spectral-data-table-conversion
Summary
Convert mass spectrometry spectrum data from mzPeak file format into structured tabular representations (pandas DataFrame, pyarrow Table, or Parquet/CSV exports) compatible with PyData analysis workflows. This skill enables downstream statistical, machine learning, and exploratory analyses on spectrum metadata and signal arrays.
When to use
When you have mzPeak format spectrum files and need to work with spectrum metadata, intensity/m/z arrays, or precursor information in a tabular, columnar, or vectorized computing environment. Use this skill when your analysis pipeline requires pandas, polars, DuckDB, or other PyData-stack tools that expect DataFrames or Arrow tables rather than binary spectrum archives.
When NOT to use
- Input is raw vendor data (mzML, .raw, .d); use vendor-specific readers or mzPeak conversion tools first.
- You need to write or modify mzPeak files; the Python implementation is read-only. Use the Rust implementation for write support.
- Your analysis requires access to chromatogram data; use chromatograms_metadata.parquet and chromatograms_data.parquet tables instead (same workflow applies).
Inputs
- mzPeak file (uncompressed ZIP archive containing mzpeak_index.json and Parquet files)
- Parquet file paths from within mzPeak archive (spectra_metadata.parquet, spectra_data.parquet, spectra_peaks.parquet)
- Spectrum index or spectrum ID selection (optional filter)
Outputs
- pandas.DataFrame (in-memory tabular representation of spectrum metadata and/or signal data)
- pyarrow.Table (columnar Arrow table, supports lazy evaluation)
- Parquet file (compressed columnar archive of converted spectrum data)
- CSV file (human-readable flat table export)
How to apply
Import the Python mzPeak reader module and pyarrow library from the mobiusklein/mzpeak_prototyping repository's python/ subdirectory. Load the mzPeak archive (an uncompressed ZIP containing Parquet files) using the Python read implementation, which automatically parses the mzpeak_index.json manifest and loads the relevant Parquet tables (spectra_metadata.parquet, spectra_data.parquet, and optional spectra_peaks.parquet). Convert the loaded spectrum data into either a pandas DataFrame for in-memory analysis or a pyarrow Table for lazy/streaming evaluation. Export the result to Parquet for efficient columnar storage or CSV for human inspection and tool interoperability. Key decision: if your workflow requires random-access reads of individual spectra, prefer the point layout representation; if you need compression or streaming, use chunked layout (which may include Numpress compression and requires decoding).
Related tools
- pyarrow (Read mzPeak Parquet files and provide columnar data access; enables conversion to pandas DataFrames and Arrow tables.) — https://arrow.apache.org/docs/python/index.html
- pandas (Convert pyarrow tables into in-memory DataFrames for statistical analysis, filtering, and aggregation.)
- Python (mzPeak reader module) (Parse mzPeak archive structure, load Parquet manifests, and handle zero-run stripping and null-marking reconstruction.) — https://github.com/mobiusklein/mzpeak_prototyping
- Rust (mzPeak CLI tools) (Pre-convert vendor formats (mzML, Thermo, Sciex) into mzPeak archives before Python ingestion.) — https://github.com/HUPO-PSI/mzPeak
Examples
from python.mzpeak import MzPeakReader; import pandas as pd; reader = MzPeakReader('sample.mzpeak'); spectra_meta = reader.spectra_metadata(); spectra_data = reader.spectra_data(); df = pd.concat([spectra_meta, spectra_data.to_pandas()], axis=1); df.to_parquet('converted_spectra.parquet')
Evaluation signals
- Parquet schema validation: verify spectra_metadata.parquet contains expected columns (spectrum_index, precursor, selected_ion groups) and spectra_data.parquet contains mz and intensity arrays with matching dimensions.
- Data shape consistency: row counts in spectra_metadata.parquet match the number of unique spectrum_index values in spectra_data.parquet; no orphaned indices.
- Null reconstruction accuracy: for null-marked m/z values (where the validity buffer indicates missing data), verify reconstructed m/z spacing using the learned polynomial model stays within ±1 ppm of true centroid positions.
- Zero-run stripping preservation: profile data should have removed interior zero-intensity runs while retaining at least the first and last zero point in each gap region.
- Export format completeness: exported Parquet or CSV files contain all spectrum_index values from the original archive with no row loss or duplication.
Limitations
- Python implementation is read-only; cannot write or modify mzPeak files. Users requiring write capability must use the Rust implementation.
- Null reconstruction accuracy depends on fitting a weighted least-squares polynomial model (δmz ~ β₀ + β₁·mz + β₂·mz²) to non-null points; sparse spectra with < 5 data points may yield unreliable spacing estimates.
- Project is work-in-progress with no stability guarantee; API and file format may change between releases.
- Zero-run stripping and null-marking are lossless only for profile data; centroid or already-processed data may not benefit from these compression techniques.
Evidence
- [intro] The Python implementation provides a complete re-implementation for reading mzPeak files using the pyarrow library, enabling conversion of mzPeak spectrum data into structured tabular artifacts compatible with the PyData stack.: "complete re-implementation for reading mzPeak files using [
pyarrow]"
- [intro] Workflow steps documented in task_id=task_002 on reading and exporting spectrum data.: "1. Clone or access the mobiusklein/mzpeak_prototyping repository and navigate to the python/ subdirectory. 2. Import the Python mzPeak reader module and pyarrow library. 3. Load the mzPeak file using"
- [readme] mzPeak archive structure and Parquet file organization.: "mzPeak is a archive of multiple Parquet files, stored directly in an uncompressed ZIP archive. Each Parquet file describes a different facet of the stored mass spectrometry run."
- [readme] Null-marking reconstruction method for sparse profile data.: "we can instead replace the flanking zero intensity points with
null m/z and intensity values and Parquet will skip storing the expensive 32- and/or 64-bit values, retaining only the validity buffer"
- [readme] Python implementation read-only limitation.: "The Python codebase does not support writing at this time although this is subject to change in the future."
- [readme] mzPeak components and metadata structure.: "
spectra_metadata.parquet: Spectrum level metadata and file-level metadata. Includes spectrum descriptions, scans, precursors, and selected ions using packed parallel tables."
1---2name: spectral-data-table-conversion-23description: Use when when you have mzPeak format spectrum files and need to work with spectrum metadata, intensity/m/z arrays, or precursor information in a tabular, columnar, or vectorized computing environment.4license: CC-BY-4.05---67# spectral-data-table-conversion89## Summary1011Convert mass spectrometry spectrum data from mzPeak file format into structured tabular representations (pandas DataFrame, pyarrow Table, or Parquet/CSV exports) compatible with PyData analysis workflows. This skill enables downstream statistical, machine learning, and exploratory analyses on spectrum metadata and signal arrays.1213## When to use1415When you have mzPeak format spectrum files and need to work with spectrum metadata, intensity/m/z arrays, or precursor information in a tabular, columnar, or vectorized computing environment. Use this skill when your analysis pipeline requires pandas, polars, DuckDB, or other PyData-stack tools that expect DataFrames or Arrow tables rather than binary spectrum archives.1617## When NOT to use1819- Input is raw vendor data (mzML, .raw, .d); use vendor-specific readers or mzPeak conversion tools first.20- You need to write or modify mzPeak files; the Python implementation is read-only. Use the Rust implementation for write support.21- Your analysis requires access to chromatogram data; use chromatograms_metadata.parquet and chromatograms_data.parquet tables instead (same workflow applies).2223## Inputs2425- mzPeak file (uncompressed ZIP archive containing mzpeak_index.json and Parquet files)26- Parquet file paths from within mzPeak archive (spectra_metadata.parquet, spectra_data.parquet, spectra_peaks.parquet)27- Spectrum index or spectrum ID selection (optional filter)2829## Outputs3031- pandas.DataFrame (in-memory tabular representation of spectrum metadata and/or signal data)32- pyarrow.Table (columnar Arrow table, supports lazy evaluation)33- Parquet file (compressed columnar archive of converted spectrum data)34- CSV file (human-readable flat table export)3536## How to apply3738Import the Python mzPeak reader module and pyarrow library from the mobiusklein/mzpeak_prototyping repository's `python/` subdirectory. Load the mzPeak archive (an uncompressed ZIP containing Parquet files) using the Python read implementation, which automatically parses the mzpeak_index.json manifest and loads the relevant Parquet tables (spectra_metadata.parquet, spectra_data.parquet, and optional spectra_peaks.parquet). Convert the loaded spectrum data into either a pandas DataFrame for in-memory analysis or a pyarrow Table for lazy/streaming evaluation. Export the result to Parquet for efficient columnar storage or CSV for human inspection and tool interoperability. Key decision: if your workflow requires random-access reads of individual spectra, prefer the point layout representation; if you need compression or streaming, use chunked layout (which may include Numpress compression and requires decoding).3940## Related tools4142- **pyarrow** (Read mzPeak Parquet files and provide columnar data access; enables conversion to pandas DataFrames and Arrow tables.) — https://arrow.apache.org/docs/python/index.html43- **pandas** (Convert pyarrow tables into in-memory DataFrames for statistical analysis, filtering, and aggregation.)44- **Python (mzPeak reader module)** (Parse mzPeak archive structure, load Parquet manifests, and handle zero-run stripping and null-marking reconstruction.) — https://github.com/mobiusklein/mzpeak_prototyping45- **Rust (mzPeak CLI tools)** (Pre-convert vendor formats (mzML, Thermo, Sciex) into mzPeak archives before Python ingestion.) — https://github.com/HUPO-PSI/mzPeak4647## Examples4849```50from python.mzpeak import MzPeakReader; import pandas as pd; reader = MzPeakReader('sample.mzpeak'); spectra_meta = reader.spectra_metadata(); spectra_data = reader.spectra_data(); df = pd.concat([spectra_meta, spectra_data.to_pandas()], axis=1); df.to_parquet('converted_spectra.parquet')51```5253## Evaluation signals5455- Parquet schema validation: verify spectra_metadata.parquet contains expected columns (spectrum_index, precursor, selected_ion groups) and spectra_data.parquet contains mz and intensity arrays with matching dimensions.56- Data shape consistency: row counts in spectra_metadata.parquet match the number of unique spectrum_index values in spectra_data.parquet; no orphaned indices.57- Null reconstruction accuracy: for null-marked m/z values (where the validity buffer indicates missing data), verify reconstructed m/z spacing using the learned polynomial model stays within ±1 ppm of true centroid positions.58- Zero-run stripping preservation: profile data should have removed interior zero-intensity runs while retaining at least the first and last zero point in each gap region.59- Export format completeness: exported Parquet or CSV files contain all spectrum_index values from the original archive with no row loss or duplication.6061## Limitations6263- Python implementation is read-only; cannot write or modify mzPeak files. Users requiring write capability must use the Rust implementation.64- Null reconstruction accuracy depends on fitting a weighted least-squares polynomial model (δmz ~ β₀ + β₁·mz + β₂·mz²) to non-null points; sparse spectra with < 5 data points may yield unreliable spacing estimates.65- Project is work-in-progress with no stability guarantee; API and file format may change between releases.66- Zero-run stripping and null-marking are lossless only for profile data; centroid or already-processed data may not benefit from these compression techniques.6768## Evidence6970- [intro] The Python implementation provides a complete re-implementation for reading mzPeak files using the pyarrow library, enabling conversion of mzPeak spectrum data into structured tabular artifacts compatible with the PyData stack.: "complete re-implementation for _reading_ mzPeak files using [`pyarrow`]"71- [intro] Workflow steps documented in task_id=task_002 on reading and exporting spectrum data.: "1. Clone or access the mobiusklein/mzpeak_prototyping repository and navigate to the python/ subdirectory. 2. Import the Python mzPeak reader module and pyarrow library. 3. Load the mzPeak file using"72- [readme] mzPeak archive structure and Parquet file organization.: "mzPeak is a archive of multiple Parquet files, stored directly in an _uncompressed_ ZIP archive. Each Parquet file describes a different facet of the stored mass spectrometry run."73- [readme] Null-marking reconstruction method for sparse profile data.: "we can instead replace the flanking zero intensity points with `null` m/z and intensity values and Parquet will skip storing the expensive 32- and/or 64-bit values, retaining only the validity buffer"74- [readme] Python implementation read-only limitation.: "The Python codebase does not support writing at this time although this is subject to change in the future."75- [readme] mzPeak components and metadata structure.: "`spectra_metadata.parquet`: Spectrum level metadata and file-level metadata. Includes spectrum descriptions, scans, precursors, and selected ions using packed parallel tables."
HolobiomicsLab 