Introduction

Compositions

MEAT
CHEESE
WINE
BREAD

Comparison of markers

MEAT
CHEESE
WINE
BREAD

List of ASVs

library(DT)
library(ggpubr)
library(kableExtra)
library(phyloseq)
library(phyloseq.extended)
library(tidyverse)
library(plotly)
library(tidyverse)
library(ggVennDiagram)
library(ggplot2)

source("curation.R")

All data used to produce this document are available on this repository: https://forgemia.inra.fr/migale/metabarfood/. The code used is available in the code chunks.

Introduction

This report shows the analyses performed on real samples of the METABARFOOD project with de DADA2_FROGS approach.

Questions

Composition of each sample?
Is the composition different according to the marker used?

raw_data <- read.csv2("data/metadata_real_reads.tsv" , sep = "\t", stringsAsFactors = F) %>% as_tibble()

Compositions

Diversity of real samples before and after a manual and expert curation are represented in the following figures.

ecos <- c("MEAT","CHEESE","WINE","BREAD")
markers <- c("ITS1","ITS2","D1D2","RPB2")
for(eco in ecos){
  dir <- paste("REAL",eco, sep="_")
  cat("\n## ",eco," {.tabset}\n\n")
  for(marker in markers){
    if(marker=="D1D2"){
      cat("\n### ", "D1/D2", "\n\n")
    }else{
      cat("\n### ", marker, "\n\n")  
    }
      
    physeq <- readRDS(file.path(dir,paste0(marker,".rds")))
    physeq_final <- readRDS(file.path(dir,paste0(marker,"_final.rds")))
    taxa_names(physeq) <- paste(taxa_names(physeq), "2", sep = "_")
    physeq_all <- merge_phyloseq(physeq_final, physeq)
    cat("\n**Abundances** \n")
    p <- plot_bar(physeq_all, x="Type") + facet_grid(~SampleID)
    p$data$Type <- factor(p$data$Type, levels = c("Raw","Curated"))
    print(p)
    cat(" \n")
    cat("\n**Compositions** \n")
    p <- plot_composition(physeq_all, "Kingdom", "Fungi", "Family", x = "Type") +
      scale_y_continuous(expand = expansion(0, 0)) + 
      scale_x_discrete(expand = expansion(0, 0)) + 
      facet_grid(~ SampleID) 
    p$data$Type <- factor(p$data$Type, levels = c("Raw","Curated"))
    print(p)
    cat("\n")
    p <- plot_composition(physeq_all, "Kingdom", "Fungi", "Genus", x = "Type") +
      scale_y_continuous(expand = expansion(0, 0)) + 
      scale_x_discrete(expand = expansion(0, 0)) + 
      facet_grid(~ SampleID) 
    p$data$Type <- factor(p$data$Type, levels = c("Raw","Curated"))
    print(p)
    cat("\n")
    cat("\n**Alpha-diversity** \n")
    p <- plot_richness(physeq = physeq_all, x="SampleID", color = "Type", shape = NULL, title = "Alpha diversity graphics", measures = c("Observed", "Chao1", "ACE", "Shannon", "Simpson", "InvSimpson"))
    p$data$Type <- factor(p$data$Type, levels = c("Raw","Curated"))
    p <- p + geom_boxplot() + NULL
    print(p)
    cat("\n")
    
    #cat("\n#### beta-diversity \n")
    #beta.dist <- distance(physeq_all, method = "bray")
    #ord <- ordinate(physeq_all, method = "MDS", distance = beta.dist)
    #p <- plot_ordination(physeq = physeq_all, ordination = ord, type = "samples", axes = c(1, 2), color = "SampleID", shape = NULL, label = NULL, title = "Samples ordination graphic, bray-curtis distance")
    #p <- p + theme_bw()
    #print(p)
    #cat("\n")
  }
  cat("\n")
}

MEAT

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

CHEESE

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

WINE

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

BREAD

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

Abundances

Compositions

Alpha-diversity

Comparison of markers

MEAT

eco <- "MEAT"

Genus
Species

rank <- "Genus"
dir <- paste("REAL",eco, sep="_")
its1 <- readRDS(file.path(dir,paste0("ITS1_final",".rds")))
its2 <- readRDS(file.path(dir,paste0("ITS2_final",".rds")))
d1d2 <- readRDS(file.path(dir,paste0("D1D2_final",".rds")))
sample_data(d1d2)$Marker <- "D1/D2"
rpb2 <- readRDS(file.path(dir,paste0("RPB2_final",".rds")))
  
# Remove low-abundant ASVs
its1 <- filter_low_abundant(its1,0.001)
its2 <- filter_low_abundant(its2,0.001)
d1d2 <- filter_low_abundant(d1d2,0.001)
rpb2 <- filter_low_abundant(rpb2,0.001)

all <- merge_phyloseq(its1, its2, d1d2, rpb2)
all_rel  = transform_sample_counts(all, function(x) x / sum(x) )
#all_rel <- prune_samples(sample_sums(all_rel) > 0, all_rel)

all_glom <- tax_glom(all_rel, taxrank=rank)

#cat("\n")
#print(tax_table(all_glom) %>% as("matrix") %>% datatable())
#cat("\n")

tax_table(all_glom)[, ] <- tax_table(all_glom)[ , ] %>%  as("matrix") %>% stringr::str_replace_all('_', ' ') %>% stringr::str_wrap(50, exdent = 3) 
## Qui est bien compatible avec le heatmap

taxa.order <- tax_table(all_glom)[ , rank] %>% sort() %>% rev() %>% rownames()


p <- plot_heatmap(all_glom, distance="bray", sample.label="SampleID", taxa.label=rank, taxa.order = taxa.order, high="#000033", low="#66CCFF", na.value = "white")
p <- p + facet_grid(~Marker, scales = "free_x", space = "free_x") 
p <- p+ theme(axis.text.y=element_text(size = 11))

#p <- p + scale_y_discrete(label = function(x) stringr::str_trunc(x, 12))
print(p)

cat("\n")

rank <- "Species"
dir <- paste("REAL",eco, sep="_")
its1 <- readRDS(file.path(dir,paste0("ITS1_final",".rds")))
its2 <- readRDS(file.path(dir,paste0("ITS2_final",".rds")))
d1d2 <- readRDS(file.path(dir,paste0("D1D2_final",".rds")))
sample_data(d1d2)$Marker <- "D1/D2"
rpb2 <- readRDS(file.path(dir,paste0("RPB2_final",".rds")))
  
# Remove low-abundant ASVs
its1 <- filter_low_abundant(its1,0.001)
its2 <- filter_low_abundant(its2,0.001)
d1d2 <- filter_low_abundant(d1d2,0.001)
rpb2 <- filter_low_abundant(rpb2,0.001)

all <- merge_phyloseq(its1, its2, d1d2, rpb2)
all_rel  = transform_sample_counts(all, function(x) x / sum(x) )
#all_rel <- prune_samples(sample_sums(all_rel) > 0, all_rel)

all_glom <- tax_glom(all_rel, taxrank=rank)

tax_table(all_glom)[, ] <- tax_table(all_glom)[ , ] %>%  as("matrix") %>% stringr::str_replace_all('_', ' ') %>% stringr::str_wrap(50, exdent = 3) 
## Qui est bien compatible avec le heatmap

taxa.order <- tax_table(all_glom)[ , rank] %>% sort() %>% rev() %>% rownames()
p <- plot_heatmap(all_glom, distance="bray", sample.label="SampleID", taxa.label=rank, taxa.order = taxa.order, high="#000033", low="#66CCFF", na.value = "white")
p <- p + facet_grid(~Marker, scales = "free_x", space = "free_x") 
p <- p+ theme(axis.text.y=element_text(size = 10))

#p <- p + scale_y_discrete(label = function(x) stringr::str_trunc(x, 12))
print(p)

cat("\n")

CHEESE

eco <- "CHEESE"

Genus
Species

rank <- "Genus"
dir <- paste("REAL",eco, sep="_")
its1 <- readRDS(file.path(dir,paste0("ITS1_final",".rds")))
its2 <- readRDS(file.path(dir,paste0("ITS2_final",".rds")))
d1d2 <- readRDS(file.path(dir,paste0("D1D2_final",".rds")))
sample_data(d1d2)$Marker <- "D1/D2"
rpb2 <- readRDS(file.path(dir,paste0("RPB2_final",".rds")))
  
# Remove low-abundant ASVs
its1 <- filter_low_abundant(its1,0.001)
its2 <- filter_low_abundant(its2,0.001)
d1d2 <- filter_low_abundant(d1d2,0.001)
rpb2 <- filter_low_abundant(rpb2,0.001)

all <- merge_phyloseq(its1, its2, d1d2, rpb2)
all_rel  = transform_sample_counts(all, function(x) x / sum(x) )
#all_rel <- prune_samples(sample_sums(all_rel) > 0, all_rel)

all_glom <- tax_glom(all_rel, taxrank=rank)

tax_table(all_glom)[, ] <- tax_table(all_glom)[ , ] %>%  as("matrix") %>% stringr::str_replace_all('_', ' ') %>% stringr::str_wrap(50, exdent = 3) 
## Qui est bien compatible avec le heatmap

taxa.order <- tax_table(all_glom)[ , rank] %>% sort() %>% rev() %>% rownames()


p <- plot_heatmap(all_glom, distance="bray", sample.label="SampleID", taxa.label=rank, taxa.order = taxa.order, high="#000033", low="#66CCFF", na.value = "white")
p <- p + facet_grid(~Marker, scales = "free_x", space = "free_x") 
p <- p+ theme(axis.text.y=element_text(size = 11))

#p <- p + scale_y_discrete(label = function(x) stringr::str_trunc(x, 12))
print(p)

cat("\n")

rank <- "Species"
dir <- paste("REAL",eco, sep="_")
its1 <- readRDS(file.path(dir,paste0("ITS1_final",".rds")))
its2 <- readRDS(file.path(dir,paste0("ITS2_final",".rds")))
d1d2 <- readRDS(file.path(dir,paste0("D1D2_final",".rds")))
sample_data(d1d2)$Marker <- "D1/D2"
rpb2 <- readRDS(file.path(dir,paste0("RPB2_final",".rds")))
  
# Remove low-abundant ASVs
its1 <- filter_low_abundant(its1,0.001)
its2 <- filter_low_abundant(its2,0.001)
d1d2 <- filter_low_abundant(d1d2,0.001)
rpb2 <- filter_low_abundant(rpb2,0.001)

all <- merge_phyloseq(its1, its2, d1d2, rpb2)
all_rel  = transform_sample_counts(all, function(x) x / sum(x) )
#all_rel <- prune_samples(sample_sums(all_rel) > 0, all_rel)

all_glom <- tax_glom(all_rel, taxrank=rank)

tax_table(all_glom)[, ] <- tax_table(all_glom)[ , ] %>%  as("matrix") %>% stringr::str_replace_all('_', ' ') %>% stringr::str_wrap(50, exdent = 3) 
## Qui est bien compatible avec le heatmap

taxa.order <- tax_table(all_glom)[ , rank] %>% sort() %>% rev() %>% rownames()
p <- plot_heatmap(all_glom, distance="bray", sample.label="SampleID", taxa.label=rank, taxa.order = taxa.order, high="#000033", low="#66CCFF", na.value = "white")
p <- p + facet_grid(~Marker, scales = "free_x", space = "free_x") 
p <- p+ theme(axis.text.y=element_text(size = 10))

#p <- p + scale_y_discrete(label = function(x) stringr::str_trunc(x, 12))
print(p)

cat("\n")

WINE

eco <- "WINE"

Genus
Species

rank <- "Genus"
dir <- paste("REAL",eco, sep="_")
its1 <- readRDS(file.path(dir,paste0("ITS1_final",".rds")))
its2 <- readRDS(file.path(dir,paste0("ITS2_final",".rds")))
d1d2 <- readRDS(file.path(dir,paste0("D1D2_final",".rds")))
sample_data(d1d2)$Marker <- "D1/D2"
rpb2 <- readRDS(file.path(dir,paste0("RPB2_final",".rds")))

# Remove low-abundant ASVs
its1 <- filter_low_abundant(its1,0.001)
its2 <- filter_low_abundant(its2,0.001)
d1d2 <- filter_low_abundant(d1d2,0.001)
rpb2 <- filter_low_abundant(rpb2,0.001)

all <- merge_phyloseq(its1, its2, d1d2, rpb2)
all_rel  = transform_sample_counts(all, function(x) x / sum(x) )
#all_rel <- prune_samples(sample_sums(all_rel) > 0, all_rel)

all_glom <- tax_glom(all_rel, taxrank=rank)
all_glom <- manual_changes_taxonomy(all_glom)


tax_table(all_glom)[, ] <- tax_table(all_glom)[ , ] %>%  as("matrix") %>% stringr::str_replace_all('_', ' ') %>% stringr::str_wrap(50, exdent = 3) 
## Qui est bien compatible avec le heatmap

taxa.order <- tax_table(all_glom)[ , rank] %>% sort() %>% rev() %>% rownames()


p <- plot_heatmap(all_glom, distance="bray", sample.label="SampleID", taxa.label=rank, taxa.order = taxa.order, high="#000033", low="#66CCFF", na.value = "white")
p <- p + facet_grid(~Marker, scales = "free_x", space = "free_x") 
p <- p+ theme(axis.text.y=element_text(size = 11))

#p <- p + scale_y_discrete(label = function(x) stringr::str_trunc(x, 12))
print(p)

cat("\n")

rank <- "Species"
dir <- paste("REAL",eco, sep="_")
its1 <- readRDS(file.path(dir,paste0("ITS1_final",".rds")))
its2 <- readRDS(file.path(dir,paste0("ITS2_final",".rds")))
d1d2 <- readRDS(file.path(dir,paste0("D1D2_final",".rds")))
sample_data(d1d2)$Marker <- "D1/D2"
rpb2 <- readRDS(file.path(dir,paste0("RPB2_final",".rds")))
  
# Remove low-abundant ASVs
its1 <- filter_low_abundant(its1,0.001)
its2 <- filter_low_abundant(its2,0.001)
d1d2 <- filter_low_abundant(d1d2,0.001)
rpb2 <- filter_low_abundant(rpb2,0.001)

all <- merge_phyloseq(its1, its2, d1d2, rpb2)
all_rel  = transform_sample_counts(all, function(x) x / sum(x) )
#all_rel <- prune_samples(sample_sums(all_rel) > 0, all_rel)

all_glom <- tax_glom(all_rel, taxrank=rank)

tax_table(all_glom)[, ] <- tax_table(all_glom)[ , ] %>%  as("matrix") %>% stringr::str_replace_all('_', ' ') %>% stringr::str_wrap(50, exdent = 3) 
## Qui est bien compatible avec le heatmap

taxa.order <- tax_table(all_glom)[ , rank] %>% sort() %>% rev() %>% rownames()
p <- plot_heatmap(all_glom, distance="bray", sample.label="SampleID", taxa.label=rank, taxa.order = taxa.order, high="#000033", low="#66CCFF", na.value = "white")
p <- p + facet_grid(~Marker, scales = "free_x", space = "free_x") 
p <- p+ theme(axis.text.y=element_text(size = 10))

#p <- p + scale_y_discrete(label = function(x) stringr::str_trunc(x, 12))
print(p)

cat("\n")

BREAD

eco <- "BREAD"

Species
Genus

rank <- "Species"
dir <- paste("REAL",eco, sep="_")
its1 <- readRDS(file.path(dir,paste0("ITS1_final",".rds")))
its2 <- readRDS(file.path(dir,paste0("ITS2_final",".rds")))
d1d2 <- readRDS(file.path(dir,paste0("D1D2_final",".rds")))
sample_data(d1d2)$Marker <- "D1/D2"
#rpb2 <- readRDS(file.path(dir,paste0("RPB2_final",".rds")))
  
# Remove low-abundant ASVs
its1 <- filter_low_abundant(its1,0.001)
its2 <- filter_low_abundant(its2,0.001)
d1d2 <- filter_low_abundant(d1d2,0.001)
#rpb2 <- filter_low_abundant(rpb2,0.001)

all <- merge_phyloseq(its1, its2, d1d2)
all_rel  = transform_sample_counts(all, function(x) x / sum(x) )
#all_rel <- prune_samples(sample_sums(all_rel) > 0, all_rel)

all_glom <- tax_glom(all_rel, taxrank=rank)

tax_table(all_glom)[, ] <- tax_table(all_glom)[ , ] %>%  as("matrix") %>% stringr::str_replace_all('_', ' ') %>% stringr::str_wrap(50, exdent = 3) 
## Qui est bien compatible avec le heatmap

taxa.order <- tax_table(all_glom)[ , rank] %>% sort() %>% rev() %>% rownames()
p <- plot_heatmap(all_glom, distance="bray", sample.label="SampleID", taxa.label=rank, taxa.order = taxa.order, high="#000033", low="#66CCFF", na.value = "white")
p <- p + facet_grid(~Marker, scales = "free_x", space = "free_x") 
p <- p+ theme(axis.text.y=element_text(size = 10))

#p <- p + scale_y_discrete(label = function(x) stringr::str_trunc(x, 12))
print(p)

cat("\n")

rank <- "Genus"
dir <- paste("REAL",eco, sep="_")
its1 <- readRDS(file.path(dir,paste0("ITS1_final",".rds")))
its2 <- readRDS(file.path(dir,paste0("ITS2_final",".rds")))
d1d2 <- readRDS(file.path(dir,paste0("D1D2_final",".rds")))
sample_data(d1d2)$Marker <- "D1/D2"
#rpb2 <- readRDS(file.path(dir,paste0("RPB2_final",".rds")))
  
# Remove low-abundant ASVs
its1 <- filter_low_abundant(its1,0.001)
its2 <- filter_low_abundant(its2,0.001)
d1d2 <- filter_low_abundant(d1d2,0.001)
#rpb2 <- filter_low_abundant(rpb2,0.001)

all <- merge_phyloseq(its1, its2, d1d2)
all_rel  = transform_sample_counts(all, function(x) x / sum(x) )
#all_rel <- prune_samples(sample_sums(all_rel) > 0, all_rel)

all_glom <- tax_glom(all_rel, taxrank=rank)

#cat("\n")
#print(tax_table(all_glom) %>% as("matrix") %>% datatable())
#cat("\n")

tax_table(all_glom)[, ] <- tax_table(all_glom)[ , ] %>%  as("matrix") %>% stringr::str_replace_all('_', ' ') %>% stringr::str_wrap(50, exdent = 3) 
## Qui est bien compatible avec le heatmap

taxa.order <- tax_table(all_glom)[ , rank] %>% sort() %>% rev() %>% rownames()


p <- plot_heatmap(all_glom, distance="bray", sample.label="SampleID", taxa.label=rank, taxa.order = taxa.order, high="#000033", low="#66CCFF", na.value = "white")
p <- p + facet_grid(~Marker, scales = "free_x", space = "free_x") 
p <- p+ theme(axis.text.y=element_text(size = 11))

#p <- p + scale_y_discrete(label = function(x) stringr::str_trunc(x, 12))
print(p)

cat("\n")

List of ASVs

all <- readRDS("data/all_samples.rds")
meat_its1 <- readRDS("REAL_MEAT/ITS1_final.rds")
meat_its1 <- transform_sample_counts(meat_its1, function(x) {x/sum(x)})
sample_names(meat_its1) <- glue::glue(paste("{sample_data(meat_its1)$SampleID}","{sample_data(meat_its1)$Eco}","{sample_data(meat_its1)$Marker}",sep="_"))
taxa_names(meat_its1) <- glue::glue(paste("{taxa_names(meat_its1)}","MEAT","ITS1",sep="_"))
meat_its2 <- readRDS("REAL_MEAT/ITS2_final.rds")
meat_its2 <- transform_sample_counts(meat_its2, function(x) {x/sum(x)})
sample_names(meat_its2) <- glue::glue(paste("{sample_data(meat_its2)$SampleID}","{sample_data(meat_its2)$Eco}","{sample_data(meat_its2)$Marker}",sep="_"))
taxa_names(meat_its2) <- glue::glue(paste("{taxa_names(meat_its2)}","MEAT","ITS2",sep="_"))
meat_rpb2 <- readRDS("REAL_MEAT/RPB2_final.rds")
meat_rpb2 <- transform_sample_counts(meat_rpb2, function(x) {x/sum(x)})
sample_names(meat_rpb2) <- glue::glue(paste("{sample_data(meat_rpb2)$SampleID}","{sample_data(meat_rpb2)$Eco}","{sample_data(meat_rpb2)$Marker}",sep="_"))
taxa_names(meat_rpb2) <- glue::glue(paste("{taxa_names(meat_rpb2)}","MEAT","RPB2",sep="_"))
meat_d1d2 <- readRDS("REAL_MEAT/D1D2_final.rds")
meat_d1d2 <- transform_sample_counts(meat_d1d2, function(x) {x/sum(x)})
sample_names(meat_d1d2) <- glue::glue(paste("{sample_data(meat_d1d2)$SampleID}","{sample_data(meat_d1d2)$Eco}","{sample_data(meat_d1d2)$Marker}",sep="_"))
taxa_names(meat_d1d2) <- glue::glue(paste("{taxa_names(meat_d1d2)}","MEAT","D1D2",sep="_"))
cheese_its1 <- readRDS("REAL_CHEESE/ITS1_final.rds")
cheese_its1 <- transform_sample_counts(cheese_its1, function(x) {x/sum(x)})
sample_names(cheese_its1) <- glue::glue(paste("{sample_data(cheese_its1)$SampleID}","{sample_data(cheese_its1)$Eco}","{sample_data(cheese_its1)$Marker}",sep="_"))
taxa_names(cheese_its1) <- glue::glue(paste("{taxa_names(cheese_its1)}","CHEESE","ITS1",sep="_"))
cheese_its2 <- readRDS("REAL_CHEESE/ITS2_final.rds")
cheese_its2 <- transform_sample_counts(cheese_its2, function(x) {x/sum(x)})
sample_names(cheese_its2) <- glue::glue(paste("{sample_data(cheese_its2)$SampleID}","{sample_data(cheese_its2)$Eco}","{sample_data(cheese_its2)$Marker}",sep="_"))
taxa_names(cheese_its2) <- glue::glue(paste("{taxa_names(cheese_its2)}","CHEESE","ITS2",sep="_"))
cheese_rpb2 <- readRDS("REAL_CHEESE/RPB2_final.rds")
cheese_rpb2 <- transform_sample_counts(cheese_rpb2, function(x) {x/sum(x)})
sample_names(cheese_rpb2) <- glue::glue(paste("{sample_data(cheese_rpb2)$SampleID}","{sample_data(cheese_rpb2)$Eco}","{sample_data(cheese_rpb2)$Marker}",sep="_"))
taxa_names(cheese_rpb2) <- glue::glue(paste("{taxa_names(cheese_rpb2)}","CHEESE","RPB2",sep="_"))
cheese_d1d2 <- readRDS("REAL_CHEESE/D1D2_final.rds")
cheese_d1d2 <- transform_sample_counts(cheese_d1d2, function(x) {x/sum(x)})
sample_names(cheese_d1d2) <- glue::glue(paste("{sample_data(cheese_d1d2)$SampleID}","{sample_data(cheese_d1d2)$Eco}","{sample_data(cheese_d1d2)$Marker}",sep="_"))
taxa_names(cheese_d1d2) <- glue::glue(paste("{taxa_names(cheese_d1d2)}","CHEESE","D1D2",sep="_"))
wine_its1 <- readRDS("REAL_WINE/ITS1_final.rds")
wine_its1 <- transform_sample_counts(wine_its1, function(x) {x/sum(x)})
sample_names(wine_its1) <- glue::glue(paste("{sample_data(wine_its1)$SampleID}","{sample_data(wine_its1)$Eco}","{sample_data(wine_its1)$Marker}",sep="_"))
taxa_names(wine_its1) <- glue::glue(paste("{taxa_names(wine_its1)}","WINE","ITS1",sep="_"))
wine_its2 <- readRDS("REAL_WINE/ITS2_final.rds")
wine_its2 <- transform_sample_counts(wine_its2, function(x) {x/sum(x)})
sample_names(wine_its2) <- glue::glue(paste("{sample_data(wine_its2)$SampleID}","{sample_data(wine_its2)$Eco}","{sample_data(wine_its2)$Marker}",sep="_"))
taxa_names(wine_its2) <- glue::glue(paste("{taxa_names(wine_its2)}","WINE","ITS2",sep="_"))
wine_rpb2 <- readRDS("REAL_WINE/RPB2_final.rds")
wine_rpb2 <- transform_sample_counts(wine_rpb2, function(x) {x/sum(x)})
sample_names(wine_rpb2) <- glue::glue(paste("{sample_data(wine_rpb2)$SampleID}","{sample_data(wine_rpb2)$Eco}","{sample_data(wine_rpb2)$Marker}",sep="_"))
taxa_names(wine_rpb2) <- glue::glue(paste("{taxa_names(wine_rpb2)}","WINE","RPB2",sep="_"))
wine_d1d2 <- readRDS("REAL_WINE/D1D2_final.rds")
wine_d1d2 <- transform_sample_counts(wine_d1d2, function(x) {x/sum(x)})
sample_names(wine_d1d2) <- glue::glue(paste("{sample_data(wine_d1d2)$SampleID}","{sample_data(wine_d1d2)$Eco}","{sample_data(wine_d1d2)$Marker}",sep="_"))
taxa_names(wine_d1d2) <- glue::glue(paste("{taxa_names(wine_d1d2)}","WINE","D1D2",sep="_"))
bread_its1 <- readRDS("REAL_BREAD/ITS1_final.rds")
bread_its1 <- transform_sample_counts(bread_its1, function(x) {x/sum(x)})
sample_names(bread_its1) <- glue::glue(paste("{sample_data(bread_its1)$SampleID}","{sample_data(bread_its1)$Eco}","{sample_data(bread_its1)$Marker}",sep="_"))
taxa_names(bread_its1) <- glue::glue(paste("{taxa_names(bread_its1)}","BREAD","ITS1",sep="_"))
bread_its2 <- readRDS("REAL_BREAD/ITS2_final.rds")
bread_its2 <- transform_sample_counts(bread_its2, function(x) {x/sum(x)})
sample_names(bread_its2) <- glue::glue(paste("{sample_data(bread_its2)$SampleID}","{sample_data(bread_its2)$Eco}","{sample_data(bread_its2)$Marker}",sep="_"))
taxa_names(bread_its2) <- glue::glue(paste("{taxa_names(bread_its2)}","BREAD","ITS2",sep="_"))
bread_d1d2 <- readRDS("REAL_BREAD/D1D2_final.rds")
bread_d1d2 <- transform_sample_counts(bread_d1d2, function(x) {x/sum(x)})
sample_names(bread_d1d2) <- glue::glue(paste("{sample_data(bread_d1d2)$SampleID}","{sample_data(bread_d1d2)$Eco}","{sample_data(bread_d1d2)$Marker}",sep="_"))
taxa_names(bread_d1d2) <- glue::glue(paste("{taxa_names(bread_d1d2)}","BREAD","D1D2",sep="_"))

all <- merge_phyloseq(meat_its1, cheese_its1, wine_its1, bread_its1, meat_its2, cheese_its2, wine_its2, bread_its2, meat_d1d2, cheese_d1d2, wine_d1d2, bread_d1d2, meat_rpb2, cheese_rpb2, wine_rpb2)

saveRDS(all, "data/all_samples_relative.rds")
tib <- psmelt(all) %>% as_tibble()
tib <- tib %>% mutate(Taxo = paste(Kingdom,Phylum,Class,Order,Family,Genus,Species,sep=";")) %>% mutate(ASV = stringr::str_split(OTU, "_") %>% map_chr(., 1)) %>% distinct(ASV, Taxo, .keep_all = T) %>% select(ASV, Taxo, Marker, Sample, Eco) %>% mutate(Marker=replace(Marker, Marker=="D1D2", "D1/D2"))
datatable(tib, extensions='Buttons', options= list(dom='Bfrtip',buttons = c('excel','csv')))

Filter:

	ASV	Taxo	Marker	Sample	Eco
1	CGAGACACCTGAAGGTCAAGCTTGTGGTCTGGTCAAGAACCTGGCATTGATGTGCTACATCACTGTTGGTACACCTGCTGAACCCATCGTGGACTTCATGATTCAGCGGAACATGGAAGTTCTCGAGGAGTTTGAACCCCAAGTGACGCCTAATGCAACAAAGGTGTTTGTCAATGGTGTCTGGGTGGGTATTCATCGGGACCCTTCGCATCTGGTTACTACGATGCAGANNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNGAGCAGTTGAATATGTCGACGCTGAAGAAGAGGAAACTATCATGATTGTCATGACCCCCGAGGATCTTGAGATCTCTCGACAGCTCCAAGCCGGTTACGCTCTGCCAGAAGATGAAACCAGCGACCCCAACAAGCGTGTTCGATCGATTCTCAGCCAGCGTGCCCACACCTGGACGCACTGCGAAATTCACCCTAGTATGATCTTGGGTGTTTGCGCTAGTATCATTC	Fungi;Ascomycota;Eurotiomycetes;Eurotiales;Aspergillaceae;Penicillium;Penicillium nalgiovense	RPB2	F9SR1_MEAT_RPB2	MEAT
2	GCATATCAATAAGCGGAGGAAAAGAAACCAACAGGGATTGCCTTAGTAACGGCGAGTGAAGCGGCAAAAGCTCAAATTTGAAATCTGGCACCTTCGGTGTCCGAGTTGTAATTTGAAGAAGGTAACTTTGGAGTTGGCTCTTGTCTATGTTCCTTGGAACAGGACGTCACAGAGGGTGAGAATCCCGTGCGATGAGATGCCCAATTCTATGTAAAGTGCTTTCGAAGAGTCGAGTTGTTTGGGAATGCAGNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNTGAAATTGTTGAAAGGGAAGGGCTTGAGATCAGACTTGGTATTTTGCGATCCTTTCCTTCTTGGTTGGGTTCCTCGCAGCTTACTGGGCCAGCATCGGTTTGGATGGTAGGATAATGACTAAGGAATGTGGCTCTACTTCGGTGGAGTGTTATAGCCTTGGTTGATACTGCCTGTCTAGACCGAGGACTGCGTCTTTTGACTAGGATGTTGGCATAATGATCTTAAGCCACCCGTCTTGAAACACGGACC	Fungi;Ascomycota;Saccharomycetes;Saccharomycetales;Debaryomycetaceae;Debaryomyces;Debaryomyces hansenii	ITS1	F4SR2_MEAT_ITS1	MEAT
3	GAAATGCGATACGTAATGTGAATTGCAAATTCAGTGAATCATCGAGTCTTTGAACGCACATTGCGCCCCCTGGTATTCCGGGGGGCATGCCTGTCCGAGCGTCATTGCTGCCCTCAAGCCCGGCTTGTGTGTTGGGCCCCGTCCTCCGATCCCGGGGGACGGGCCCGAAAGGCAGCGGCGGCACCGCGTCCGGTCCTCGAGCGTATGGGGCTTTGTCACCCGCTCTGTAGGCCCGGCCGGCGCTTGCCGATCAACCCAAATTTTTATCCAGGTTGACCTCGGATCAGGTAGGGATACCCGCTGAACTTAA	Fungi;Ascomycota;Eurotiomycetes;Eurotiales;Aspergillaceae;Penicillium;Penicillium Group nalgiovense	ITS2	F8SR1_MEAT_ITS2	MEAT
4	AAGTCGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTACCGAGTGAGGGCCCTCTGGGTCCAACCTCCCACCCGTGTTTATTTTACCTTGTTGCTTCGGCGGGCCCGCCTTAACTGGCCGCCGGGGGGCTCACGCCCCCGGGCCCGCGCCCGCCGAAGACACCCTCGAACTCTGTCTGAAGATTGTAGTCTGAGTGAAAATATAAATTATTTAAAACTTTCAACAACGGATCTCTTGGTTCCG	Fungi;Ascomycota;Eurotiomycetes;Eurotiales;Aspergillaceae;Penicillium;Penicillium Group nalgiovense	ITS1	F9SR1_MEAT_ITS1	MEAT
5	AGAAACTCCAGAAGGTCAAGCGTGTGGTTTGGTCAAGAATTTGTCTTTGATGTCGTGTATATCTGTTGGTACTTCATCAGAACCAATTTTGTATTTCTTAGAAGAATGGGGTATGGAACCTTTGGAAGATTATGTACCTTCGAATTCTCCCGATTCAACGAGAGTCTTTGTTAATGGTGTCTGGGTTGGTACTCATAGAGAACCTGCTCATTTGGTTGACACTATGCGTANNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNTAGAATATGTTGATGCAGAAGAAGAAGAAACCATAATGATTGCTATGACTCCAGAAGACTTGGAAACAAGCAGAAGTACTTTATCAGAGACAGAACAGAAGGATATGCAACTTGAAGAACAAGAAGTTGACCCTGCAAAGAGAATCAAGCCTACACTTGGATTACATACGTTTACCCATTGTGAAATCCATCCTTCAATGATTCTAGGAGTTGCAGCCTCTATTATTC	Fungi;Ascomycota;Saccharomycetes;Saccharomycetales;Debaryomycetaceae;Debaryomyces;Debaryomyces hansenii	RPB2	echantillon2-2_CHEESE_RPB2	CHEESE
6	AAACCAACAGGGATTGCCCCAGTAACGGCGAGTGAAGCGGCAAGAGCTCAAATTTGAAAGCTGGCTCCTTCGGGGTCCGCATTGTAATTTGTAGAGGATGCTTCGGGAGCGGTCCCCATCTAAGTGCCCTGGAACGGGACGTCATAGAGGGTGAGAATCCCGTATGGGATGGGGTGTCCGCGCCCGTGTGAAGCTCCTTCGACGAGTCGAGTTGTTTGGGAATGCANNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNGTGAAATTGTTGAAAGGGAAGCGCTTGCGACCAGACTCGCTCGCGGGGTTCAGCCGGCATTCGTGCCGGTGTACTTCCCCGCGGGCGGGCCAGCGTCGGTTTGGGCGGTCGGTCAAAGGCCCTCGGAAGGTAACGCCCCTAGGGGCGTCTTATAGCCGAGGGTGCAATGCGACCTGCCTAGACCGAGGAACGCGCTTCGGCTCGGACGCTGGCATAATGGTCGTAAACGAC	Fungi;Ascomycota;Eurotiomycetes;Eurotiales;Aspergillaceae;Penicillium;Penicillium sp.	D1/D2	F9SR1_MEAT_D1D2	MEAT
7	AAACCAACAGGGATTGCCTTAGTAACGGCGAGTGAAGCGGCAAAAGCTCAAATTTGAAATCTGGCACCTTCGGTGTCCGAGTTGTAATTTGAAGAAGGTAACTTTGGAGTTGGCTCTTGTCTATGTTCCTTGGAACAGGACGTCACAGAGGGTGAGAATCCCGTGCGATGAGATGCCCAATTCTATGTAAAGTGCTTTCGAAGAGTCGAGTTGTTTGGGAATGCAGNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNTGAAATTGTTGAAAGGGAAGGGCTTGAGATCAGACTTGGTATTTTGCGATCCTTTCCTTCTTGGTTGGGTTCCTCGCAGCTTACTGGGCCAGCATCGGTTTGGATGGTAGGATAATGACTAAGGAATGTGGCTCTACTTCGGTGGAGTGTTATAGCCTTGGTTGATACTGCCTGTCTAGACCGAGGACTGCGTCTTTTGACTAGGATGTTGGCATAATGATCTTAAGCCAC	Fungi;Ascomycota;Saccharomycetes;Saccharomycetales;Debaryomycetaceae;Debaryomyces;Debaryomyces hansenii	D1/D2	echantillon1-2_CHEESE_D1D2	CHEESE
8	GAAACGCGATATTTCTTGTGAATTGCAGAAGTGAATCATCAGTTTTTGAACGCACATTGCACTTTGGGGTATCCCCCAAAGTATACTTGTTTGAGCGTTGTTTCTCTCTTGGAATTGCTTTGCTCTTCTAAAATTTCGAATCAAATTCGTTTGAAAAACAACACTATTCAACCTCAGATCAAGTAGGATTACCCGCTGAACTTAA	Fungi;Ascomycota;Saccharomycetes;Saccharomycetales;Dipodascaceae;Geotrichum;Geotrichum candidum	ITS2	echantillon3-1_CHEESE_ITS2	CHEESE
9	AAGTCGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTATGAATTAATAATATTTGTGAAATTTCAACAAACAACATCAATTTTATAGTCTATTATTTTTAATTAAAACTTTTAACAATGGATCTCTTGGTTCTC	Fungi;Ascomycota;Saccharomycetes;Saccharomycetales;Dipodascaceae;Geotrichum;Geotrichum candidum	ITS1	echantillon2-2_CHEESE_ITS1	CHEESE
10	AAACCAACCGGGATTGCCTTAGTAACGGCGAGTGAAGCGGCAAAAGCTCAAATTTGAAATCTGGTACCTTCGGTGCCCGAGTTGTAATTTGGAGAGGGCAACTTTGGGGCCGTTCCTTGTCTATGTTCCTTGGAACAGGACGTCATAGAGGGTGAGAATCCCGTGTGGCGAGGAGTGCGGTTCTTTGTAAAGTGCCTTCGAAGAGTCGAGTTGTTTGGGAATGCAGNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNGAAATTGTTGAAAGGGAAGGGCATTTGATCAGACATGGTGTTTTGTGCCCTCTGCTCCTTGTGGGTAGGGGAATCTCGCATTTCACTGGGCCAGCATCAGTTTTGGTGGCAGGATAAATCCATAGGAATGTAGCTTGCCTCGGTAAGTATTATAGCCTGTGGGAATACTGCCAGCTGGGACTGAGGACTGCGACGTAAGTCAAGGATGCTGGCATAATGGTTATATGCCGC	Fungi;Ascomycota;Saccharomycetes;Saccharomycetales;Saccharomycetaceae;Saccharomyces;Saccharomyces cerevisiae	D1/D2	echantillon3-2_BREAD_D1D2	BREAD

Showing 1 to 10 of 2,359 entries

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#write.table(tib %>% select(ASV, Taxo, Marker, Sample, Eco) ,"data/real_samples_ASVs.tsv",sep="\t",row.names=FALSE,col.names=TRUE)

METABARFOOD - Results on real communities

Olivier Rué

2022-11-24

Introduction

Compositions

MEAT

CHEESE

WINE

BREAD

Comparison of markers

MEAT

CHEESE

WINE

BREAD

List of ASVs