In nearinj/Healthy_Oral_Microbiome: Analysis for Healthy Oral Microbiome Atlantic PATH Cohort

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(HealthyOralMicrobiome)

#Load in metadata
Metadata <- HealthyOralMicrobiome::Healthy_Metadata
dim(Metadata)



ASV_Rare <- HealthyOralMicrobiome::Healthy_ASV_table_Rare
dim(ASV_Rare)



Genus_Rare <- HealthyOralMicrobiome::Healthy_Genus_Rare

### Convert Genus to R.A

complete_genus_RA <- sweep(Genus_Rare, 2, colSums(Genus_Rare), '/')

core_list <- list()
core_list[[1]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.05)
core_list[[2]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.1)
core_list[[3]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.15)
core_list[[4]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.20)
core_list[[5]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.25)
core_list[[6]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.30)
core_list[[7]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.35)
core_list[[8]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.4)
core_list[[9]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.45)
core_list[[10]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.5)
core_list[[11]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.55)
core_list[[12]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.6)
core_list[[13]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.65)
core_list[[14]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.70)
core_list[[15]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.75)
core_list[[16]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.80)
core_list[[17]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.85)
core_list[[18]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.90)
core_list[[19]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.95)
core_list[[20]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.96)
core_list[[21]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.97)
core_list[[22]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.98)
core_list[[23]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 0.99)
core_list[[24]] <- HealthyOralMicrobiome::get_core_features(complete_genus_RA, 1)

core_abundance <- vector()

for(i in core_list){

  cor_RA <- complete_genus_RA[i,]
  core_abundance <- c(core_abundance, mean(colSums(cor_RA)))

}

num_genus <- vector()

for(i in 1:length(core_list)){

  num_genus[[i]] <- length(which(core_list[[i]]))

}

cutoffs <- c(.05, 0.1, 0.15, .2, .25, .3, .35, .4, .45, .5, .55, .6, .65, .7, .75, 0.80, 0.85, 0.90, 0.95, 0.96, 0.97, 0.98, 0.99, 1)

complete_ASV_RA <- sweep(ASV_Rare, 2, colSums(ASV_Rare), '/')

core_list_ASV <- list()
core_list_ASV[[1]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.05)
core_list_ASV[[2]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.1)
core_list_ASV[[3]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.15)
core_list_ASV[[4]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.20)
core_list_ASV[[5]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.25)
core_list_ASV[[6]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.30)
core_list_ASV[[7]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.35)
core_list_ASV[[8]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.4)
core_list_ASV[[9]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.45)
core_list_ASV[[10]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.5)
core_list_ASV[[11]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.55)
core_list_ASV[[12]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.6)
core_list_ASV[[13]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.65)
core_list_ASV[[14]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.70)
core_list_ASV[[15]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.75)
core_list_ASV[[16]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.80)
core_list_ASV[[17]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.85)
core_list_ASV[[18]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.90)
core_list_ASV[[19]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.95)
core_list_ASV[[20]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.96)
core_list_ASV[[21]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.97)
core_list_ASV[[22]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.98)
core_list_ASV[[23]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 0.99)
core_list_ASV[[24]] <- HealthyOralMicrobiome::get_core_features(complete_ASV_RA, 1)

core_abundance_ASV <- vector()

for(i in core_list_ASV){


  cor_RA <- complete_ASV_RA[i,]
  core_abundance_ASV <- c(core_abundance_ASV, mean(colSums(cor_RA)))

}

num_ASV <- vector()
for(i in 1:length(core_list_ASV)){

  num_ASV[[i]] <- length(which(core_list_ASV[[i]]))

}

complete_genus_RA <- sweep(Genus_Rare, 2, colSums(Genus_Rare), '/')

#get mean RA for each genus
Mean_genus_RA <- apply(complete_genus_RA, 1, function(x) mean(x))

#set anything under 1% RA to Other
other_feat_len <- length(which(Mean_genus_RA <= 0.01))


rownames(complete_genus_RA)[which(Mean_genus_RA < 0.01)] <- make.unique(rep("Other", other_feat_len), "_")

rownames(complete_genus_RA) <- gsub(".*;D_4__", "", rownames(complete_genus_RA))
rownames(complete_genus_RA) <- gsub(";D_5__", " ", rownames(complete_genus_RA))

mean(colSums(complete_genus_RA[grep(' ', rownames(complete_genus_RA)),]))

library(reshape2)
#load in betadiv to generate PC cords
W_unifrac <- HealthyOralMicrobiome::get_beta_div("w_unifrac", "whole", Metadata)
dim(W_unifrac[["Distance"]])
### PCoA
W_unifrac_PCoA <- cmdscale(W_unifrac[["Distance"]], k=2, eig=T)
PC_cords <- W_unifrac_PCoA$points
identical(rownames(PC_cords), colnames(complete_genus_RA))




melt_complete_genus_RA <- melt(as.matrix(complete_genus_RA))

### squish Others together
melt_complete_genus_RA$Var1 <- gsub("Other_.*", "Other", melt_complete_genus_RA$Var1)

aggregate(melt_complete_genus_RA$value, by=list(melt_complete_genus_RA$Var2, melt_complete_genus_RA$Var1), function(x) sum(x))

squished_melt_df <- aggregate(melt_complete_genus_RA$value, by=list(melt_complete_genus_RA$Var2, melt_complete_genus_RA$Var1), function(x) sum(x))

#assign PC cords to each samples
squished_melt_df$PC_cord <- -100
for(i in 1:length(PC_cords[,1])){
  for(j in 1:17833){
    if(squished_melt_df$Group.1[j]==names(PC_cords[,1])[i]){
      squished_melt_df$PC_cord[j] <- PC_cords[,1][i]
    }
  }
}

library(cowplot)
library(ggplot2)
theme_set(theme_cowplot())

## Order each sample based on PC1 coord
squished_melt_df$sample_num <- as.numeric(as.factor(squished_melt_df$PC_cord))

test <- factor(squished_melt_df$sample_num, levels = 1:1049)
squished_melt_df$test <- test

cols <- c('#e6194B', '#3cb44b', '#f58231', '#911eb4', '#42d4f4', '#f032e6', '#bfef45', '#fabebe', '#469990', '#e6beff', '#9A6324', '#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1', '#000075', '#a9a9a9')


mean_RAs <- aggregate(squished_melt_df$x, by=list(squished_melt_df$Group.2), function(x) mean(x))
mean_RAs <- mean_RAs[order(mean_RAs$x),]
mean_RAs

### Names 
squished_melt_df$Group.2 <- factor(squished_melt_df$Group.2, levels=c("Other", "Veillonellaceae Veillonella", "Neisseriaceae Neisseria",
                                                              "Streptococcaceae Streptococcus", "Prevotellaceae Prevotella 7", "Pasteurellaceae Haemophilus",
                                                              "Porphyromonadaceae Porphyromonas", "Fusobacteriaceae Fusobacterium", 
                                                              "Prevotellaceae Prevotella", "Flavobacteriaceae Capnocytophaga", 
                                                              "Veillonellaceae Megasphaera", "Carnobacteriaceae Granulicatella",
                                                              "Leptotrichiaceae Leptotrichia", "Family XI Gemella", "Prevotellaceae Alloprevotella",
                                                              "Prevotellaceae Prevotella 6", "Actinomycetaceae Actinomyces"))

effect_gg_plot <- ggplot(squished_melt_df, aes(x=test, y=x, fill=Group.2)) + geom_bar(stat="identity", width=1) + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.line.x = element_blank(), axis.line.y=element_blank(), axis.title.x = element_text(vjust=10)) + scale_fill_manual(values = cols) +xlab("Sample") +ylab("Relative Abundance") + labs(fill="Genera")
effect_gg_plot

library(ggplot2)
library(cowplot)

ASV_df <- data.frame(Relative_Abundance=core_abundance_ASV,
                     Presence_cutoff=cutoffs,
                     num_ASVs=num_ASV)

Genus_df <- data.frame(Relative_Abundance=core_abundance,
                       Presence_cutoff=cutoffs,
                       num_genus=num_genus)



theme_set(theme_cowplot())

ASV_plot <- ggplot(ASV_df, aes(x=Presence_cutoff, y=Relative_Abundance)) + geom_point() + xlab("Presence cutoff") + ylab("Relative Abundance") +
  ggtitle("Amplicon Sequence Variants") + ylim(c(0,1))
ASV_plot

Genus_plot <- ggplot(Genus_df, aes(x=Presence_cutoff, y=Relative_Abundance)) + geom_point() + xlab("Presence cutoff") + ylab("Relative Abundance") +
  ggtitle("Genus") + ylim(c(0,1))
Genus_plot


ASV_plot_number <- ggplot(ASV_df[-c(1:9),], aes(x=Presence_cutoff, y=num_ASVs)) + geom_point() + xlab("Presence cutoff") + ylab("Number of ASVs") +
  ggtitle("Amplicon Sequence Variants")
ASV_plot_number

subpanel1 <- plot_grid(Genus_plot, ASV_plot, labels=c("B", "C"), ncol=1)

Genus_plot_number <- ggplot(Genus_df[-c(1:9),], aes(x=Presence_cutoff, y=num_genus)) + geom_point() + xlab("Presence cutoff") + ylab("Number of Genera") +
  ggtitle("Genus")

Genus_plot_number

library(cowplot)
theme_set(theme_cowplot())

sup_fig_2 <- plot_grid(Genus_plot_number, ASV_plot_number, labels = c("B", "C"), ncol=1)

sup_fig_2

### Final plot

Figure1 <- plot_grid(effect_gg_plot, subpanel1, labels = c("A", ""))
Figure1

nearinj/Healthy_Oral_Microbiome documentation built on April 3, 2020, 12:58 a.m.