Spectral Fingerprint Vectorization

spectral-fingerprint-vectorization

Summary

Generate numeric fingerprint vectors from MS2 spectra by counting occurrences of fragment peaks and neutral losses per sample, enabling retention time-agnostic comparison of metabolomics samples. This skill is essential when samples have poor feature overlap, strong retention time shifts, or were acquired on different LC-MS platforms.

When to use

Apply this skill when you have MS2 fragmentation spectra from multiple metabolomics samples and need to compare them in a retention time-agnostic manner, especially when samples are chemically diverse, acquired with different LC methods or mass spectrometer technologies (e.g., Q-ToF vs Orbitrap), or exhibit strong chromatographic shifts that prevent traditional feature alignment.

When NOT to use

• Input data is already a processed feature abundance table (e.g., from untargeted metabolomics peak picking); use fingerprinting only on raw or lightly processed spectra.
• Samples have been aligned by retention time and you need to preserve compound identity from external libraries; fingerprinting is agnostic to compound annotation.
• You require high-resolution mass accuracy matching; fingerprinting counts occurrence rather than scoring similarity, so it is robust to ppm-level shifts but does not exploit them.

Inputs

• MS2 spectra files in standard formats (mzML, mzXML, MGF, msp)
• Precursor m/z values and fragment m/z lists per spectrum
• Sample-level grouping metadata

Outputs

• Per-sample MS2 fingerprint vectors (numeric, normalized by sample size)
• Peak occurrence count matrix across samples
• Neutral loss occurrence count matrix across samples

How to apply

Load MS2 spectra files (mzML, mzXML, MGF, or msp formats) using matchms to parse fragmentation data and extract precursor m/z values for each spectrum. Iterate through all spectra within each sample and count occurrences of MS2 peaks (m/z values above the noise threshold) and calculate neutral losses by subtracting each fragment m/z from its precursor m/z. Aggregate peak and neutral loss occurrence counts across all spectra within each sample using spec2vec document conversion to handle spectral-to-vector transformation. Normalize counts by sample size to account for differences in spectral depth, producing a numeric fingerprint vector per sample that combines relative frequencies of peaks and neutral losses. The resulting fingerprint is dimensionless and comparable across samples regardless of acquisition parameters.

Related tools

• MEMO (Core method implementing MS2-based sample vectorization via peak and neutral loss counting and fingerprint generation) — https://github.com/mandelbrot-project/memo
• matchms (Parses MS2 spectra from standard file formats and extracts precursor/fragment m/z metadata) — https://github.com/matchms/matchms
• spec2vec (Converts aggregated spectral peaks and neutral losses into document-like vectors for fingerprint representation) — https://github.com/iomega/spec2vec
• Python 3.8+ (Execution environment for MEMO pipeline and numpy-based vector operations)
• numpy (Numeric array operations for peak/loss count aggregation and vector normalization)

Examples

from memo import MEMO
from matchms.importing_utils import load_from_msp
spectra = load_from_msp('samples.msp')
memo = MEMO()
fingerprints = memo.fit_transform(spectra)

Evaluation signals

• Fingerprint vector length equals the total number of unique MS2 peaks and neutral losses observed across the entire sample cohort.
• Sum of normalized fingerprint values per sample equals 1.0 (or close, depending on normalization scheme), confirming occurrence counts were normalized by sample size.
• Fingerprints from blank/negative control samples contain primarily low-abundance peaks; after blank filtering, comparison of sample fingerprints should show increased separation.
• Samples acquired on different LC-MS platforms or with strong retention time offsets produce fingerprints that cluster by chemistry, not by instrument or RT drift.
• Pairwise sample distance or similarity computed from fingerprints (e.g., via MDS/PCoA or TMAP visualization) reflects known chemical relationships or biological groupings in the sample set.

Limitations

• Fingerprinting discards temporal and structural information (retention time, exact m/z, peak intensity rank); it counts only presence/absence or frequency of fragment patterns, not their properties.
• Neutral loss calculation requires accurate precursor m/z assignment; errors in precursor selection propagate to incorrect neutral loss values.
• Samples with very different total spectral counts (e.g., one sample has 10× more MS2 scans than another) may be biased during normalization if sample size weighting is not carefully applied.
• The method is most effective for chemodiverse or poorly overlapping samples; samples with near-identical chemistry will produce nearly identical fingerprints, offering little discriminatory power.
• No inherent quality control on spectral noise; peaks below the noise threshold must be filtered prior to fingerprinting using external thresholds (e.g., relative intensity cutoffs).

Evidence

• [other] MS2 fingerprints are generated by counting the occurrence of MS2 peaks and neutral losses (relative to the precursor ion) in each sample, producing a numeric fingerprint representation.: "The occurence of MS2 peaks and neutral losses (to the precursor) in each sample is counted and used to generate an MS2 fingerprint of the sample"
• [other] Fingerprint generation workflow includes loading spectra, extracting precursor m/z, counting peaks and neutral losses, and aggregating via spec2vec document conversion.: "1. Load MS2 spectra files using matchms to parse fragmentation data. 2. Iterate through spectra in each sample and extract precursor m/z values. 3. Count occurrences of all MS2 peaks (m/z values"
• [readme] Fingerprints enable retention time-agnostic alignment of metabolomics samples.: "M\ s2 bas\ E\ d sa\ M\ ple vect\ O\ rization (MEMO) is a method allowing a Retention Time (RT) agnostic alignment of metabolomics samples using the fragmentation spectra (MS2) of"
• [readme] MEMO suits for comparing chemodiverse samples with poor feature overlap or strong retention time shift across different LC methods and instruments.: "MEMO suits particularly well to compare chemodiverse samples, ie with a poor features overlap, or to compare samples with a strong RT shift, acquired using different LC methods or even different mass"
• [other] Filtering and visualization of fingerprints can follow vectorization.: "different filtering (remove peaks/losses from blanks for example) and visualization techniques (MDS/PCoA, TMAP, Heatmap, ...) can be used"
• [other] matchms is the primary tool for parsing MS2 spectra and spec2vec is used for vectorization.: "MEMO is mainly built on matchms_ and spec2vec_ packages for handling the MS2 spectra"