airr 1.3.1

Reading AIRR Repertoire metadata files

The airr package contains functions to read and write AIRR repertoire metadata 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 repertoires are loaded into memory at once and no streaming interface is provided:

import airr

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

Why are the repertoires in a list versus in a dictionary keyed by the repertoire_id? There are two primary reasons for this. First, the repertoire_id might not have been assigned yet. Some systems might allow MiAIRR metadata to be entered but the repertoire_id is assigned to that data later by another process. Without the repertoire_id, the data could not be stored in a dictionary. Secondly, the list allows the repertoire data to have a default ordering. If you know that the repertoires all have a unique repertoire_id then you can quickly create a dictionary object using a comprehension:

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

Writing AIRR Repertoire metadata files

Writing AIRR repertoire metadata is also a light wrapper over standard YAML or JSON parsers. The airr library provides a function to create a blank repertoire object in the appropriate format with all of the required fields. As with the load 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
reps = []
for i in range(5):
    reps.append(airr.repertoire_template())

# Write the repertoires
airr.write_repertoire('output.airr.json', reps)

Reading AIRR Rearrangement TSV files

The airr package contains functions to read and write AIRR rearrangement 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 formatted 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 repertoire and rearrangement data files to insure 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 file
airr-tools validate rearrangement -a input.tsv

# Validate a repertoire metadata file
airr-tools validate repertoire -a input.airr.json

Combining Repertoire metadata and Rearrangement files

The airr package does not keep track of which repertoire metadata files are associated with rearrangement files, so users will need to handle those associations themselves. However, in the data, the repertoire_id field forms the link. 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 the repertoires
data = airr.load_repertoire('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