IPFIOConfig

class pyprophet._config.IPFIOConfig(infile: str, outfile: str, subsample_ratio: float, level: str, context: str, ipf_ms1_scoring: bool = True, ipf_ms2_scoring: bool = True, ipf_h0: bool = True, ipf_grouped_fdr: bool = False, ipf_grouped_fdr_strategy: Literal['num_peptidoforms'] = 'num_peptidoforms', ipf_max_precursor_pep: float = 0.7, ipf_max_peakgroup_pep: float = 0.7, ipf_max_precursor_peakgroup_pep: float = 0.4, ipf_max_transition_pep: float = 0.6, ipf_min_supporting_transitions: int = 0, ipf_min_peakgroup_intensity: float = 0.0, propagate_signal_across_runs: bool = False, ipf_max_alignment_pep: float = 0.7, across_run_confidence_threshold: float = 0.5, use_alignment_candidates: bool = False, min_alignment_mapping_confidence: float = 0.5)

Bases: BaseIOConfig

Configuration for Inference of Peptidoforms (IPF).

ipf_ms1_scoring

Use MS1 precursor data for IPF.

Type:

bool

ipf_ms2_scoring

Use MS2 precursor data for IPF.

Type:

bool

ipf_h0

Include possibility that peak groups are not covered by the peptidoform space (null hypothesis H0).

Type:

bool

ipf_grouped_fdr

[Experimental] Compute grouped FDR instead of pooled FDR to support heterogeneous peptidoform counts per peak group.

Type:

bool

ipf_grouped_fdr_strategy

Grouping strategy used when grouped FDR is enabled.

Type:

Literal[“num_peptidoforms”]

ipf_max_precursor_pep

Maximum PEP to consider scored precursors in IPF.

Type:

float

ipf_max_peakgroup_pep

Maximum PEP to consider scored peak groups in IPF.

Type:

float

ipf_max_precursor_peakgroup_pep

Maximum BHM layer 1 integrated precursor-peakgroup PEP to consider in IPF.

Type:

float

ipf_max_transition_pep

Maximum PEP to consider scored transitions in IPF.

Type:

float

ipf_min_supporting_transitions

Minimum number of supporting transitions required to keep an inferred peptidoform result.

Type:

int

ipf_min_peakgroup_intensity

Minimum MS2 peakgroup area intensity required to keep an inferred peptidoform result.

Type:

float

propagate_signal_across_runs

Propagate signal across runs (requires alignment step).

Type:

bool

ipf_max_alignment_pep

Maximum PEP to consider for good alignments.

Type:

float

across_run_confidence_threshold

Maximum PEP threshold for propagating signal across runs for aligned features.

Type:

float

use_alignment_candidates

Use FEATURE_MS2_ALIGNMENT_CANDIDATE instead of FEATURE_MS2_ALIGNMENT when available.

Type:

bool

min_alignment_mapping_confidence

Minimum MAPPING_CONFIDENCE required when using FEATURE_MS2_ALIGNMENT_CANDIDATE.

Type:

float

__eq__(other)

Return self==value.

__hash__ = None

__init__(infile: str, outfile: str, subsample_ratio: float, level: str, context: str, ipf_ms1_scoring: bool = True, ipf_ms2_scoring: bool = True, ipf_h0: bool = True, ipf_grouped_fdr: bool = False, ipf_grouped_fdr_strategy: Literal['num_peptidoforms'] = 'num_peptidoforms', ipf_max_precursor_pep: float = 0.7, ipf_max_peakgroup_pep: float = 0.7, ipf_max_precursor_peakgroup_pep: float = 0.4, ipf_max_transition_pep: float = 0.6, ipf_min_supporting_transitions: int = 0, ipf_min_peakgroup_intensity: float = 0.0, propagate_signal_across_runs: bool = False, ipf_max_alignment_pep: float = 0.7, across_run_confidence_threshold: float = 0.5, use_alignment_candidates: bool = False, min_alignment_mapping_confidence: float = 0.5) → None

__repr__()

Return repr(self).

classmethod from_cli_args(infile, outfile, subsample_ratio, level, context, ipf_ms1_scoring, ipf_ms2_scoring, ipf_h0, ipf_grouped_fdr, ipf_grouped_fdr_strategy, ipf_max_precursor_pep, ipf_max_peakgroup_pep, ipf_max_precursor_peakgroup_pep, ipf_max_transition_pep, ipf_min_supporting_transitions, ipf_min_peakgroup_intensity, propagate_signal_across_runs, ipf_max_alignment_pep, across_run_confidence_threshold, use_alignment_candidates=False, min_alignment_mapping_confidence=0.5)

Creates a configuration object from command-line arguments.