immuneML.workflows.instructions.ligo_sim_feasibility package

Submodules

immuneML.workflows.instructions.ligo_sim_feasibility.FeasibilitySummaryInstruction module

class immuneML.workflows.instructions.ligo_sim_feasibility.FeasibilitySummaryInstruction.FeasibilitySumReports(signal_frequencies: immuneML.reports.ReportResult.ReportResult = None, signal_cooccurrences: immuneML.reports.ReportResult.ReportResult = None, signal_cond_probs: immuneML.reports.ReportResult.ReportResult = None, signal_joint_probs: immuneML.reports.ReportResult.ReportResult = None, p_gen_histogram: immuneML.reports.ReportResult.ReportResult = None, seq_len_dist: immuneML.reports.ReportResult.ReportResult = None, warnings: list = <factory>)[source]

Bases: object

p_gen_histogram: ReportResult = None
seq_len_dist: ReportResult = None
signal_cond_probs: ReportResult = None
signal_cooccurrences: ReportResult = None
signal_frequencies: ReportResult = None
signal_joint_probs: ReportResult = None
warnings: list
class immuneML.workflows.instructions.ligo_sim_feasibility.FeasibilitySummaryInstruction.FeasibilitySummaryInstruction(simulation, sequence_count: int, number_of_processes: int, signals: List[Signal], name: str = None)[source]

Bases: Instruction

FeasibilitySummary instruction creates a small synthetic dataset and reports summary metrics to show if the simulation with the given parameters is feasible. The input parameters to this analysis are the name of the simulation (the same that can be used with LigoSim instruction later if feasibility analysis looks acceptable), and the number of sequences to simulate for estimating the feasibility.

The feasibility analysis is performed for each generative model separately as these could differ in the analyses that will be reported.

Specification arguments:

  • simulation (str): a name of a simulation object containing a list of SimConfigItem as specified under definitions key; defines how to combine signals with simulated data; specified under definitions

  • sequence_count (int): how many sequences to generate to estimate feasibility (default value: 100 000)

  • number_of_processes (int): for the parts of the analysis that are possible to parallelize, how many processes to use

YAML specification:

instructions:
    my_feasibility_summary: # user-defined name of the instruction
        type: FeasibilitySummary # which instruction to execute
        simulation: sim1
        sequence_count: 10000
MAX_SIG_FREQ = 0.1
MIN_SIG_FREQ = 1e-05
run(result_path: Path)[source]
class immuneML.workflows.instructions.ligo_sim_feasibility.FeasibilitySummaryInstruction.FeasibilitySummaryState(simulation: immuneML.simulation.SimConfig.SimConfig, sequence_count: int, signals: List[immuneML.simulation.implants.Signal.Signal], name: str = None, result_path: pathlib.Path = None, reports: Dict[str, immuneML.workflows.instructions.ligo_sim_feasibility.FeasibilitySummaryInstruction.FeasibilitySumReports] = <factory>)[source]

Bases: object

name: str = None
reports: Dict[str, FeasibilitySumReports]
result_path: Path = None
sequence_count: int
signals: List[Signal]
simulation: SimConfig

immuneML.workflows.instructions.ligo_sim_feasibility.feasibility_reports module

immuneML.workflows.instructions.ligo_sim_feasibility.feasibility_reports.report_p_gen_histogram(sequences: BackgroundSequences, p_gen_bin_count: int, path: Path) → ReportResult[source]
immuneML.workflows.instructions.ligo_sim_feasibility.feasibility_reports.report_seq_len_dist(sequences: BackgroundSequences, sequence_type: SequenceType, path: Path) → ReportResult[source]
immuneML.workflows.instructions.ligo_sim_feasibility.feasibility_reports.report_signal_cond_probs(signal_matrix: ndarray, signal_names: list, path: Path) → ReportResult[source]
immuneML.workflows.instructions.ligo_sim_feasibility.feasibility_reports.report_signal_cooccurrences(unique_values: ndarray, counts: ndarray, path: Path) → ReportResult[source]
immuneML.workflows.instructions.ligo_sim_feasibility.feasibility_reports.report_signal_frequencies(frequencies: pandas.DataFrame, path: Path) → ReportResult[source]
immuneML.workflows.instructions.ligo_sim_feasibility.feasibility_reports.report_signal_joint_probs(signal_matrix: ndarray, signal_names: list, path: Path) → ReportResult[source]

Module contents