Specific components of the ps object can be extracted and converted to a ame for additional analyses.Įxamining the number of reads for each sample We also have 4 variables (Subject, Gender, Day, and When) and that information can be easily accessed and computations or descriptive statistics performed.

Here we can see that we have 19 samples and they are assigned the sample names we gave them during the DADA2 tutorial. # 9 10 median(sample_data(ps)$Day) # 141 metadata <- ame(sample_data(ps)) # 'Early' 'Early' 'Early' table(sample_data(ps)$When) # # 'Late' 'Late' 'Late' 'Late' 'Early' 'Early' 'Early' 'Early' # F3D144 3 F 144 Late sample_data(ps)$When # 'Early' 'Early' 'Late' 'Late' 'Late' 'Late' 'Late' 'Late' # 'F3D5' 'F3D6' 'F3D7' 'F3D8' 'F3D9' sample_variables(ps) # 'Subject' 'Gender' 'Day' 'When' head(sample_data(ps)) # Subject Gender Day When We will run through several commands below and then discuss the output. Next we will see how each of the components can be accessed. This makes it easy to keep all of your data together and to share it with colleagues or include it as a supplemental file to a publication. Each of these data structures is contained in a single object. This highlights one of the key advantages of working with phyloseq objects in R.

A sample metadata file consisting of 4 variables.

An OTU table with 232 taxa and 19 samples.

Here we can see that we have a phyloseq object that consists of:.

To learn more, Paul McMurdie has an excellent set of tutorials that I encourage you to explore.Īccessing the sample information and sample metadata ps # phyloseq-class experiment-level object The session will quickly cover some of the basic accessor, analysis and graphical functions available to you when using the phyloseq package in R. We did not generate a phylogenetic tree from these sequences, but if we had, it could be included as well.

The phyloseq object contains: an ASV table, sample metadata, taxonomic classifications, and the reference sequences. If you recall, these were murine stool samples collected from a single mouse over time. We will be working with the phyloseq object that was created during the DADA2 tutorial. The goal of this interactive session is to introduce you to some of the basic functionality of the phyloseq package that can help you to explore and better understand your metagenomic data. I thought it might be useful to a broader audience so decided to post it. This tutorial picks up where Ben Callahan’s DADA2 tutorial leaves off and highlights some of the main accessor and processor functions of the package. It provides a quick introduction some of the functionality provided by phyloseq and follows some of Paul McMurdie’s excellent tutorials. This post is from a tutorial demonstrating the processing of amplicon short read data in R taught as part of the Introduction to Metagenomics Summer Workshop.

Dada2 rarify