Source code for immuneML.reports.data_reports.ReceptorDatasetOverview

from pathlib import Path
from typing import Tuple, List

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

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 ReceptorDatasetOverview(DataReport): """ This report plots the length distribution per chain for a receptor (paired-chain) dataset. Arguments: batch_size (int): how many receptors to load at once; 50 000 by default YAML specification: .. indent with spaces .. code-block:: yaml reports: my_receptor_overview_report: ReceptorDatasetOverview """ def __init__(self, batch_size: int, dataset: ReceptorDataset = None, result_path: Path = None, name: str = None): super().__init__(dataset, result_path, name) self.batch_size = batch_size
[docs] @classmethod def build_object(cls, **kwargs): return ReceptorDatasetOverview(**kwargs)
def _generate(self) -> ReportResult: PathBuilder.build(self.result_path) figure, tables = self._generate_sequence_length_distribution_plots() return ReportResult(name=self.name, output_figures=[figure], output_tables=tables) def _prepare_data_for_length_distribution(self): receptors = {} for receptor in self.dataset.get_data(self.batch_size): for chain in receptor.get_chains(): receptor_dict = { "length": len(receptor.get_chain(chain).get_sequence()), "chain": chain } if chain in receptors: receptors[chain].append(receptor_dict) else: receptors[chain] = [receptor_dict] chains = list(receptors.keys()) dfs = [pd.DataFrame(receptors[chain]) for chain in chains] return dfs, chains def _generate_sequence_length_distribution_plots(self) -> Tuple[ReportOutput, List[ReportOutput]]: dfs, chains = self._prepare_data_for_length_distribution() fig = go.Figure() fig.add_trace(go.Histogram( x=dfs[0]["length"], histnorm='probability density', opacity=0.75, name=chains[0], marker={'color': px.colors.diverging.Tealrose[0]} )) fig.add_trace(go.Histogram( x=dfs[1]["length"], histnorm='probability density', opacity=0.75, name=chains[1], marker={'color': px.colors.diverging.Tealrose[-2]} )) fig.update_layout(title_text="Receptor sequence length distribution per chain", xaxis_title_text="receptor sequence length", yaxis_title_text="frequency", bargap=0.2, bargroupgap=0.1, template="plotly_white") image_output, table_outputs = self._store_sequence_distribution_data(fig, dfs, chains) return image_output, table_outputs def _store_sequence_distribution_data(self, fig, dfs, chains): fig.write_html(str(self.result_path / "sequence_length_distribution.html")) image_output = ReportOutput(self.result_path / "sequence_length_distribution.html", name="sequence length distribution per chain") table_outputs = [ReportOutput(self.result_path / f"sequence_length_distribution_chain_{chains[index]}.csv") for index in range(len(chains))] for index, df in enumerate(dfs): df.to_csv(table_outputs[index].path, index=False) return image_output, table_outputs
[docs] def check_prerequisites(self): if isinstance(self.dataset, ReceptorDataset): return True else: return False