## Issues Tracker

Additional development of phyloseq is ongoing, the details of which are documented heavily at [the phyloseq issues tracker](https://github.com/joey711/phyloseq/issues).

## High level goals

In broader strokes, some near-term plans include:

1. The "compartmentalization" of the data infrastructure portion of the phyloseq package into a separate Bioconductor package, [a la this issue](https://github.com/joey711/phyloseq/issues/102). This may reduce the number of dependencies implied when reusing the data tools of phyloseq for other Bioconductor/R packages.

1. The development and calibration of additional tools in phyloseq for formal preprocessing, filtering, normalization, shrinkage and variance stabilization ([e.g. Allison 2006](http://www.nature.com/nrg/journal/v7/n1/full/nrg1749.html)) 

1. Additional structured wrapping tools to the [ade4](http://cran.r-project.org/web/packages/ade4/index.html) and [vegan](http://cran.r-project.org/web/packages/vegan/index.html) packages, with the most commonly-used tools given specific wrapping functions in phyloseq or phyloseqBase.

1. Structures for additional data components, e.g. mass spec and expression data.

1. Animated ordinations for time-series (and analogous) data. See beta-version support package: [animate.phyloseq](https://github.com/joey711/animate.phyloseq)

1. The compilation of a [Bioconductor data package](http://www.bioconductor.org/packages/release/data/experiment/) ("phyloseqData") that includes many key published datasets already imported as separate "phyloseq" instances and available through R's `data` interface.

1. **Big(ger)** Data
The firehose of new-gen sequencing data is making possible "big" datasets in this realm. For example, [the demo on importing the Human Microbiome Project data into R](http://joey711.github.com/phyloseq-demo/HMP_import_example.html) takes a considerable amount of time to run on a typical desktop/laptop and may push some less powerful machines to their limit. And that's just data import. We are considering some of the best approaches to help a tool like phyloseq address computational issues that are arising from dealing with this data of this size, without compromising some of the other features (interactivity, reproducibility, connection with existing R tools). Some promising tools already available for R that might help include:


## Sparse Representation

This is simply a back-end consideration for very-large datasets 
that challenge available memory.
This is not the case for most users, or datasets.

[The Matrix package](http://cran.r-project.org/web/packages/Matrix/index.html)
has a wonderful pantheon of data class extensions that could address this, for instance.


## Improved Interoperability with biomformat

Option to store full dataset in a comprehensive, cross-language data structure. 
The biom-format is a good candidate, 
especially now that it has an HDF5 definition
to satisfy the case of very large datasets,
where on-disk storage might make the most sense.

Some candidate packages for storage are:

- HDF5 via rhdf5, biomformat packages

- [the ff package](http://cran.r-project.org/web/packages/ff/index.html)

