This release is a pre-release and may not be stable for production use.
tadamz
tadamz is a specialized Python package for targeted LC-MS(/MS) (Liquid Chromatography-Mass Spectrometry) data analysis, built on top of the emzed3 mass spectrometry framework.
Developed at ETH Zurich (Institute of Microbiology), tadamz provides a robust, modular, and highly configurable pipeline to extract, classify, normalize, calibrate, and quantify targeted compounds from MS1, MS/MS, or SRM chromatograms.
Key Features
- 📊 Peak Extraction & Integration: High-performance chromatography integration, baseline subtraction (via
pybaselines), and extraction of chromatogram peaks based on targeted precursor $m/z$ and retention times. - 🤖 Peak Classification: Integrates a Random Forest classifier (
scikit-learn) to score peak quality (peak_quality_score), filtering out noise or false positives based on peak metrics (symmetry, FWHM, peak-to-noise ratio, etc.). - 🔄 RT Adaptation & Co-elution Alignment: Corrects and adapts target retention times dynamically across batches and alignments using reference co-eluting compound peaks.
- ⚖️ Calibration & Absolute Quantification: Fits weighted (e.g., $1/x$, $1/x^2$, $1/s^2$) linear or quadratic calibration models to calibrant tables to achieve accurate absolute concentration calculations.
- 📉 Normalization Options: Includes sample-wise normalization, standard-free normalization, and natural abundance/isotopologue overlay correction for stable isotope labeling experiments.
- 📝 Automated Report Generation: Automatically generates comprehensive PDF reports featuring global abundance heatmaps and compound-wise acquisition-order trends (for retention times, FWHM, and abundance).
- 💻 Interactive emzed GUI Integration: Built-in tools for interactive table exploration, manual peak scoring, and data visualization via
emzed-spyder.
📦 Installation
tadamz requires Python >= 3.11 and the core emzed framework.
You can install tadamz directly via pip from the repository:
pip install git+https://gitlab.com/emzed3_extensions/targeted.git
This will automatically install dependencies, including:
emzed >= 3.0.2pyyaml,uncertainties,pybaselines == 1.1.0numpy >= 2,seaborn,sklearn-migrator >= 0.22.1
🛠️ Getting Started
1. Configuration (YAML)
tadamz workflows are driven by configuration dictionaries, typically stored as YAML files:
extract_peaks:
integration_algorithm: linear
ms_data_type: MS_Chromatogram
mz_tol_abs: 0.3
peak_search_window_size: 60
subtract_baseline: true
classify_peaks:
scoring_model: random_forest_classification
scoring_model_params:
classifier_name: srm_peak_classifier
normalize_peaks:
sample_wise: true
correct_int_std_for_nat_abundance: false
processing_steps:
- extract_peaks
- classify_peaks
postprocessings:
- postprocessing1
- postprocessing2
postprocessing1:
- classify_peaks
- coeluting_peaks
- normalize_peaks
2. Workflow Execution
Load targets, samples, and config, and execute the pipeline:
from tadamz import run_workflow, postprocess_result_table, load_config
from tadamz.in_out import load_targets_table, load_samples_from_folder
# Load inputs
config = load_config("path/to/config.yaml")
targets_table = load_targets_table("path/to/targets_table.xlsx")
samples = load_samples_from_folder("path/to/raw_data_folder")
# Run main workflow
result = run_workflow(targets_table, samples, config)
# Post-process (e.g., RT coelution alignment & peak normalization)
result = postprocess_result_table(result, config, postprocess_id=0)
🤖 Training a custom Peak Classifier
Train custom peak classifiers for your targeted assays using manually scored peaks:
from tadamz import generate_peak_classifier
generate_peak_classifier(
classifier_name="my_custom_srm_classifier",
path_to_folder="path/to/save/classifier",
path_to_table="path/to/measured_peaks.table",
ms_data_type="MS_Chromatogram",
inspect=True, # Opens interactive GUI for manual scoring
)
📊 Generating Reports
tadamz can create comprehensive PDF reports of your targeted analysis:
from tadamz.generate_report import generate_report
# Generate global, compound-wise, and calibration report figures. Calibration
# plots are derived from the calibration_model column in result_table.
global_plots, compound_plots, calibration_plots = generate_report(
result_table,
pdf_folder="path/to/output_folder",
# Optional; omit when no calibration was performed. Calibration plots use
# column names such as calibrate.value_col from the full configuration.
config=config,
)
# Calibration plots are grouped by compound, then plot name.
curve = calibration_plots["compound_name"]["calibration_curves"]
📂 Project Structure
tadamz/
├── src/
│ └── tadamz/ # Main package source code
│ ├── calibration/ # Regression models & absolute calibration curve fitting
│ ├── data/ # Default configuration files & sample datasets
│ ├── scoring/ # Peak quality metrics & Random Forest classifiers
│ ├── in_out.py # IO helpers for tables, YAML, JSON, and PKL files
│ ├── workflow.py # Main workflow definition and runner functions
│ └── generate_report.py # PDF report generator using matplotlib and seaborn
├── tests/ # Unit tests & regression tests (run via pytest)
├── setup.cfg # Metadata, classifiers, and project dependencies
└── pyproject.toml # Build system configuration
📄 License & Credits
- Author: Patrick Kiefer (pkiefer@ethz.ch)
- Organization: Institute of Microbiology, ETH Zurich
- License: MIT License
1 maintainer
Metadata
1 maintainer
Release files for tadamz 1.0.0a24
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