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Viromics Workshop

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Rstudio set up from Conda environment, package installment and data download

Overview

Teaching: 0 min
Exercises: 30 min
Questions
Objectives

  • Create Conda environment from the terminal

  • Install few extra R packages

  • Loading data and packages into R.

  • Using the FeatureTable package.

Install environment

Open the terminal. Navigate into your working directory in the terminal(e.g. day5/). Download the environment file. Create the conda environment using the command lines below explicitely. Note that you should use --file instead of -f in the line of conda create .... Activate the environment. Export a library into the environment. Replace prakXXX with your own computer ID in the line of export .... Initiate Rstudio from the shell.

cd day5/
wget https://raw.githubusercontent.com/lingyi-owl/jena_workshop/gh-pages/data/day5_env.txt
conda create --name day5_env --file day5_env.txt
conda activate day5_env
export LD_LIBRARY_PATH=/mnt/local/prakXXX/anaconda3/envs/day5_env/lib
rstudio

Install extra packages

In RStudio interface openned from the previous step, install the packages below.

Download the FeatureTable package to your working directory. Install the package in RStudio using the scripts below. Skip updates when installing breakaway. It might take a while to install breakaway.

# install featuretable
install.packages("featuretable_0.0.10.tar.gz", repos = NULL)
install.packages("remotes")
library(remotes)
# install DivNet
remotes::install_github("adw96/breakaway")
remotes::install_github("adw96/DivNet")
# install biplotr
remotes::install_github("mooreryan/biplotr")
# install vmikk/metagMisc
remotes::install_github("vmikk/metagMisc")

Load packages

Load packages in the order below to avoid conflicts.

library(phyloseq)
library(breakaway)
library(DivNet)
library(ggplot2)
library(gridExtra)
library(magrittr)
library(picante)
library(reshape2)
library(biplotr)
library(zCompositions)
library(vegan)
library(ggdendro)
library(ALDEx2)
library(featuretable)
library(ComplexHeatmap)
library(metagMisc)

R will throw an error if any of the packages are not installed.

Download the data

The input data for the workshop today can be downloaded through github. Store these data in the directory of day5/data/. It is a microbial 16S dataset from a pond study through a seasonal change period with two size fractions (0.2µm. & 1µm). We used this microbial 16S dataset for this workshop because it has enough sequencing depth and sample numbers to conduct ecological analysis. The twins phages dataset is not big enough to show variance between samples. The principles to study viral ecology and microbial ecology should be the same. Thus, you can apply the analysis methods you learned from today’s workshop to your viromics data in the future.

# asv count table
wget https://raw.githubusercontent.com/lingyi-owl/jena_workshop/gh-pages/data/asv_count_table.txt
# taxonomy column table
wget https://raw.githubusercontent.com/lingyi-owl/jena_workshop/gh-pages/data/taxonomy_columns.txt
# sample metadata table
wget https://raw.githubusercontent.com/lingyi-owl/jena_workshop/gh-pages/data/sample_metadata.txt
# asv tree file
wget https://raw.githubusercontent.com/lingyi-owl/jena_workshop/gh-pages/data/asv_FastTree.newick
# environment data by month table
wget https://raw.githubusercontent.com/lingyi-owl/jena_workshop/gh-pages/data/env_data_by_month.txt

Load data

Load data to R. Construct and save a featuretable object and a phyloseq object using the provided data for further usage. Before you load the data, make sure that the name of the feature(ASV) column matches in the feature(ASV) count table and the taxonomy table. They will not match by default. My labels for both are “ASV” Load the ASV count table, taxonomy table, and sample metadata using the read.table() function:

# ASV table with raw counts
counts <- read.table("data/asv_count_table.txt",
# tells the function that rows are separated by a tab
sep = "\t",
# logical confirming that the table already has headers
header = TRUE,
# make the first column of the table into row names
row.names = 1)
# taxonomy info for the ASVs
taxonomy <- read.table("data/taxonomy_columns.txt",
sep = "\t",
header = TRUE,
row.names = 1,
na.strings = c("", "NA"))
# sample metadata
sample_data <- read.table("data/sample_metadata.txt",
sep = "\t",
header = TRUE,
row.names = 2)
# phylogenetic tree of ASVs
tree <- read_tree("data/asv_FastTree.newick")

# make and save featuretable object
pond_ft <- FeatureTable$new(t(counts), taxonomy, sample_data)
print(pond_ft)
save(pond_ft, file = "data/pond_featuretable.Rdata")

# make and save phyloseq object
pond_phyloseq <- phyloseq(
otu_table(counts, taxa_are_rows = T),
tax_table(as.matrix(taxonomy)),
sample_data(sample_data),
phy_tree(tree)
)
save(pond_phyloseq, file = "data/pond_phyloseq.Rdata")

Key Points

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