# Developing a QIIME 2 plugin¶

Note

This document is a work in progress, and serves as basic instructions for creating a QIIME 2 plugin. You can also find some (very preliminary) developer documentation at https://dev.qiime2.org.

Creating a QIIME 2 plugin allows you to provide microbiome analysis functionality to QIIME 2 users. A plugin can be a standalone software project, or you can make a few small additions to your existing software project to make it a QIIME 2 plugin. Creating a single QIIME 2 plugin will make your functionality accessible through any QIIME 2 interface, including the QIIME 2 Studio, q2cli, and the Artifact API.

## Overview¶

There are several high-level steps to creating a QIIME 2 plugin:

1. A QIIME 2 plugin must define one or more Python 3 functions that will be accessible through QIIME. The plugin must be a Python 3 package that can be installed with setuptools.

2. The plugin must then instantiate a qiime2.plugin.Plugin object and define some information including the name of the plugin and its URL. In the plugin package’s setup.py file, this instance will be defined as an entry point.

3. The plugin must then register its functions as QIIME 2 Actions, which will be accessible to users through any of the QIIME 2 interfaces.

4. Optionally, the plugin should be distributed through Anaconda, as that will simplify its installation for QIIME 2 users (since that is the supported mechanism for installing QIIME 2).

These steps are covered in detail below.

Writing a simple QIIME 2 plugin should be a straightforward process. For example, the q2-emperor plugin, which connects Emperor to QIIME 2, is written in only around 100 lines of code. It is a standalone plugin that defines how and which functionality in Emperor should be accessible through QIIME 2. Plugins will vary in their complexity. For example, a plugin that defines a lot of new functionality would likely be quite a bit bigger. q2-diversity is a good example of this. Unlike q2-emperor, there is some specific functionality (and associated unit tests) defined in this project, and it depends on several other Python 3 compatible libraries.

Before starting to write a plugin, you should install QIIME 2 and some plugins to familiarize yourself with the system and to provide a means for testing your plugin.

## Plugin components¶

The following discussion will refer to the q2-diversity plugin as an example. This plugin can serve as a reference as you define your own QIIME 2 plugins.

Note

QIIME 2 does not place restrictions on how a plugin package is structured. The q2-diversity plugin is however a good representative of the conventions present in many of the initial QIIME 2 plugins. This package structure is simply a recommendation and a starting point for developing your own plugin; feel free to deviate from this structure as necessary or desired.

### Define functionality¶

QIIME 2 users will access your functionality as QIIME 2 Actions. These Actions can be either Methods or Visualizers. A Method is an operation that takes some combination of Artifacts and Parameters as input, and produces one or more Artifacts as output. These output Artifacts could subsequently be used as input to other QIIME 2 Methods or Visualizers. A Visualizer is an operation that takes some combination of Artifacts and Parameters as input, and produces exactly one Visualization as output. Output Visualizations, by definition, cannot be used as input to other QIIME 2 Methods or Visualizers. Methods therefore can produce intermediate or terminal output in a QIIME analysis, while Visualizers can only create terminal output.

This section will describe how to define Python 3 functions that can be converted to QIIME 2 Methods or Visualizers. These functions can be defined anywhere in your project; QIIME doesn’t put restrictions on how your plugin package is structured.

#### Create a function to register as a Method¶

A function that can be registered as a Method will have a Python 3 API, and the inputs and outputs for that function will be annotated with their data types using mypy syntax. mypy annotation does not impact functionality (though the syntax is new to Python 3), so these can be added to existing functions in your Python 3 software project. An example is q2_diversity.beta_phylogenetic, which takes a biom.Table, an skbio.TreeNode and a str as input, and produces an skbio.DistanceMatrix as output. The signature for this function is:

def beta_phylogenetic(table: biom.Table, phylogeny: skbio.TreeNode,
metric: str)-> skbio.DistanceMatrix:


As far as QIIME is concerned, it doesn’t matter what happens inside this function (as long as it adheres to the contract defined by the signature regarding the input and output types). For example, q2_diversity.beta_phylogenetic is making some calls to the skbio and biom APIs, but it could be doing anything, including making system calls (if your plugin is wrapping a command line application), executing an R library, etc.

#### Create a function to register as a Visualizer¶

Defining a function that can be registered as a Visualizer is very similar to defining one that can be registered as a Method with a few additional requirements.

First, the first parameter to this function must be output_dir. This parameter should be annotated with type str.

Next, at least one index.* file must be written to output_dir by the function. This index file will provide the starting point for your users to explore the Visualization object that is generated by the Visualizer. Index files with different extensions can be created by the function (e.g., index.html, index.tsv, index.png), but at least one must be created. You can write whatever files you want to output_dir, including tables, graphics, and textual descriptions of the results, but you should expect that your users will want to find those files through your index file(s). If your function does create many different files, an index.html containing links to those files is likely to be helpful.

Finally, the function cannot return anything, and its return type should be annotated as None.

q2_diversity.alpha_group_significance is an example of a function that can be registered as a Visualizer. In addition to its output_dir, it takes alpha diversity results in a pandas.Series and sample metadata in a qiime2.Metadata object and creates several different files (figures and tables) that are linked and/or presented in an index.html file. The signature of this function is:

def alpha_group_significance(output_dir: str, alpha_diversity: pd.Series,


### Instantiating a plugin¶

The next step is to instantiate a QIIME 2 Plugin object. This might look like the following:

from qiime2.plugin import Plugin
import q2_diversity

plugin = Plugin(
name='diversity',
version=q2_diversity.__version__,
website='https://qiime2.org',
user_support_text='https://forum.qiime2.org',
package='q2_diversity'
)


This will provide QIIME with essential information about your Plugin.

The name parameter is the name that users will use to access your plugin from within different QIIME 2 interfaces. It should be a “command line friendly” name, so should not contain spaces or punctuation. (Avoiding uppercase characters and using dashes (-) instead of underscores (_) are preferable in the plugin name, but not required).

version should be the version number of your package (the same that is used in its setup.py).

website should be the page where you’d like end users to refer for more information about your package. Since q2-diversity doesn’t have its own website, we’re including the QIIME 2 website here.

package should be the Python package name for your plugin.

While not shown in the previous example, plugin developers can optionally provide the following parameters to qiime2.plugin.Plugin:

• citation_text: free text describing how users should cite the plugin and/or the underlying tools it wraps. If not provided, users are told to cite the website.

• user_support_text: free text describing how users should get help with the plugin (e.g. issue tracker, StackOverflow tag, mailing list, etc.). If not provided, users are referred to the website for support. q2-diversity is supported on the QIIME 2 Forum, so we include that URL here. We encourage plugin developers to support their plugins on the QIIME 2 Forum, so you can include that URL as the user_support_text for your plugin. If you do that, you should get in the habit of monitoring the QIIME 2 Forum for technical support questions.

The Plugin object can live anywhere in your project, but by convention it will be in a file called plugin_setup.py. For an example, see q2_diversity/plugin_setup.py.

### Registering an Action¶

Once you have functions that you’d like to register as Actions (i.e., either Methods or Visualizers), and you’ve instantiated your Plugin object, you are ready to register those functions. This will likely be done in the file where the Plugin object was instantiated, as it will use that instance (which will be referred to as plugin in the following examples).

#### Registering a Method¶

First we’ll register a Method by calling plugin.methods.register_function as follows:

from q2_types import (FeatureTable, Frequency, Phylogeny,
Rooted, DistanceMatrix)
from qiime2.plugin import Str, Choices, Properties, Metadata

import q2_diversity
import q2_diversity._beta as beta

plugin.methods.register_function(
function=q2_diversity.beta_phylogenetic,
inputs={'table': FeatureTable[Frequency],
'phylogeny': Phylogeny[Rooted]},
parameters={'metric': Str % Choices(beta.phylogenetic_metrics())},
outputs=[('distance_matrix', DistanceMatrix % Properties('phylogenetic'))],
input_descriptions={
'table': ('The feature table containing the samples over which beta '
'diversity should be computed.'),
'phylogeny': ('Phylogenetic tree containing tip identifiers that '
'correspond to the feature identifiers in the table. '
'This tree can contain tip ids that are not present in '
'the table, but all feature ids in the table must be '
'present in this tree.')
},
parameter_descriptions={
'metric': 'The beta diversity metric to be computed.'
},
output_descriptions={'distance_matrix': 'The resulting distance matrix.'},
name='Beta diversity (phylogenetic)',
description=("Computes a user-specified phylogenetic beta diversity metric"
" for all pairs of samples in a feature table.")
)


The values being provided are:

function: The function to be registered as a method.

inputs: A dictionary indicating the parameter name and its semantic type, for each input Artifact. These semantic types differ from the data types that you provided in your mypy annotation of the input, as semantic types describe the data, where the data types indicate the structure of the data. The currently available semantic types are detailed here, along with a discussion of the motivation for defining semantic types. In the example above we’re indicating that the table parameter must be a FeatureTable of Frequency (i.e. counts), and that the phylogeny parameter must be a Phylogeny that is Rooted. Notice that the keys in inputs map directly to the parameter names in q2_diversity.beta_phylogenetic.

parameters: A dictionary indicating the parameter name and its semantic type, for each input Parameter. These parameters are primitive values (i.e., non-Artifacts). In the example above, we’re indicating that the metric should be a string from a specific set (in this case, the set of known phylogenetic beta diversity metrics).

outputs: A list of tuples indicating each output name and its semantic type.

input_descriptions: A dictionary containing input artifact names and their corresponding descriptions. This information is used by interfaces to instruct users how to use each specific input artifact.

parameter_descriptions: A dictionary containing parameter names and their corresponding descriptions. This information is used by interfaces to instruct users how to use each specific input parameter. You should not include any default parameter values in these descriptions, as these will generally be added automatically by an interface.

output_descriptions: A dictionary containing output artifact names and their corresponding descriptions. This information is used by interfaces to inform users what each specific output artifact will be.

name: A human-readable name for the Method. This may be presented to users in interfaces.

description: A human-readable description of the Method. This may be presented to users in interfaces.

#### Registering a Visualizer¶

Registering Visualizers is the same as registering Methods, with two exceptions.

First, you call plugin.visualizers.register_function to register a Visualizer.

Next, you do not provide outputs or output_descriptions when making this call, as Visualizers, by definition, only return a single visualization. Since the visualization output path is a required parameter, you do not include this in an outputs list (it would be the same for every Visualizer that was ever registered, so it is added automatically).

Registering q2_diversity.alpha_group_significance as a Visualizer looks like the following:

plugin.visualizers.register_function(
function=q2_diversity.alpha_group_significance,
input_descriptions={
'alpha_diversity': 'Vector of alpha diversity values by sample.'
},
parameter_descriptions={
},
name='Alpha diversity comparisons',
description=("Visually and statistically compare groups of alpha diversity"
" values.")
)


### Defining your plugin object as an entry point¶

Finally, you need to tell QIIME where to find your instantiated Plugin object. This is done by defining it as an entry_point in your project’s setup.py file. In q2-diversity, this is done as follows:

setup(
...
entry_points={
'qiime2.plugins': ['q2-diversity=q2_diversity.plugin_setup:plugin']
}
)


The relevant key in the entry_points dictionary will be 'qiime2.plugins', and the value will be a single element list containing a string formatted as <distribution-name>=<import-path>:<instance-name>. <distribution-name> is the name of the Python package distribution (matching the value passed for name in this call to setup); <import-path> is the import path for the Plugin instance you created above; and <instance-name> is the name for the Plugin instance you created above.

## Testing your plugin with q2cli during development¶

If you are testing your plugin with q2cli (i.e. the qiime command) while you are developing it, you’ll need to run qiime dev refresh-cache to see the latest changes to your plugin reflected in the command line interface (CLI). You’ll need to run this command anytime you modify your plugin’s interface (e.g. add/rename/remove a command or its inputs/parameters/outputs, and edit any of the plugin/action/input/parameter/output descriptions).

Another option is to set the environment variable Q2CLIDEV=1 so that the cache is refreshed every time a command is run. This will slow down the CLI while developing because refreshing the cache is slow. However, the CLI is much faster when a user installs release versions of QIIME 2 and plugins, so this slowdown should only be apparent when developing a plugin.

This manual refreshing of the q2cli cache is necessary because it can’t detect when changes are made to a plugin’s code while under development (the plugin’s version remains the same across code edits). This manual refreshing of the cache should only be necessary while developing a plugin; when users install QIIME 2 and your released plugin (i.e. no longer in development), the cache will automatically be updated when necessary.

## Plugin testing¶

Many of the QIIME 2 plugins, including q2-emperor and q2-diversity, have continuous integration (CI) configuration for Travis-CI in their software repositories. This allows for automated testing any time a change to the plugin code is committed on GitHub if Travis-CI is enabled on the plugin’s software repository. Plugin CI testing generally includes flake8 linting/style-checking and a nose or py.test command for running unit tests.

Plugin developers are encouraged to add unit tests for their plugin’s functionality, and to perform style checking with flake8. Unit tests are an important part of determining if your software is working as expected, which will give you and your users confidence in the plugin. Adhering to a style convention, and checking that style with a tool like flake8, is very helpful for others who want to understand your code, including users who want an in depth understanding of the functionality and potential open source software contributors.

Wilson et al. (2014) present a good discussion of software testing and related topics that is very helpful for scientists who are beginning to develop and distribute software.