor(full$week_fig, levels = c("week of life 1","week of life 4","week of life 6","week of life 10"))
# create plot
ggplot(full, aes(factor(within), turnover, colour=factor(within))) + geom_jitter(size = 1, alpha = 0.2, width=0.2) +
geom_boxplot(alpha = 0.5) + facet_grid(country~week_fig) +
ylab("turnover (weighted)") + xlab("") + scale_color_manual(values = c("#01665e", "#d8b365")) +
stat_compare_means(label.y = 1.1, size=4) +
scale_y_continuous(limits = c(0,1.2), breaks=c(0,0.4,0.8,1.2)) +
theme_bw() + theme(legend.position = "none", strip.background = element_blank()) +
theme(axis.text = element_text(size = 13), axis.title = element_text(size = 13), plot.title = element_text(size = 13), strip.text = element_text(size = 13))
```
#### Turnover, paired vs random (unweighted)
```{r, fig.width=9, fig.height=7}
# create dataframe, and recode week
t1 = subset(t, sample_type!="MS1")
within = data.frame(sample_ID_full = t1$sample_ID_full, country = t1$country, week_fig = t1$week_fig, turnover=t1$turnover_u, within = rep("paired", nrow(t1)))
between = data.frame(sample_ID_full = t1$sample_ID_full, country = t1$country, week_fig = t1$week_fig, turnover=t1$turnover_randompair_u, within = rep("random", nrow(t1)))
full = rbind(within, between)
full$week_fig = factor(full$week_fig, levels = c("week of life 1","week of life 4","week of life 6","week of life 10"))
# create plot
ggplot(full, aes(factor(within), turnover, colour=factor(within))) + geom_jitter(size = 1, alpha = 0.2, width=0.2) +
geom_boxplot(alpha = 0.5) + facet_grid(country~week_fig) +
ylab("turnover (weighted)") + xlab("") + scale_color_manual(values = c("#01665e", "#d8b365")) +
stat_compare_means(label.y = 1.1, size=4) +
scale_y_continuous(limits = c(0,1.2), breaks=c(0,0.4,0.8,1.2)) +
theme_bw() + theme(legend.position = "none", strip.background = element_blank()) +
theme(axis.text = element_text(size = 13), axis.title = element_text(size = 13), plot.title = element_text(size = 13), strip.text = element_text(size = 13))
```
Longitudinal samples from a given infant more closely clustered than expected by chance in each of the three cohorts.
Differential taxa {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### Genus-level
```{r}
set.seed(12345)
### select input data and create specify output comparisons
input_m = data.frame(sample_data(rps8_genus))
comparison_list = data.frame(country1 = c("India", "India", "Malawi", "India (OPV)"),
country2 = c("Malawi", "UK", "UK", "India (IPV)"))
subset_list = c("BS1", "BS2", "BS3", "BS5", "MS1")
# create dataframe for collated accuracy results
collated_rf = data.frame(subset = rep(NA,5*4))
collated_rf$subset = collated_rf$country1 = collated_rf$country2 = collated_rf$comparison = collated_rf$crossval_median = collated_rf$crossval_q1 = collated_rf$crossval_q3 = NA
if (run_rf_full) {
# run RF in loop across 5 sample types and 4 country-country comparisons
for (s in 1:5) {
for (c in 1:4) {
subset = subset_list[s]
country1 = comparison_list$country1[c]
country2 = comparison_list$country2[c]
if (country1=="India (OPV)") {
samplelist = subset(input_m, (country_arm==country1 | country_arm==country2) & sample_type==subset)$sample_ID_full
} else {
samplelist = subset(input_m, (country==country1 | country==country2) & sample_type==subset)$sample_ID_full
}
input_rps = prune_samples(samplelist, rps8_genus)
input_ps = prune_samples(samplelist, ps7_genus)
if (country1=="India") {
sample_data(input_rps)$country_arm = sample_data(input_rps)$country
sample_data(input_ps)$country_arm = sample_data(input_ps)$country
}
rf = run_rf(input_rps, input_ps)
collated_rf$subset[4*(s-1)+c] = subset
collated_rf$country1[4*(s-1)+c] = as.character(country1)
collated_rf$country2[4*(s-1)+c] = as.character(country2)
collated_rf$comparison[4*(s-1)+c] = paste0(country1,"-",country2)
collated_rf$crossval_median[4*(s-1)+c] = rf$gini.cv$crossval_median[1]
collated_rf$crossval_q1[4*(s-1)+c] = rf$gini.cv$crossval_quartile_1[1]
collated_rf$crossval_q3[4*(s-1)+c] = rf$gini.cv$crossval_quartile_3[1]
}
}
write.csv(collated_rf,"output_module3/crossval_collated_country_genus.csv")
}
collated_rf_full = read.csv("output_module3/crossval_collated_country_genus.csv", row.names=1)
```
#### Cross-validation accuracy, India vs Malawi vs UK
```{r, fig.width=10, fig.height=3.5}
collated_rf_full$week = revalue(as.factor(collated_rf_full$subset), c("BS1" = "week of life 1", "BS2" = "week of life 4", "BS3" = "week of life 6", "BS5" = "week of life 10", "MS1" = "mother"))
collated_rf = subset(collated_rf_full, country2!="India (IPV)")
# recode comparison
collated_rf$comparison = as.character(collated_rf$comparison)
collated_rf$comparison[collated_rf$comparison=="India-Malawi"] = "IND-MLW"
collated_rf$comparison[collated_rf$comparison=="India-UK"] = "IND-UK"
collated_rf$comparison[collated_rf$comparison=="Malawi-UK"] = "MLW-UK"
ggplot(collated_rf, aes(y = crossval_median, x = comparison, colour=factor(comparison))) +
geom_point(size = 5, alpha=0.8) +
geom_errorbar(aes(ymin=crossval_q1, ymax=crossval_q3), width=0) +
facet_grid(.~week) + geom_hline(yintercept = 0.5, linetype="dotted") +
scale_y_continuous(limits=c(0.4, 1), oob=rescale_none, breaks=c(0.5,0.75,1)) + #coord_flip() +
scale_colour_manual(values = as.vector(GetColors(8,"sunset", reverse=T))) +
theme(legend.position = "") + xlab("") + ylab("CV accuracy") +
theme(strip.background = element_blank()) +
theme(axis.text.x = element_text(size = 15, angle = 90, vjust = 0.5)) +
theme(axis.text.y = element_text(size = 18), axis.title = element_text(size = 18), plot.title = element_text(size = 13),
strip.text = element_text(size = 18))
```
#### Pre-vaccination comparisons
```{r, fig.width=12, fig.height=17}
IM_BS3 = read.csv("output_module3/RF_output_importance/crossval_gini_India_Malawi_BS3_genus.csv", row.names=1)
IU_BS3 = read.csv("output_module3/RF_output_importance/crossval_gini_India_UK_BS3_genus.csv", row.names=1)
MU_BS3 = read.csv("output_module3/RF_output_importance/crossval_gini_Malawi_UK_BS3_genus.csv", row.names=1)
gini = plot_grid(gini_plot(IM_BS3, India_col, Malawi_col, species=F) + ggtitle("week of life 6") + ylab(" ") + scale_y_continuous(breaks = c(0,1,2), limits = c(0,2)),
gini_plot(IU_BS3, India_col, UK_col, species=F) + ggtitle(" ") + ylab(" ") + scale_y_continuous(breaks = c(0,1,2), limits = c(0,2)),
gini_plot(MU_BS3, Malawi_col, UK_col,species=F) + ggtitle(" ") + scale_y_continuous(breaks = c(0,1,2), limits = c(0,2)) +
theme(axis.title = element_text(size=22)),
ncol=1, align = "v")
prev = plot_grid(rf_prev_plot(IM_BS3, label1="India", colour1 = India_col,
label2 = "Malawi", colour2=Malawi_col, species=F) + ggtitle(" ") + ylab(" "),
rf_prev_plot(IU_BS3, label1="India", colour1 = India_col,
label2 = "UK", colour2=UK_col, species=F) + ggtitle(" ") + ylab(" "),
rf_prev_plot(MU_BS3, label1="Malawi", colour1 = Malawi_col,
label2 = "UK", colour2=UK_col, species=F) + ggtitle(" ") + theme(axis.title = element_text(size=22)), ncol=1, align = "v")
grid.arrange(gini, prev, ncol=2, widths=c(2,1.4))
```
#### Differential abundance plots - significant by Fisher's test or Aldex2 (FDR p <0.05)
```{r, fig.width=9, fig.height=6}
plot_grid(
summary_abund_plot("India","IND", "Malawi", "MLW", India_col, Malawi_col, "genus", threshold=0.05, yshift=3, ymax=100) + xlab(" ") + ylab("number of genera") +
scale_y_continuous(limits = c(0,100), breaks = c(0,25,50,75)),
summary_abund_plot("India", "IND", "UK", "UK", India_col, UK_col, "genus", threshold=0.05, yshift=3, ymax=100) + ylab(" ") +
scale_y_continuous(limits = c(0,100), breaks = c(0,25,50,75)),
summary_abund_plot("Malawi", "MLW", "UK", "UK", Malawi_col, UK_col, "genus", threshold=0.05, yshift=3, ymax=100) + ylab(" ") + xlab(" ") +
scale_y_continuous(limits = c(0,100), breaks = c(0,25,50,75)),
ncol=3)
```
Longitudinal abundance comparisons {.hidden}
=====================================
Column {.tabset .tabset-fade}
-------------------------------------
### Genus-level
```{r}
# select subsets of data
t = data.frame(as(tax_table(ps7_genus), "matrix"))
t_rsv = data.frame(as(tax_table(ps7_l_t_pr_d_dd_25k), "matrix"))
# append sequence of most common rsv to each genus
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$sequence[1]) # pick out top sequence (previously ordered by overall abundance)
}
#all(rownames(t) == taxa_names(ps6_genus))
t1 = tax_table(t)
rownames(t1) = rownames(t)
colnames(t1) = names(t)
tax_table(ps7_genus) = t1
# pick out subset of taxa present in at least 25% of infant samples in at least one country
ps7_I = subset_samples(ps7_genus, sample_type!="MS1" & country=="India") %>%
prune_taxa(rowSums(otu_table(.)>0)/nsamples(.)>=0.20, .)
ps7_U = subset_samples(ps7_genus, sample_type!="MS1" & country_arm=="UK") %>%
prune_taxa(rowSums(otu_table(.)>0)/nsamples(.)>=0.20, .)
ps7_M = subset_samples(ps7_genus, sample_type!="MS1" & country_arm=="Malawi") %>%
prune_taxa(rowSums(otu_table(.)>0)/nsamples(.)>=0.20, .)
taxa_list = unique(c(taxa_names(ps7_I), taxa_names(ps7_U), taxa_names(ps7_M)))
ps7_t = prune_taxa(taxa_list, ps7_genus)
# determine mean abundances
rps7_t = transform_sample_counts(ps7_t, function(x) {x/sum(x)})
rps7_t = subset_samples(rps7_t, sample_type!="MS1")
means = data.frame(genus = tax_table(rps7_t)[,"Genus"], means = rowMeans(otu_table(rps7_t)))
names(means)[1] = "genus"
```
#### India vs UK
```{r}
if (run_nb_full) {
# pick out subset of samples and taxa
ps7_t1 = subset_samples(ps7_t, sample_type!="MS1" & (country=="India" | country=="UK"))
ps7_t1 = prune_taxa(c(taxa_names(ps7_I), taxa_names(ps7_U)), ps7_t1)
# create feature table and pick out metadata variables
features = data.frame(t(as(otu_table(ps7_t1), "matrix")))
if (all(colnames(features) == rownames(tax_table(ps7_t1)))) {
colnames(features) = data.frame(as(tax_table(ps7_t1), "matrix"))$Genus
}
meta = data.frame(sample_data(ps7_t1))
N = meta$count_ps7
week = meta$week
country = meta$country
subject = meta$family_ID
# ZINB models (all taxa)
f = mms(y = features, fixed = ~ country + week + offset(log(N)),
random = ~ 1 | subject, min.p = 0, method = "zinb", verbose=TRUE)
res = data.frame(get.fixed(f, part="dist", vr.name="countryUK", sort.p=F))
res$method = "zinb"
# negative binomials without zero inflation (if prevalence >95%)
f_nb <- mms(y = features, fixed = ~ country + week + offset(log(N)),
random = ~ 1 | subject, min.p = 0.95, method = "nb", verbose=TRUE)
res_nb = data.frame(get.fixed(f_nb, part="dist", vr.name="countryUK", sort.p=F))
res_nb$method = "nb"
# replace ZINB with negative binomial model outputs for relevant taxa
for (i in 1:nrow(res_nb)){
if (rownames(res_nb)[i] %in% rownames(res)) {
res[rownames(res_nb)[i],] = res_nb[i,]
} else { res = rbind(res, res_nb[i,]) }
}
# collate results
res$genus = rownames(res)
res$padj = p.adjust(res$pvalue, method="BH")