Phyloseq_Hydrochar.md

Phyloseq

Quick install (BiocManager)

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("phyloseq")

Data

The aim of this project was to characterise the microbial diversity of anaerobic reactors. The data is part of the project Effect of hydrochar from acid hydrolysis on anaerobic digestion of chicken manure, where solid residues from the production of biofuel precursor levulinic acid were generated via microwave-assisted acid hydrolysis of Miscanthus x Giganteus and investigated for the supplementation of anaerobic reactors digesting Chicken Manure.

Load libraries

install.packages("dplyr")     # To manipulate dataframes
install.packages("readxl")    # To read Excel files into R
install.packages("ggplot2")   # for high quality graphics
source("https://bioconductor.org/biocLite.R")
biocLite("phyloseq")  

Read the data and transform it to Phyloseq objects

Three different tables are needed (tables were obtained from QIIME2) and organised in Excel: • OTU • Taxonomy • Samples

OTU Table

Taxonomy Table

Samples Table

Read data

setwd("/Users/user/Documents/Postdoc/Sequencing/Anaerobic_Digestion")
otu_mat<-read_excel("/Users/user/Documents/Postdoc/Sequencing/Anaerobic_Digestion/OTU_table_trd.xlsx", sheet = "OTU matrix")
tax_mat<-read_excel("/Users/user/Documents/Postdoc/Sequencing/Anaerobic_Digestion/OTU_taxonomy_trd.xlsx", sheet = "Taxonomy table")
samples_df<-read_excel("/Users/user/Documents/Postdoc/Sequencing/Anaerobic_Digestion/table_samples_trd.xlsx", sheet = "Samples")

Define the row names

otu_mat <- otu_mat %>%
  tibble::column_to_rownames("out") 
tax_mat <- tax_mat %>% 
  tibble::column_to_rownames("out")
samples_df <- samples_df %>% 
  tibble::column_to_rownames("sample")

Transform into matrixes otu to tables (sample table can be left as data frame)

otu_mat <- as.matrix(otu_mat)
tax_mat <- as.matrix(tax_mat)

Transform to Phyloseq objects

OTU = otu_table(otu_mat, taxa_are_rows = TRUE)
TAX = tax_table(tax_mat)
samples = sample_data(samples_df)
carbom <- phyloseq(OTU, TAX, samples)
carbom

Visualise data

sample_names(carbom)
rank_names(carbom)
sample_variables(carbom)

Normalize the number of reads in each sample using median sequencing depth

total = median(sample_sums(carbom))
standf = function(x, t=total) round(t * (x / sum(x)))
carbom = transform_sample_counts(carbom, standf)

Bar graph

The bar graph shown here was created at the phylum level, but depending on the data, graphs can be created at different levels

plot_bar(carbom, fill = "Phylum")

Relative abundance at phylum level

Heatmap

The heatmap also shows the (abundance) of items, however, for this specific case the map contained multiple rows and the information is not clear.

It is possible to edit the input table, but for this specific case the heatmap was not longer considered relevant.

plot_heatmap(carbom, method = "NMDS", distance = "bray")

Alpha diversity

Alpha diversity measurements can be calculated individually or all together with the function Plot_richness

plot_richness(carbom, color = "samples", measures=c("Chao1", "Shannon", "Observed", "InvSimpson"))
plot_richness(carbom, color = "samples")
estimate_richness(carbom, measures = "Observed")

Ordination

Multivariate analysis is based on Bray-Curtis distance and NMDS ordination.

carbom.ord <- ordinate(carbom, "NMDS", "bray")
plot_ordination(carbom, carbom.ord, type = "taxa", color = "Class", shape = "Domain", title = "OTUs")
plot_ordination(carbom, carbom.ord, type = "taxa", color = "Phylum", shape = "Domain", title = "OTUs")
plot_ordination(carbom, carbom.ord, type = "taxa", color = "Class", title = "OTUs", label = "Class") + facet_wrap(~Domain, 3)