1. Importing and Exporting Mass Spectrometry Data
Load spectra from multiple file formats and export processed data:
```python
from matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json
from matchms.exporting import save_as_mgf, save_as_msp, save_as_json
# Import spectra
spectra = list(load_from_mgf("spectra.mgf"))
spectra = list(load_from_mzml("data.mzML"))
spectra = list(load_from_msp("library.msp"))
# Export processed spectra
save_as_mgf(spectra, "output.mgf")
save_as_json(spectra, "output.json")
```
Supported formats:
- mzML and mzXML (raw mass spectrometry formats)
- MGF (Mascot Generic Format)
- MSP (spectral library format)
- JSON (GNPS-compatible)
- metabolomics-USI references
- Pickle (Python serialization)
For detailed importing/exporting documentation, consult references/importing_exporting.md.
2. Spectrum Filtering and Processing
Apply comprehensive filters to standardize metadata and refine peak data:
```python
from matchms.filtering import default_filters, normalize_intensities
from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks
# Apply default metadata harmonization filters
spectrum = default_filters(spectrum)
# Normalize peak intensities
spectrum = normalize_intensities(spectrum)
# Filter peaks by relative intensity
spectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)
# Require minimum peaks
spectrum = require_minimum_number_of_peaks(spectrum, n_required=5)
```
Filter categories:
- Metadata processing: Harmonize compound names, derive chemical structures, standardize adducts, correct charges
- Peak filtering: Normalize intensities, select by m/z or intensity, remove precursor peaks
- Quality control: Require minimum peaks, validate precursor m/z, ensure metadata completeness
- Chemical annotation: Add fingerprints, derive InChI/SMILES, repair structural mismatches
Matchms provides 40+ filters. For the complete filter reference, consult references/filtering.md.
3. Calculating Spectral Similarities
Compare spectra using various similarity metrics:
```python
from matchms import calculate_scores
from matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian
# Calculate cosine similarity (fast, greedy algorithm)
scores = calculate_scores(references=library_spectra,
queries=query_spectra,
similarity_function=CosineGreedy())
# Calculate modified cosine (accounts for precursor m/z differences)
scores = calculate_scores(references=library_spectra,
queries=query_spectra,
similarity_function=ModifiedCosine(tolerance=0.1))
# Get best matches
best_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10]
```
Available similarity functions:
- CosineGreedy/CosineHungarian: Peak-based cosine similarity with different matching algorithms
- ModifiedCosine: Cosine similarity accounting for precursor mass differences
- NeutralLossesCosine: Similarity based on neutral loss patterns
- FingerprintSimilarity: Molecular structure similarity using fingerprints
- MetadataMatch: Compare user-defined metadata fields
- PrecursorMzMatch/ParentMassMatch: Simple mass-based filtering
For detailed similarity function documentation, consult references/similarity.md.
4. Building Processing Pipelines
Create reproducible, multi-step analysis workflows:
```python
from matchms import SpectrumProcessor
from matchms.filtering import default_filters, normalize_intensities
from matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz
# Define a processing pipeline
processor = SpectrumProcessor([
default_filters,
normalize_intensities,
lambda s: select_by_relative_intensity(s, intensity_from=0.01),
lambda s: remove_peaks_around_precursor_mz(s, mz_tolerance=17)
])
# Apply to all spectra
processed_spectra = [processor(s) for s in spectra]
```
5. Working with Spectrum Objects
The core Spectrum class contains mass spectral data:
```python
from matchms import Spectrum
import numpy as np
# Create a spectrum
mz = np.array([100.0, 150.0, 200.0, 250.0])
intensities = np.array([0.1, 0.5, 0.9, 0.3])
metadata = {"precursor_mz": 250.5, "ionmode": "positive"}
spectrum = Spectrum(mz=mz, intensities=intensities, metadata=metadata)
# Access spectrum properties
print(spectrum.peaks.mz) # m/z values
print(spectrum.peaks.intensities) # Intensity values
print(spectrum.get("precursor_mz")) # Metadata field
# Visualize spectra
spectrum.plot()
spectrum.plot_against(reference_spectrum)
```
6. Metadata Management
Standardize and harmonize spectrum metadata:
```python
# Metadata is automatically harmonized
spectrum.set("Precursor_mz", 250.5) # Gets harmonized to lowercase key
print(spectrum.get("precursor_mz")) # Returns 250.5
# Derive chemical information
from matchms.filtering import derive_inchi_from_smiles, derive_inchikey_from_inchi
from matchms.filtering import add_fingerprint
spectrum = derive_inchi_from_smiles(spectrum)
spectrum = derive_inchikey_from_inchi(spectrum)
spectrum = add_fingerprint(spectrum, fingerprint_type="morgan", nbits=2048)
```
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