DESeq2 has an official extension within the phyloseq package and an accompanying vignette. The vignette has been copied/included here for continuity, and as you can see, phyloseq_to_deseq2 does not need to be defined before using it because it is already available when you load phyloseq.
If you find this extension or tutorial useful in your work, please cite the following:
McMurdie and Holmes (2014) Waste Not, Want Not: Why Rarefying Microbiome Data is Inadmissible. PLoS Computational Biology in press
McMurdie and Holmes (2013) phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE. 8(4):e61217
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15(12): 550
In this example I use the publicly available data from a study on colorectal cancer:
Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Kostic, A. D., Gevers, D., Pedamallu, C. S., Michaud, M., Duke, F., Earl, A. M., et al. (2012). Genome research, 22(2), 292-298.
As a side-note, this work was published ahead of print in Genome Research alongside a highly-related article from a separate group of researchers (long-live reproducible observations!): Fusobacterium nucleatum infection is prevalent in human colorectal carcinoma. In case you are interested. For the purposes of example, however, we will stick to the data from the former study, with data available at the microbio.me/qiime server.
Study ID: 1457
Project Name: Kostic_colorectal_cancer_fusobacterium
Study Abstract: The tumor microenvironment of colorectal carcinoma is a complex community of genomically altered cancer cells, nonneoplastic cells, and a diverse collection of microorganisms. Each of these components may contribute to carcino genesis; however, the role of the microbiota is the least well understood. We have characterized the composition of the microbiota in colorectal carcinoma using whole genome sequences from nine tumor/normal pairs. Fusobacterium sequences were enriched in carcinomas, confirmed by quantitative PCR and 16S rDNA sequence analysis of 95 carcinoma/normal DNA pairs, while the Bacteroidetes and Firmicutes phyla were depleted in tumors. Fusobacteria were also visualized within colorectal tumors using FISH. These findings reveal alterations in the colorectal cancer microbiota; however, the precise role of Fusobacteria in colorectal carcinoma pathogenesis requires further investigation.
Start by loading phyloseq.
library("phyloseq"); packageVersion("phyloseq")
Defined file path, and import the published OTU count data into R.
filepath = system.file("extdata", "study_1457_split_library_seqs_and_mapping.zip", package="phyloseq")
kostic = microbio_me_qiime(filepath)
Here I had to use a relative file path so that this example works on all systems that have phyloseq installed. In practice, your file path will look like this (if you've downloaded the data ahead of time):
filepath = "~/Downloads/study_1457_split_library_seqs_and_mapping.zip"
kostic = microbio_me_qiime(filepath)
Or like this (if you're accessing data directly from the microbio.me/qiime server directly):
kostic = microbio_me_qiime(1457)
In this example I'm using the major sample covariate, DIAGNOSIS, as the study design factor. The focus of this study was to compare the microbiomes of pairs of healthy and cancerous tissues, so this makes sense. Your study could have a more complex or nested design, and you should think carefully about the study design formula, because this is critical to the test results and their meaning. You might even need to define a new factor if none of the variables in your current table appropriately represent your study's design. See the DESeq2 home page for more details.
Here is the summary of the data variable kostic that we are about to use, as well as the first few entries of the DIAGNOSIS factor.
kostic
head(sample_data(kostic)$DIAGNOSIS, 25)
Unfortunately, the diagnosis variable has a third placeholder class indicating that no diagnosis was given ("None"). For the purposes of testing, these samples will be removed.
kostic = subset_samples(kostic, DIAGNOSIS != "None")
kostic
First load DESeq2.
library("DESeq2")
packageVersion("DESeq2")
The following two lines actually do all the complicated DESeq2 work. The function phyloseq_to_deseq2 converts your phyloseq-format microbiome data into a DESeqDataSet with dispersions estimated, using the experimental design formula, also shown (the ~DIAGNOSIS term). The DESeq function does the rest of the testing, in this case with default testing framework, but you can actually use alternatives.