Source code for immuneML.reports.data_reports.GLIPH2Exporter

from pathlib import Path

import pandas as pd

from immuneML.data_model.dataset.ReceptorDataset import ReceptorDataset
from immuneML.reports.ReportOutput import ReportOutput
from immuneML.reports.ReportResult import ReportResult
from immuneML.reports.data_reports.DataReport import DataReport
from immuneML.util.PathBuilder import PathBuilder

[docs]class GLIPH2Exporter(DataReport): """ Report which exports the receptor data to GLIPH2 format so that it can be directly used in GLIPH2 tool. Currently, the report accepts only receptor datasets. GLIPH2 publication: Huang H, Wang C, Rubelt F, Scriba TJ, Davis MM. Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening. Nature Biotechnology. Published online April 27, 2020:1-9. `doi:10.1038/s41587-020-0505-4 <>`_ Arguments: condition (str): name of the parameter present in the receptor metadata in the dataset; condition can be anything which can be processed in GLIPH2, such as tissue type or treatment. YAML specification: .. indent with spaces .. code-block:: yaml my_gliph2_exporter: # user-defined name GLIPH2Exporter: condition: epitope # for instance, epitope parameter is present in receptors' metadata with values such as "MtbLys" for Mycobacterium tuberculosis (as shown in the original paper). """
[docs] @classmethod def build_object(cls, **kwargs): return GLIPH2Exporter(**kwargs)
def __init__(self, dataset: ReceptorDataset = None, result_path: Path = None, name: str = None, condition: str = None): super().__init__(dataset, result_path, name) self.condition = condition def _generate(self) -> ReportResult: alpha_chains, beta_chains, trbv, trbj, subject_condition, count = [], [], [], [], [], [] for index, receptor in enumerate(self.dataset.get_data()): alpha_chains.append(receptor.get_chain("alpha").amino_acid_sequence) beta_chains.append(receptor.get_chain("beta").amino_acid_sequence) trbv.append(receptor.get_chain("beta").metadata.v_gene) trbj.append(receptor.get_chain("beta").metadata.j_gene) subject_condition.append(f"{getattr(receptor.metadata, 'subject_id', str(index))}:{receptor.metadata[self.condition]}") count.append(receptor.get_chain("beta").metadata.count if receptor.get_chain('beta').metadata is not None and receptor.get_chain('beta').metadata.count is not None else 1) df = pd.DataFrame({"CDR3b": beta_chains, "TRBV": trbv, "TRBJ": trbj, "CDR3a": alpha_chains, "subject:condition": subject_condition, "count": count}) file_path = self.result_path / "exported_data.tsv" df.to_csv(file_path, sep="\t", index=False) return ReportResult(, output_tables=[ReportOutput(file_path, "exported data in GLIPH2 format")])
[docs] def check_prerequisites(self): if isinstance(self.dataset, ReceptorDataset): return True else: return False