Peak And Neutral Loss Counting

peak-and-neutral-loss-counting

Summary

Counts occurrences of MS2 peaks and neutral losses (to precursor) within individual samples to generate sample-level MS2 fingerprints. This quantitative summarization enables retention-time-agnostic sample comparison and alignment in metabolomics workflows.

When to use

When you have matchms-processed MS2 spectra from multiple samples and need to create comparable sample-level signatures for cross-sample analysis, particularly when samples are chemodiverse, have poor feature overlap, or exhibit strong retention time shifts across LC methods or MS technologies.

When NOT to use

• Input spectra have not been preprocessed through matchms (data quality and metadata standardization cannot be assumed).
• Retention-time information is critical for your analysis; MEMO is explicitly RT-agnostic and discards RT.
• You require feature-by-sample matrices with exact m/z alignment; MEMO fingerprints are sample-level aggregate counts, not individual spectral features.

Inputs

• Per-sample MS2 spectra (matchms Spectrum objects)
• Precursor m/z values for each spectrum
• spec2vec document representations

Outputs

• Per-sample MS2 fingerprint vectors (occurrence counts)
• MemoMatrix: unified fingerprint alignment matrix (numpy array)

How to apply

Load per-sample MS2 spectra processed through matchms and extract spec2vec document representations. For each sample, systematically count the frequency of each unique MS2 fragment peak m/z value and neutral loss mass (calculated relative to precursor m/z). Aggregate these counts into a fingerprint vector where each element represents the occurrence frequency of a specific m/z or loss. The counting is performed independently per sample before aggregation; no cross-sample normalization occurs at this stage. Fingerprints are then assembled into a unified matrix via numpy array operations to enable downstream alignment and filtering.

Related tools

• matchms (Processes and standardizes MS2 spectra; provides metadata cleaning and peak filtering for input to fingerprinting) — https://github.com/matchms/matchms
• spec2vec (Generates spectral embeddings and document representations from MS2 fragmentation relationships for fingerprint construction) — https://github.com/iomega/spec2vec
• numpy (Aggregates per-sample fingerprint vectors into unified MemoMatrix alignment arrays)
• MEMO (Complete framework implementing peak/neutral-loss counting and sample vectorization for metabolomics) — https://github.com/mandelbrot-project/memo

Evaluation signals

• Fingerprint vector length matches the total number of unique MS2 m/z values and neutral losses observed across all samples.
• All occurrence counts are non-negative integers; no negative or fractional counts are present.
• MemoMatrix dimensions are (n_samples, n_features) where n_features is constant across all rows, confirming feature alignment consistency.
• Sum of counts per sample correlates with the total number of MS2 spectra in that sample, validating exhaustive counting.
• Fingerprints from blank/control samples show predictably low or sparse counts compared to active samples before filtering.

Limitations

• Fingerprints discard spectral intensity and fragmentation fragmentation pathway information; only presence/absence frequency is retained.
• MS2 peak clustering or tolerance-based grouping is not applied; each distinct m/z is counted separately, potentially inflating feature dimensionality in high-resolution instruments.
• Neutral loss calculation depends on accurate precursor m/z assignment; errors propagate into incorrect loss counts.
• Very small samples or samples with few MS2 spectra may produce sparse, uninformative fingerprints with high noise-to-signal ratio.

Evidence

• [intro] MS2 fingerprints are generated by counting occurrences of MS2 peaks and neutral losses in each sample: "The occurence of MS2 peaks and neutral losses (to the precursor) in each sample is counted and used to generate an MS2 fingerprint"
• [intro] Fingerprints are aligned in a second stage to enable cross-sample comparison: "These fingerprints can in a second stage be aligned to compare different samples"
• [readme] MEMO is built on matchms and spec2vec for MS2 handling: "MEMO is mainly built on matchms_ and spec2vec_ packages for handling the MS2 spectra"
• [other] Fingerprint aggregation uses numpy array operations to ensure consistent feature alignment: "Aggregate fingerprint vectors across all samples into a unified matrix using numpy array operations, ensuring consistent feature alignment"
• [readme] MEMO suits chemodiverse samples with poor feature overlap or strong RT shift: "MEMO suits particularly well to compare chemodiverse samples, ie with a poor features overlap, or to compare samples with a strong RT shift"