mass-spectrometry-tune-data-loading
Summary
Load collision cross section (CCS) calibration tune reference data from HDF5 files using DEIMoS, preparing standardized tune compounds for downstream calibration workflows. This skill is essential for establishing the m/z–CCS relationship required for accurate ion mobility spectrometry analysis.
When to use
You have positive- or negative-mode tune reference compound data stored in HDF5 format (e.g., example_tune_pos.h5) and need to extract the tune mass spectrum for CCS calibration. This skill is the entry point before applying deimos.calibration.tunemix() to establish a calibration model with known m/z and CCS values.
When NOT to use
- Input data is already in processed feature table format (peaks detected and aligned); use deimos.load() only on raw or tune reference data.
- Data is stored in non-HDF5 formats (e.g., raw mzML, NetCDF); convert using ProteoWizard msconvert or appropriate format-specific loader first.
- You are loading sample data for feature detection rather than tune reference compounds; tune data serves a calibration-specific role and should not be used as analysis samples.
Inputs
- HDF5 file containing tune reference data (e.g., example_tune_pos.h5)
- key parameter specifying the dataset path within the HDF5 file (string, typically 'ms1')
Outputs
- Tune mass spectrum DataFrame with columns including m/z, intensity, and optional drift_time/retention_time dimensions
- Structured data object ready for calibration function input
How to apply
Use deimos.load() with the HDF5 tune data file path and specify the key parameter (typically 'ms1' for mass spectrometry data). The function returns a structured DataFrame containing m/z, intensity, and other spectral dimensions for the tune reference compounds. Extract the loaded tune data object for subsequent calibration. The loaded data should span the expected m/z range of the tune mix (e.g., m/z 118.086255–1521.971475 for positive-mode compounds) and contain intensity values for all reference compound peaks.
Related tools
- deimos (Provides the deimos.load() function to parse and deserialize HDF5 tune data into memory-efficient DataFrames) — https://github.com/pnnl/deimos
- Python (Runtime environment for executing deimos.load() and downstream calibration workflows)
- ProteoWizard (Used to convert tune data from other formats (e.g., native vendor formats) to mzML before HDF5 conversion if needed)
Examples
tune_pos = deimos.load('example_tune_pos.h5', key='ms1')
Evaluation signals
- Returned DataFrame contains expected columns: m/z, intensity, and (optionally) drift_time, retention_time, or other dimensions
- m/z values span the known range of tune reference compounds (e.g., 118.086–1521.971 for positive-mode mix)
- Intensity values are non-negative and non-zero for all reference compound peaks
- DataFrame can be passed without error to deimos.calibration.tunemix() for CCS calibration
- Row count and data shape are consistent with the number of scans/spectra in the tune file
Limitations
- HDF5 file must be properly formatted with the specified key present; missing or malformed keys will raise KeyError
- Loading large tune datasets may require significant memory; no streaming or chunked loading option is documented
- deimos.load() is largely agnostic to instrument type but relies on HDF5 data structure conforming to DEIMoS conventions
- Tune data quality depends on upstream data acquisition and conversion; corrupted or incomplete tune files will propagate errors to calibration
Evidence
- [results] tune_pos = deimos.load('example_tune_pos.h5', key='ms1'): "tune_pos = deimos.load('example_tune_pos.h5', key='ms1')"
- [other] Load the positive-mode tune data from example_tune_pos.h5 using deimos.load() with key 'ms1': "Load the positive-mode tune data from example_tune_pos.h5 using deimos.load() with key 'ms1'"
- [readme] DEIMoS, or Data Extraction for Integrated Multidimensional Spectrometry, is a Python application programming interface and command-line tool: "DEIMoS, or Data Extraction for Integrated Multidimensional Spectrometry, is a Python application programming interface and command-line tool"
- [other] m/z 118.086255–1521.971475 with known CCS values: "m/z 118.086255–1521.971475 with known CCS values"
- [results] Conversion to mzML from several other formats can be performed using the free and open-source ProteoWizard msconvert utility: "Conversion to mzML from several other formats can be performed using the free and open-source ProteoWizard msconvert utility"
1---2name: mass-spectrometry-tune-data-loading3description: Use when you have positive- or negative-mode tune reference compound data stored in HDF5 format (e.g., example_tune_pos.h5) and need to extract the tune mass spectrum for CCS calibration. This skill is the entry point before applying deimos.calibration.4license: CC-BY-4.05---67# mass-spectrometry-tune-data-loading89## Summary1011Load collision cross section (CCS) calibration tune reference data from HDF5 files using DEIMoS, preparing standardized tune compounds for downstream calibration workflows. This skill is essential for establishing the m/z–CCS relationship required for accurate ion mobility spectrometry analysis.1213## When to use1415You have positive- or negative-mode tune reference compound data stored in HDF5 format (e.g., example_tune_pos.h5) and need to extract the tune mass spectrum for CCS calibration. This skill is the entry point before applying deimos.calibration.tunemix() to establish a calibration model with known m/z and CCS values.1617## When NOT to use1819- Input data is already in processed feature table format (peaks detected and aligned); use deimos.load() only on raw or tune reference data.20- Data is stored in non-HDF5 formats (e.g., raw mzML, NetCDF); convert using ProteoWizard msconvert or appropriate format-specific loader first.21- You are loading sample data for feature detection rather than tune reference compounds; tune data serves a calibration-specific role and should not be used as analysis samples.2223## Inputs2425- HDF5 file containing tune reference data (e.g., example_tune_pos.h5)26- key parameter specifying the dataset path within the HDF5 file (string, typically 'ms1')2728## Outputs2930- Tune mass spectrum DataFrame with columns including m/z, intensity, and optional drift_time/retention_time dimensions31- Structured data object ready for calibration function input3233## How to apply3435Use deimos.load() with the HDF5 tune data file path and specify the key parameter (typically 'ms1' for mass spectrometry data). The function returns a structured DataFrame containing m/z, intensity, and other spectral dimensions for the tune reference compounds. Extract the loaded tune data object for subsequent calibration. The loaded data should span the expected m/z range of the tune mix (e.g., m/z 118.086255–1521.971475 for positive-mode compounds) and contain intensity values for all reference compound peaks.3637## Related tools3839- **deimos** (Provides the deimos.load() function to parse and deserialize HDF5 tune data into memory-efficient DataFrames) — https://github.com/pnnl/deimos40- **Python** (Runtime environment for executing deimos.load() and downstream calibration workflows)41- **ProteoWizard** (Used to convert tune data from other formats (e.g., native vendor formats) to mzML before HDF5 conversion if needed)4243## Examples4445```46tune_pos = deimos.load('example_tune_pos.h5', key='ms1')47```4849## Evaluation signals5051- Returned DataFrame contains expected columns: m/z, intensity, and (optionally) drift_time, retention_time, or other dimensions52- m/z values span the known range of tune reference compounds (e.g., 118.086–1521.971 for positive-mode mix)53- Intensity values are non-negative and non-zero for all reference compound peaks54- DataFrame can be passed without error to deimos.calibration.tunemix() for CCS calibration55- Row count and data shape are consistent with the number of scans/spectra in the tune file5657## Limitations5859- HDF5 file must be properly formatted with the specified key present; missing or malformed keys will raise KeyError60- Loading large tune datasets may require significant memory; no streaming or chunked loading option is documented61- deimos.load() is largely agnostic to instrument type but relies on HDF5 data structure conforming to DEIMoS conventions62- Tune data quality depends on upstream data acquisition and conversion; corrupted or incomplete tune files will propagate errors to calibration6364## Evidence6566- [results] tune_pos = deimos.load('example_tune_pos.h5', key='ms1'): "tune_pos = deimos.load('example_tune_pos.h5', key='ms1')"67- [other] Load the positive-mode tune data from example_tune_pos.h5 using deimos.load() with key 'ms1': "Load the positive-mode tune data from example_tune_pos.h5 using deimos.load() with key 'ms1'"68- [readme] DEIMoS, or Data Extraction for Integrated Multidimensional Spectrometry, is a Python application programming interface and command-line tool: "DEIMoS, or Data Extraction for Integrated Multidimensional Spectrometry, is a Python application programming interface and command-line tool"69- [other] m/z 118.086255–1521.971475 with known CCS values: "m/z 118.086255–1521.971475 with known CCS values"70- [results] Conversion to mzML from several other formats can be performed using the free and open-source ProteoWizard msconvert utility: "Conversion to mzML from several other formats can be performed using the free and open-source ProteoWizard msconvert utility"
HolobiomicsLab