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Thyra

Tests PyPI Python 3.12+ License: MIT Code style: black Docs Open in Colab

Thyra (from Greek thyra, meaning "door" or "portal") -- a modern Python library for converting Mass Spectrometry Imaging (MSI) data into the standardized SpatialData/Zarr format, serving as your portal to spatial omics analysis workflows.

Read the documentation | Getting Started | Tutorial | CLI Reference | API Reference

Try it without any data

pip install thyra
thyra-example-data example_data/synthetic_brain.imzML   # generates a small synthetic dataset
thyra example_data/synthetic_brain.imzML example_data/synthetic_brain.zarr

See the Tutorial for a full walkthrough, including the published example dataset (10.5281/zenodo.18326569).

Features

  • Multiple Input Formats: ImzML, Bruker (.d directories), Waters (.raw directories), PHI SmartSoft-TOF ToF-SIMS (.raw files)
  • SpatialData Output: Modern, cloud-ready format with Zarr backend
  • Memory Efficient: Handles large datasets (100+ GB) through streaming processing
  • Optical Alignment: Automatic MSI-to-optical image registration for Bruker data
  • Multi-Region Support: Handles slides with multiple tissue sections
  • Resampling: Physics-aware mass axis resampling (on by default in the CLI; opt-in from the Python API)
  • Ion Mobility and MS/MS: TIMS acquisitions keep their mass-mobility heatmap and can carry a mobility-resolved sibling table; scheduled PASEF MS/MS acquisitions are written split apart by precursor by default. The defaults and the measurements behind them are recorded in the design decisions
  • Validated Metadata: Versioned, ontology-mapped metadata schema (PSI-MS, NCBITaxon, UBERON, CHEBI) with thyra validate and one-command METASPACE export
  • 3D Support: Process volume data or treat as 2D slices
  • Cross-Platform: Windows, macOS, and Linux

Installation

pip install thyra

Quick Start

Command Line

# Basic conversion (resampling enabled by default)
thyra input.imzML output.zarr

# Bruker data with verbose logging
thyra data.d output.zarr -v DEBUG

# PHI SmartSoft-TOF ToF-SIMS (a .raw file, not a directory)
thyra tofsims_run.raw output.zarr

# Disable resampling
thyra input.imzML output.zarr --no-resample

Thyra auto-detects the input format. Note that .raw is claimed by two vendors and resolved by shape: Waters writes a directory, PHI writes a single file. See Supported Formats.

Python API

from thyra import convert_msi

success = convert_msi("data/sample.imzML", "output/sample.zarr")

Working with the Output

import spatialdata as sd

sdata = sd.read_zarr("output/sample.zarr")
msi_table = sdata.tables["msi_dataset_z0"]

print(f"Shape: {msi_table.shape}")  # (pixels, m/z bins)
print(f"m/z range: {msi_table.var['mz'].min():.1f} -- {msi_table.var['mz'].max():.1f}")

Metadata

Every converted store carries a versioned, ontology-mapped metadata block (uns["msi_metadata"]), auto-populated from the source file:

thyra validate output.zarr                               # schema + ontology checks
thyra export-metaspace output.zarr --merge sample.json   # METASPACE submission JSON

See Metadata Schema.

Documentation

Full documentation: M4i-Imaging-Mass-Spectrometry.github.io/thyra

Supported Formats

Input Extension Status
ImzML .imzML Full support
Bruker .d Full support (timsTOF + solariX + Rapiflex)
Waters .raw directory Full support
PHI SmartSoft-TOF .raw file Full support (nanoTOF, ToF-SIMS)
mzPeak .mzpeak Experimental (HUPO-PSI v0.9 draft, read-only)
Shimadzu .imdx, .kbd In development (workaround: imzML export from IMAGEREVEAL MS)

PHI mosaic, MS/MS and depth-profiling acquisitions are implemented but so far tested only against synthetic files -- real data in those modes is very welcome.

Output: SpatialData/Zarr -- cloud-ready, efficient, standardized

Development

git clone https://github.com/M4i-Imaging-Mass-Spectrometry/thyra.git
cd thyra
uv sync
uv run pre-commit install
uv run pytest

Contributing

See CONTRIBUTING.md for guidelines.

License

MIT -- see LICENSE.

Citation

@software{thyra2024,
  title = {Thyra: Modern Mass Spectrometry Imaging Data Conversion},
  author = {Visvikis, Theodoros},
  year = {2024},
  url = {https://github.com/M4i-Imaging-Mass-Spectrometry/thyra}
}

Acknowledgments

Visual identity

Logomark and logotype designed by Nepsis Scriptorium.

Instagram @nepsis.scriptorium Email nepsisscriptorium@gmail.com

Metadata

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