Configuration¶

PipelineConfig¶

The PipelineConfig dataclass controls the QuantificationPipeline. It uses nested sub-configurations for each pipeline stage.

from mokume.pipeline import PipelineConfig
from mokume.pipeline.config import (
    InputConfig,
    FilterConfig,
    NormalizationConfig,
    QuantificationConfig,
    IRSConfig,
    BatchCorrectionConfig,
    DEConfig,
    OutputConfig,
)

InputConfig¶

Field	Type	Default	Description
`parquet`	`str`	required	Input parquet file path
`sdrf`	`str \\| None`	`None`	SDRF metadata file
`fasta_file`	`str \\| None`	`None`	FASTA file (for iBAQ)

FilterConfig¶

Field	Type	Default	Description
`min_aa`	`int`	`7`	Minimum amino acid length
`min_unique_peptides`	`int`	`2`	Minimum unique peptides per protein
`remove_contaminants`	`bool`	`True`	Remove contaminants and decoys

NormalizationConfig¶

Field	Type	Default	Description
`run_method`	`str`	`"median"`	Run-level normalization: median, mean, iqr, max, max_min, none
`sample_method`	`str`	`"globalMedian"`	Sample-level: globalMedian, conditionMedian, hierarchical, tmm, none
`proteins_file`	`str \\| None`	`None`	File with protein IDs for normalization

QuantificationConfig¶

Field	Type	Default	Description
`method`	`str`	`"maxlfq"`	Quantification method
`ion_alignment`	`str \\| None`	`None`	Ion alignment: none or hierarchical
`coverage_threshold`	`float \\| None`	`None`	Min non-missing fraction per condition
`ratio_fraction_merge`	`str`	`"mean"`	Fraction merge: mean or max
`directlfq_num_cores`	`int \\| None`	`None`	CPU cores for DirectLFQ
`directlfq_min_nonan`	`int`	`1`	Min non-NaN values
`directlfq_num_samples_quadratic`	`int`	`50`	Quadratic threshold

IRSConfig¶

Field	Type	Default	Description
`enabled`	`bool`	`False`	Enable IRS normalization
`reference_samples`	`list \\| None`	`None`	Reference sample names
`sdrf_column`	`str \\| None`	`None`	SDRF column for detection
`sdrf_values`	`list \\| None`	`None`	Reference indicator values
`reference_regex`	`str`	`"pool\\|powder\\|ref\\|reference\\|bridge"`	Auto-detection regex
`stat`	`str`	`"median"`	Plex reference statistic
`remove_reference`	`bool`	`False`	Remove reference samples

BatchCorrectionConfig¶

Field	Type	Default	Description
`enabled`	`bool`	`False`	Enable ComBat
`method`	`str`	`"sample_prefix"`	Batch detection: sample_prefix, run, column
`column`	`str \\| None`	`None`	SDRF column (for method="column")
`covariates`	`list \\| None`	`None`	Covariate columns to preserve
`parametric`	`bool`	`True`	Use parametric ComBat
`mean_only`	`bool`	`False`	Only correct mean (not variance)
`ref_batch`	`int \\| None`	`None`	Reference batch index

DEConfig¶

Field	Type	Default	Description
`enabled`	`bool`	`False`	Enable DE analysis
`contrasts`	`list \\| None`	`None`	Contrasts (e.g., ["A-B"])
`method`	`str`	`"ttest"`	Method: ttest or limma
`log2fc_threshold`	`float`	`0.5`	Min absolute log2 fold change
`fdr_threshold`	`float`	`0.05`	Max FDR
`output`	`str \\| None`	`None`	Output file for DE results

OutputConfig¶

Field	Type	Default	Description
`export_peptides`	`str \\| None`	`None`	Export peptides to file
`export_ions`	`str \\| None`	`None`	Export ions to file
`plot_dir`	`str \\| None`	`None`	Plot output directory
`plot_volcano`	`bool`	`False`	Generate volcano plots
`plot_heatmap`	`bool`	`False`	Generate heatmaps
`plot_pca`	`bool`	`False`	Generate PCA plots
`highlight_genes`	`list \\| None`	`None`	Genes to highlight in plots
`interactive_report`	`bool`	`False`	Generate HTML QC report
`report_output`	`str \\| None`	`None`	Report output path

Full Example¶

config = PipelineConfig(
    input=InputConfig(
        parquet="features.parquet",
        sdrf="experiment.sdrf.tsv",
    ),
    filtering=FilterConfig(
        min_aa=7,
        min_unique_peptides=2,
        remove_contaminants=True,
    ),
    normalization=NormalizationConfig(
        run_method="median",
        sample_method="globalMedian",
    ),
    quantification=QuantificationConfig(
        method="median",
    ),
    irs=IRSConfig(
        enabled=True,
        remove_reference=True,
    ),
    batch=BatchCorrectionConfig(
        enabled=True,
        method="sample_prefix",
        covariates=["characteristics[sex]"],
    ),
    de=DEConfig(
        enabled=True,
        contrasts=["NASH-HL"],
        method="ttest",
    ),
    output=OutputConfig(
        plot_dir="plots/",
        plot_volcano=True,
        plot_pca=True,
        interactive_report=True,
        report_output="qc_report.html",
    ),
)

Preprocessing Filter Configuration¶

Filter configurations use YAML format. See Preprocessing Filters for details.

YAML Structure¶

name: my_filters          # Configuration name
enabled: true             # Master enable/disable

intensity:
  min_intensity: 0.0
  remove_zero_intensity: true
  cv_threshold: null      # null = disabled
  min_replicate_agreement: 1
  quantile_lower: 0.0
  quantile_upper: 1.0

peptide:
  min_peptide_length: 7
  max_peptide_length: 50
  allowed_charge_states: null    # e.g., [2, 3, 4]
  exclude_modifications: []
  max_missed_cleavages: null
  min_search_score: null
  exclude_sequence_patterns: []

protein:
  min_unique_peptides: 2
  remove_contaminants: true
  remove_decoys: true
  contaminant_patterns:
    - CONTAMINANT
    - ENTRAP
    - DECOY
  fdr_threshold: 0.01
  min_coverage: 0.0
  razor_peptide_handling: keep

run_qc:
  min_total_intensity: 0.0
  min_identified_features: 0
  max_missing_rate: 1.0
  min_sample_correlation: null

Pre-configured Templates¶

Available in tests/example/filters/:

Template	Use Case
`basic_qc.yaml`	Standard experiments
`stringent_filtering.yaml`	Publication-quality
`tmt_labeling.yaml`	TMT/iTRAQ
`dia_analysis.yaml`	DIA workflows
`exploratory_analysis.yaml`	Data exploration