Installation

Install in the usual manner from PyPI:

> pip3 install airr --user

Or from the downloaded source code directory:

> python3 setup.py install --user

Quick Start

Deprecation Notice

The load_repertoire, write_repertoire, and validate_repertoire functions have been deprecated for the new generic load_airr_data, write_airr_data, and validate_airr_data functions. These new functions are backwards compatible with the Repertoire metadata format but also support the new AIRR objects such as GermlineSet, RepertoireGroup, GenotypeSet, Cell and Clone. This new format is defined by the DataFile Schema, which describes a standard set of objects included in a file containing AIRR Data Model presentations. Currently, the AIRR DataFile does not completely support Rearrangement, so users should continue using AIRR TSV files and its specific functions. Also, the repertoire_template function has been deprecated for the Schema.template method, which can now be called on any AIRR Schema to create a blank object.

Reading AIRR Data Files

The airr package contains functions to read and write AIRR Data Model files. The file format is either YAML or JSON, and the package provides a light wrapper over the standard parsers. The file needs a json, yaml, or yml file extension so that the proper parser is utilized. All of the AIRR objects are loaded into memory at once and no streaming interface is provided:

import airr

# Load the AIRR data
data = airr.read_airr('input.airr.json')
# loop through the repertoires
for rep in data['Repertoire']:
    print(rep)

Why are the AIRR objects, such as Repertoire, GermlineSet, and etc., in a list versus in a dictionary keyed by their identifier (e.g., repertoire_id)? There are two primary reasons for this. First, the identifier might not have been assigned yet. Some systems might allow MiAIRR metadata to be entered but the identifier is assigned to that data later by another process. Without the identifier, the data could not be stored in a dictionary. Secondly, the list allows the data to have a default ordering. If you know that the data has a unique identifier then you can quickly create a dictionary object using a comprehension. For example, with repertoires:

rep_dict = { obj['repertoire_id'] : obj for obj in data['Repertoire'] }

another example with germline sets:

germline_dict = { obj['germline_set_id'] : obj for obj in data['GermlineSet'] }

Writing AIRR Data Files

Writing an AIRR Data File is also a light wrapper over standard YAML or JSON parsers. Multiple AIRR objects, such as Repertoire, GermlineSet, and etc., can be written together into the same file. In this example, we use the airr library template method to create some blank Repertoire objects, and write them to a file. As with the read function, the complete list of repertoires are written at once, there is no streaming interface:

import airr

# Create some blank repertoire objects in a list
data = { 'Repertoire': [] }
for i in range(5):
    data['Repertoire'].append(airr.schema.RepertoireSchema.template())

# Write the AIRR Data
airr.write_airr('output.airr.json', data)

Reading AIRR Rearrangement TSV files

The airr package contains functions to read and write AIRR Rearrangement TSV files as either iterables or pandas data frames. The usage is straightforward, as the file format is a typical tab delimited file, but the package performs some additional validation and type conversion beyond using a standard CSV reader:

import airr

# Create an iteratable that returns a dictionary for each row
reader = airr.read_rearrangement('input.tsv')
for row in reader: print(row)

# Load the entire file into a pandas data frame
df = airr.load_rearrangement('input.tsv')

Writing AIRR Rearrangement TSV files

Similar to the read operations, write functions are provided for either creating a writer class to perform row-wise output or writing the entire contents of a pandas data frame to a file. Again, usage is straightforward with the airr output functions simply performing some type conversion and field ordering operations:

import airr

# Create a writer class for iterative row output
writer = airr.create_rearrangement('output.tsv')
for row in reader:  writer.write(row)

# Write an entire pandas data frame to a file
airr.dump_rearrangement(df, 'file.tsv')

By default, create_rearrangement will only write the required fields in the output file. Additional fields can be included in the output file by providing the fields parameter with an array of additional field names:

# Specify additional fields in the output
fields = ['new_calc', 'another_field']
writer = airr.create_rearrangement('output.tsv', fields=fields)

A common operation is to read an AIRR rearrangement file, and then write an AIRR rearrangement file with additional fields in it while keeping all of the existing fields from the original file. The derive_rearrangement function provides this capability:

import airr

# Read rearrangement data and write new file with additional fields
reader = airr.read_rearrangement('input.tsv')
fields = ['new_calc']
writer = airr.derive_rearrangement('output.tsv', 'input.tsv', fields=fields)
for row in reader:
    row['new_calc'] = 'a value'
    writer.write(row)

Validating AIRR data files

The airr package can validate AIRR Data Model JSON/YAML files and Rearrangement TSV files to ensure that they contain all required fields and that the fields types match the AIRR Schema. This can be done using the airr-tools command line program or the validate functions in the library can be called:

# Validate a rearrangement TSV file
airr-tools validate rearrangement -a input.tsv

# Validate an AIRR DataFile
airr-tools validate airr -a input.airr.json

Combining Repertoire metadata and Rearrangement files

The airr package does not currently keep track of which AIRR Data Model files are associated with which Rearrangement TSV files, though there is ongoing work to define a standardized manifest, so users will need to handle those associations themselves. However, in the data, AIRR identifier fields, such as repertoire_id, form the link between objects in the AIRR Data Model. The typical usage is that a program is going to perform some computation on the Rearrangements, and it needs access to the Repertoire metadata as part of the computation logic. This example code shows the basic framework for doing that, in this case doing gender specific computation:

import airr

# Load AIRR data containing repertoires
data = airr.read_airr('input.airr.json')

# Put repertoires in dictionary keyed by repertoire_id
rep_dict = { obj['repertoire_id'] : obj for obj in data['Repertoire'] }

# Create an iteratable for rearrangement data
reader = airr.read_rearrangement('input.tsv')
for row in reader:
    # get repertoire metadata with this rearrangement
    rep = rep_dict[row['repertoire_id']]

    # check the gender
    if rep['subject']['sex'] == 'male':
        # do male specific computation
    elif rep['subject']['sex'] == 'female':
        # do female specific computation
    else:
        # do other specific computation