proteomics-toolkit 26.6.0

Proteomics analysis toolkit for mass spectrometry data

Proteomics Analysis Toolkit

A Python toolkit for analyzing mass spectrometry-based proteomics data, supporting both Skyline CSV and PRISM parquet workflows.

Features

Core Analysis Modules

• data_import: Load Skyline CSV, PRISM parquet, or DIA-NN pg_matrix.tsv data, handle batch suffixes, manage sample metadata
• preprocessing: Protein identifier parsing, sample classification, data quality assessment
• normalization: Seven normalization methods (median, VSN, quantile, MAD, z-score, RLR, LOESS)
• statistical_analysis: Differential protein analysis — t-tests, Wilcoxon, Mann-Whitney, mixed-effects, moderated linear model with empirical-Bayes variance shrinkage (limma / DEqMS / intensity-trend)
• visualization: Publication-ready plots — volcano, PCA, box plots, heatmaps, correlation, trajectories, bi-clustered sample clustermaps, UMAP, PCA loadings
• enrichment: Gene set enrichment via Enrichr API
• temporal_clustering: K-means clustering of temporal protein trends
• classification: Binary classification with cross-validation, SHAP interpretability, multi-class permutation importance; recursive feature elimination with cross-validation, per-feature stability selection, and a permutation null (run_rfecv_stability)
• marker_discovery: Descriptive marker-discovery metrics and silhouette-driven k-means clustering for low-n designs
• multivariate: PERMANOVA variance partitioning on sample-by-sample distance matrices
• validation: Metadata/data consistency checking with diagnostic reports
• export: Standardized result export with timestamped configs

Installation

With uv (recommended)

uv is a fast Python package and project manager. Install it once with:

curl -LsSf https://astral.sh/uv/install.sh | sh

Then, from a clone of this repository:

git clone https://github.com/uw-maccosslab/proteomics-toolkit.git
cd proteomics-toolkit
uv sync                  # creates .venv and installs runtime deps from uv.lock
uv sync --extra dev      # also installs pytest + pytest-cov for running tests
uv sync --extra umap     # also installs umap-learn for plot_umap

# Run commands inside the managed venv
uv run pytest tests/ -v
uv run python -c "import proteomics_toolkit as ptk; print(ptk.__version__)"

Or add it to an existing uv-managed project:

uv add proteomics-toolkit
uv add 'proteomics-toolkit[umap]'   # with optional UMAP support

With pip

# Install from PyPI
pip install proteomics-toolkit

# With optional UMAP support (for plot_umap)
pip install proteomics-toolkit[umap]

# Install from GitHub (latest development version)
pip install git+https://github.com/uw-maccosslab/proteomics-toolkit.git

# For development (editable install from local clone)
git clone https://github.com/uw-maccosslab/proteomics-toolkit.git
cd proteomics-toolkit
pip install -e '.[dev]'

Quick Start

PRISM Workflow (recommended for batch-corrected data)

import proteomics_toolkit as ptk
import pandas as pd

# 1. Load PRISM data
protein_data, metadata, sample_cols = ptk.load_prism_data(
    'PRISM-Output/corrected_proteins.parquet',
    'PRISM-Output/sample_metadata.csv',
)

# 2. Map batch-suffixed column names to short replicate IDs
col_map = ptk.strip_batch_suffix(sample_cols)  # {full_col: short_name}
short_to_col = {v: k for k, v in col_map.items()}

# 3. Build sample metadata dict (keys = full PRISM column names)
meta_dict = {}
for _, row in metadata.iterrows():
    full_col = short_to_col.get(row['Replicate'])
    if full_col:
        meta_dict[full_col] = row.to_dict()

# 4. Build annotation + sample data for stats
annot = protein_data[[
    'leading_protein', 'leading_description', 'leading_gene_name',
    'leading_uniprot_id', 'leading_name'
]].copy()
annot.columns = ['Protein', 'Description', 'Protein Gene', 'UniProt_Accession', 'UniProt_Entry_Name']
data = pd.concat([annot.reset_index(drop=True),
                   protein_data[sample_cols].reset_index(drop=True)], axis=1)
data.index = data['Protein']  # accession as index

# 5. Statistical analysis
config = ptk.StatisticalConfig()
config.analysis_type = 'unpaired'
config.statistical_test_method = 'welch_t'
config.group_column = 'Group'
config.group_labels = ['Control', 'Treatment']  # [reference, study]
config.correction_method = 'fdr_bh'
config.p_value_threshold = 0.05
config.fold_change_threshold = 1.0
config.log_transform_before_stats = True
config.validate()

results = ptk.run_comprehensive_statistical_analysis(
    data, meta_dict, config, protein_annotations=annot
)

# 6. Visualization
ptk.plot_volcano(results, fc_threshold=1.0, gene_column='Protein Gene', label_top_n=15)
ptk.display_analysis_summary(results, config)

# 7. Enrichment
enrich_config = ptk.EnrichmentConfig(
    enrichr_libraries=['GO_Biological_Process_2023', 'KEGG_2021_Human'],
    pvalue_cutoff=0.05,
)
enrich = ptk.run_differential_enrichment(
    results, gene_column='Protein Gene', logfc_column='logFC',
    pvalue_column='adj.P.Val', config=enrich_config,
)

Skyline CSV Workflow

# 1. Load data
protein_data, metadata, peptide_data = ptk.load_skyline_data(
    protein_file='protein_quant.csv',
    metadata_file='metadata.csv',
)

# 2. Process sample names
sample_columns = ptk.data_import.identify_sample_columns(protein_data, metadata)
cleaned_names = ptk.clean_sample_names(sample_columns)

# 3. Parse annotations and filter
processed_data = ptk.parse_protein_identifiers(protein_data)

# 4. Normalize (skip for PRISM — already normalized)
normalized = ptk.median_normalize(processed_data, sample_columns=list(cleaned_names.values()))

# 5. QC plots
ptk.plot_box_plot(normalized, list(cleaned_names.values()), sample_metadata)
ptk.plot_pca(normalized, list(cleaned_names.values()), sample_metadata)

Statistical Analysis

All statistical analyses use StatisticalConfig + run_comprehensive_statistical_analysis().

Unpaired comparison (two independent groups)

config = ptk.StatisticalConfig()
config.analysis_type = 'unpaired'
# Options: 'welch_t', 'mann_whitney', 'moderated_linear_model'
config.statistical_test_method = 'welch_t'
config.group_column = 'Group'
config.group_labels = ['Control', 'Treatment']
config.log_transform_before_stats = 'auto'
config.validate()

results = ptk.run_comprehensive_statistical_analysis(
    data, sample_metadata, config, protein_annotations=annot
)

For small sample sizes, prefer empirical Bayes variance shrinkage: set config.statistical_test_method = 'moderated_linear_model' and choose a config.moderation prior — 'intensity_trend' (default; works on proteins or peptides), 'limma' (global prior), or 'deqms' (protein-level only; uses the n_peptides column from PRISM output to build a peptide-count-conditioned variance prior). See docs/06-statistical-analysis.md for details.

Paired comparison (before/after per subject)

config = ptk.StatisticalConfig()
config.analysis_type = 'paired'
config.statistical_test_method = 'paired_t'
config.subject_column = 'Subject'
config.paired_column = 'Condition'
config.paired_label1 = 'Before'
config.paired_label2 = 'After'
config.group_column = 'Condition'
config.group_labels = ['Before', 'After']
config.validate()

Mixed-effects model (repeated measures)

config = ptk.StatisticalConfig()
config.analysis_type = 'paired'
config.statistical_test_method = 'mixed_effects'
config.subject_column = 'Subject'
config.paired_column = 'Visit'
config.paired_label1 = 'Baseline'
config.paired_label2 = 'Follow-up'
config.group_column = 'Treatment'
config.group_labels = ['Placebo', 'Drug']
config.interaction_terms = ['Treatment', 'Visit']
config.validate()

Output columns: Protein, logFC, P.Value, adj.P.Val, AveExpr, t, Protein Gene, Description, UniProt_Accession, Gene

Enrichment

Enrichment results use these column names (not the Enrichr web-UI names):

Column	Description
Term	Pathway / GO term name
P_Value	Unadjusted p-value
Adj_P_Value	BH-adjusted p-value
Z_Score	Enrichr z-score
Combined_Score	log(p) × z — used for ranking
Genes	Semicolon-separated gene list
N_Genes	Number of overlapping genes
Library	Source Enrichr library

Dependencies

• pandas >= 1.3.0
• numpy >= 1.21.0
• scipy >= 1.7.0
• matplotlib >= 3.4.0
• seaborn >= 0.11.0
• scikit-learn >= 1.0.0
• statsmodels >= 0.12.0
• requests >= 2.25.0 (for Enrichr API)
• pyarrow >= 8.0.0 (for PRISM parquet files)

Module Reference

data_import.py

• load_skyline_data() — Load Skyline protein/peptide CSVs + metadata
• load_prism_data() — Load PRISM protein parquet + metadata
• load_prism_peptide_data() — Load PRISM peptide parquet
• load_diann_data() — Load DIA-NN report.pg_matrix.tsv protein-group matrix
• load_fasta_sequences() — Parse a FASTA file into {accession -> sequence}
• identify_sample_columns() — Auto-detect sample columns
• clean_sample_names() — Remove common prefixes/suffixes
• detect_batch_suffix() — Detect PRISM __@__ batch suffix
• strip_batch_suffix() — Map batch-suffixed names → short names
• create_sample_column_mapping() — Map data columns to metadata sample names
• match_samples_to_metadata() — Link samples to metadata rows
• BATCH_SUFFIX_DELIMITER — Constant: "__@__"

preprocessing.py

• parse_protein_identifiers() — Extract UniProt accessions and databases
• parse_gene_and_description() — Parse gene names from descriptions
• classify_samples() — Classify samples into groups / controls with color assignment
• apply_systematic_color_scheme() — Generate consistent group colors
• create_standard_data_structure() — Build standard 5-column annotation + sample layout
• assess_data_completeness() — Evaluate missing data patterns
• filter_proteins_by_completeness() — Remove proteins below detection threshold
• calculate_group_colors() — Generate group color mapping
• identify_annotation_columns() — Auto-detect annotation vs sample columns

normalization.py

• median_normalize() — Median-based normalization (preserves original scale)
• vsn_normalize() — Variance Stabilizing Normalization (arcsinh-transformed)
• quantile_normalize() — Force identical distributions
• mad_normalize() — Median absolute deviation normalization
• z_score_normalize() — Standardize to mean=0, sd=1
• rlr_normalize() — Robust linear regression (log2-transformed)
• loess_normalize() — LOESS intensity-dependent (log2-transformed)
• handle_negative_values() — Handle negative values from VSN
• analyze_negative_values() — Analyze negative value patterns
• calculate_normalization_stats() — Evaluate normalization effectiveness

statistical_analysis.py

• StatisticalConfig — Configuration class (zero-arg constructor, set attributes individually)
• run_comprehensive_statistical_analysis() — Main analysis entry point (dispatches by statistical_test_method)
• run_moderated_linear_model() — Per-feature linear model with empirical-Bayes variance moderation (moderation='intensity_trend' / 'limma' / 'deqms'); supports covariate adjustment for analysis_type='unpaired'
• get_intensity_trend_points() — Recover the per-(feature, group) diagnostic points DataFrame from a moderated intensity_trend results object
• compute_paired_fold_changes() — Build a per-subject fold-change matrix for paired designs (used as input to the classification module)
• display_analysis_summary() — Print/return summary of results
• run_statistical_analysis() — Backward-compatible wrapper

visualization.py

• plot_box_plot() — Sample intensity distributions by group
• plot_volcano() — Volcano plot with labeled top hits
• plot_pca() — PCA with group coloring, optional log-transform
• plot_pca_loadings() — PCA loadings biplot with top-N protein labels
• plot_umap() — UMAP projection of samples colored by metadata group (requires [umap] extra)
• plot_comparative_pca() — Compare PCA across normalization methods
• plot_normalization_comparison() — Before/after normalization QC
• plot_missing_value_heatmap() — Missing-value pattern across samples x features
• plot_identifications_per_sample() — Bar plot of #features identified per sample
• plot_intensity_distributions() — Density overlay of intensity per sample
• plot_cv_distribution() — CV distribution across all samples (or by group)
• plot_sample_correlation_heatmap() — Full correlation matrix
• plot_sample_correlation_triangular_heatmap() — Lower-triangle correlation
• plot_control_correlation() — Control sample correlation with optional clustering
• plot_control_correlation_analysis() — Multi-panel control QC
• plot_control_group_correlation_analysis() — Group-wise control QC
• plot_individual_control_pool_analysis() — Individual control analysis
• plot_control_cv_distribution() — CV distribution for control samples
• plot_grouped_heatmap() — Heatmap for any grouped data
• plot_sample_clustermap() — Bi-clustered heatmap of samples x features with optional group color bar
• plot_grouped_trajectories() — Line plots for temporal/dose-response data
• plot_protein_profile() — Single protein expression profile
• plot_peptide_coverage_map() — Peptide positions along a protein sequence with optional coloring by abundance / fold-change
• plot_variance_vs_intensity() — Diagnostic for the intensity_trend moderation prior
• plot_variance_vs_peptide_count() — Diagnostic for the DEqMS moderation prior

enrichment.py

• EnrichmentConfig — Configuration dataclass (libraries, thresholds, API settings)
• query_enrichr() — Query Enrichr API with a gene list
• parse_enrichr_results() — Parse raw results into a tidy DataFrame
• run_enrichment_analysis() — Complete enrichment on a gene list
• run_enrichment_by_group() — Enrichment for each group in a DataFrame
• run_differential_enrichment() — Split by up/down-regulated, run enrichment on each
• plot_enrichment_barplot() — Horizontal bar plot by Combined Score
• plot_enrichment_comparison() — Dot plot comparing enrichment across groups
• get_available_libraries() — List common Enrichr libraries
• merge_enrichment_results() — Merge multiple enrichment DataFrames

temporal_clustering.py

• TemporalClusteringConfig — Configuration dataclass
• run_temporal_analysis() — Complete pipeline: clustering → visualization → enrichment
• calculate_temporal_means() — Mean abundance per timepoint across subjects
• cluster_temporal_trends() — K-means or hierarchical clustering
• name_clusters_by_pattern() — Assign descriptive cluster names
• classify_trend_pattern() — Classify individual protein trends
• merge_with_statistics() — Merge temporal data with statistical results
• filter_significant_proteins() — Filter to significant proteins
• run_enrichment_by_cluster() — Enrichment per cluster
• plot_cluster_heatmap() — Cluster-organized heatmap
• plot_cluster_parallel_coordinates() — Parallel coordinate plots

classification.py

• run_binary_classification() — Binary classification with feature selection and cross-validation; returns CV metrics, ROC data, and (with return_model=True) the fitted pipeline
• run_rfecv_stability() — Recursive feature elimination with cross-validation under an honest outer CV; returns held-out AUC, per-feature selection frequency, consensus signature, and a permutation null
• plot_selection_frequency() — Lollipop plot of the most stable features from run_rfecv_stability
• select_features_by_mad() — Unsupervised feature ranking by MAD across subjects (the leakage-free default selector)
• compute_shap_values() — shap.TreeExplainer wrapper for RandomForest / XGBoost binary classifiers; collapses to the 2-D positive-class slice (requires [shap] extra)
• plot_shap_summary() — SHAP beeswarm or bar summary for the top features (requires [shap] extra)
• relabel_features_with_genes() — Map pipeline-internal feature IDs (e.g. PRISM PG####) to gene symbols for plot labels and importance tables, with fallback to the original ID
• multiclass_feature_importance() — Multi-class RF / XGBoost permutation importance with bootstrap stability scores, for descriptive marker discovery in low-replication designs
• plot_fold_change_pca() — PCA of per-subject fold-changes by group
• plot_roc_curve() — ROC curve from a single classification result (with per-fold mean +/- SD band)
• plot_roc_comparison() — Overlay ROC curves from multiple methods

marker_discovery.py

• method_specificity_score() — Per-(protein, group) descriptive marker score: group mean, distance from the second-best group (delta_top), specificity vs across-group median, and rank
• inter_vs_intra_group_variance() — Per-protein ratio of variance across group means to mean within-group variance; descriptive complement to ANOVA when n per group is too small
• cluster_proteins_kmeans() — K-means clustering of proteins over samples with silhouette-driven k selection; supports per-protein z-score for shape-based clustering

multivariate.py

• permanova() — Anderson 2001 PERMANOVA on a sample-by-sample distance matrix with label permutation for significance; supports euclidean, braycurtis, cosine, correlation, and cityblock metrics

validation.py

• validate_metadata_data_consistency() — Check metadata matches data columns
• enhanced_sample_processing() — Sample processing with validation
• generate_sample_matching_diagnostic_report() — Detailed mismatch diagnostics
• SampleMatchingError — Exception for sample matching failures
• ControlSampleError — Exception for control sample configuration issues

export.py

• export_complete_analysis() — Full export: data + config + results
• export_analysis_results() — Export normalized data + differential results
• export_timestamped_config() — Save analysis config with timestamp
• create_config_dict_from_notebook_vars() — Build config dict from notebook variables
• export_significant_proteins_summary() — Export significant results summary
• export_results() — General-purpose result export

See Also

• User Guide -- Index of topic-focused recipe pages under docs/
• Tutorial notebook -- End-to-end workflow on the bundled example dataset
• CLAUDE.md — Project conventions and data prep patterns