Preprocessing Filters

mokume provides a comprehensive filter system for quality control, configurable via YAML/JSON files or CLI options.

Filter Categories

Intensity Filters

Filter	Parameter	Default	Description
MinIntensityFilter	min_intensity	0.0	Remove features below threshold
CVThresholdFilter	cv_threshold	null	Max CV across replicates
ReplicateAgreementFilter	min_replicate_agreement	1	Min replicates with detection
QuantileFilter	quantile_lower/upper	0.0/1.0	Remove intensity outliers

Peptide Filters

Filter	Parameter	Default	Description
PeptideLengthFilter	min/max_peptide_length	7/50	Peptide length range
ChargeStateFilter	allowed_charge_states	null	Allowed charges (e.g., [2,3,4])
ModificationFilter	exclude_modifications	[]	Remove specific modifications
MissedCleavageFilter	max_missed_cleavages	null	Max missed cleavages
SearchScoreFilter	min_search_score	null	Min search engine score
SequencePatternFilter	exclude_sequence_patterns	[]	Regex patterns to exclude

Protein Filters

Filter	Parameter	Default	Description
ContaminantFilter	remove_contaminants/decoys	true	Remove contaminants/decoys
MinPeptideFilter	min_unique_peptides	2	Min unique peptides per protein
ProteinFDRFilter	fdr_threshold	0.01	Protein-level FDR
CoverageFilter	min_coverage	0.0	Min sequence coverage
RazorPeptideFilter	razor_peptide_handling	"keep"	Handle shared peptides

Run/Sample QC Filters

Filter	Parameter	Default	Description
RunIntensityFilter	min_total_intensity	0.0	Min total intensity per run
MinFeaturesFilter	min_identified_features	0	Min features per run
MissingRateFilter	max_missing_rate	1.0	Max missing value rate
SampleCorrelationFilter	min_sample_correlation	null	Min replicate correlation

Configuration

YAML Configuration File

Generate an example configuration:

mokume features2peptides --generate-filter-config filters.yaml

Example basic_qc.yaml:

name: basic_qc
enabled: true

intensity:
  remove_zero_intensity: true

peptide:
  min_peptide_length: 7
  max_peptide_length: 50

protein:
  min_unique_peptides: 2
  remove_contaminants: true
  remove_decoys: true
  contaminant_patterns:
    - CONTAMINANT
    - ENTRAP
    - DECOY

Using Filter Configurations

# From config file
mokume features2peptides \
    -p features.parquet -s experiment.sdrf.tsv \
    --filter-config filters.yaml \
    --output peptides.csv

# CLI overrides (take precedence over config file)
mokume features2peptides \
    -p features.parquet -s experiment.sdrf.tsv \
    --filter-config filters.yaml \
    --filter-min-intensity 1000 \
    --filter-cv-threshold 0.3 \
    --output peptides.csv

# CLI-only filtering (no config file)
mokume features2peptides \
    -p features.parquet -s experiment.sdrf.tsv \
    --filter-min-intensity 500 \
    --filter-min-unique-peptides 2 \
    --output peptides.csv

Python API

from mokume.preprocessing.filters import (
    load_filter_config,
    get_filter_pipeline,
    generate_example_config,
)
from mokume.model.filters import PreprocessingFilterConfig

# Generate example
generate_example_config("filters.yaml")

# Load from file
config = load_filter_config("filters.yaml")

# Or create programmatically
config = PreprocessingFilterConfig(name="custom", enabled=True)
config.intensity.min_intensity = 1000.0
config.peptide.allowed_charge_states = [2, 3, 4]
config.protein.min_unique_peptides = 2

# Apply filters
pipeline = get_filter_pipeline(config)
filtered_df, results = pipeline.apply(df)

# Check results
for result in results:
    print(f"{result.filter_name}: removed {result.removed_count} ({result.removal_rate:.1%})")

Pre-configured Templates

mokume includes templates for common scenarios in tests/example/filters/:

Configuration	Use Case	Description
basic_qc.yaml	General QC	Minimal filtering for standard experiments
stringent_filtering.yaml	Publication	High-confidence results with strict thresholds
tmt_labeling.yaml	TMT/iTRAQ	Optimized for multiplexed labeling
dia_analysis.yaml	DIA	Optimized for DIA-NN, Spectronaut
exploratory_analysis.yaml	Exploration	Minimal filtering for data exploration