Multi Sample Metabolomics Data Integration

multi-sample-metabolomics-data-integration

Summary

Merges separately generated MS2 fingerprint matrices (MemoMatrix objects) from multiple metabolomics samples into a single unified matrix, enabling retention-time-agnostic comparative analysis across chemodiverse or cross-platform datasets. This skill is essential when aligning MS2 fragmentation data from samples acquired under different LC–MS conditions or on different instrument platforms.

When to use

Apply this skill when you have two or more independently processed MemoMatrix objects (each generated from a separate sample set) and your analysis goal requires direct comparison of MS2 fingerprint profiles across those samples. Typical triggers include: comparing samples acquired on different mass spectrometer technologies (e.g., MaXis Q-ToF vs Q-Exactive), reconciling data from different LC methods with strong retention-time shifts, or unifying chemodiverse sample sets with poor feature overlap that would fail alignment at the feature level.

When NOT to use

• Input matrices are from the same LC–MS run or cohort and have already been aligned at the feature level; use within-batch normalization instead.
• You need to retain retention-time information for peak matching; MEMO is explicitly RT-agnostic and discards RT data.
• Samples have extreme differences in total ion abundance and you require abundance-normalized or probabilistic similarity scores; the merged fingerprint is a raw count matrix.

Inputs

• MemoMatrix object (from sample set A)
• MemoMatrix object (from sample set B)
• Additional MemoMatrix objects (optional, for multi-way merges)

Outputs

• Merged MemoMatrix object (unified matrix containing all samples and all MS2 peaks/neutral losses)
• Serialized merged MemoMatrix (exported to structured output file, e.g., JSON, HDF5, or CSV)

How to apply

Load each independently generated MemoMatrix object (containing MS2 peak and neutral loss occurrence counts for a sample set) into the MEMO merge module. Execute the merged_memo() function to combine all input matrices into a single unified matrix. The merge operation aligns the MS2 fingerprint data across samples by taking the union of all rows (samples) and columns (MS2 peaks/neutral losses), preserving occurrence counts from each input matrix. After merging, validate dimensional consistency (output rows = union of input rows; output columns = union of input columns) and verify that all expected columns from both inputs are present in the merged output. Finally, serialize and export the unified MemoMatrix to a structured output file for downstream filtering and visualization.

Related tools

• MEMO (Core library providing merged_memo() function and MemoMatrix data structure for multi-sample MS2 fingerprint integration) — https://github.com/mandelbrot-project/memo
• memo-ms (Python package distribution of MEMO; installed via pip to access merged_memo() and related APIs) — https://pypi.org/project/memo-ms/
• matchms (Underlying library for MS2 spectrum handling, metadata cleaning, and spectral format I/O that MEMO depends on) — https://github.com/matchms/matchms
• spec2vec (Optional downstream tool for computing MS2 spectral similarity scores on merged fingerprints) — https://github.com/iomega/spec2vec
• Python 3.8+ (Runtime environment for executing MEMO merge workflows and serialization)

Examples

from memo import MemoMatrix, merged_memo; memo1 = MemoMatrix.load('sample_set_A.memo'); memo2 = MemoMatrix.load('sample_set_B.memo'); merged = merged_memo([memo1, memo2]); merged.to_file('merged_output.memo')

Evaluation signals

• Merged MemoMatrix has correct dimensions: row count equals union of input sample counts; column count equals union of unique MS2 peaks and neutral losses across all inputs.
• All columns present in input matrices appear in the merged output; no loss of peak or neutral loss features during merge.
• Occurrence counts are preserved: spot-check a known MS2 peak or neutral loss value in input and verify it appears with the same count in the corresponding sample row of the merged matrix.
• Serialized output file validates against expected schema (if using JSON) or loads successfully with memo-ms deserialization functions without format errors.
• Merged matrix can be successfully passed to downstream MEMO filtering or visualization functions (e.g., blank peak removal, TMAP, MDS/PCoA) without shape mismatches or missing-value errors.

Limitations

• Merging assumes all input MemoMatrix objects were generated using the same MS2 peak detection and neutral loss definition parameters; inconsistency in preprocessing may lead to incomparable fingerprints.
• The merged matrix retains only occurrence counts; absolute intensity or area under the curve (AUC) data are lost if they were not baked into the fingerprint generation step.
• No built-in handling of batch effects, systematic retention-time shifts, or instrument-specific peak calibration offsets; such effects must be addressed before or after merging using external normalization or filtering.
• Merged matrices can become high-dimensional (many columns) when combining very diverse sample sets, potentially increasing sparsity and memory footprint.
• Performance on merging >10 large MemoMatrix objects is not explicitly benchmarked in the publication; scalability beyond typical two-to-three-sample merges is unclear.

Evidence

• [other] MEMO enables alignment of MS2 fingerprints generated from separate samples to compare them in a second stage.: "MEMO enables alignment of MS2 fingerprints generated from separate samples to compare them in a second stage."
• [intro] 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.: "The occurence of MS2 peaks and neutral losses (to the precursor) in each sample is counted and used to generate an MS2 fingerprint"
• [other] These fingerprints can in a second stage be aligned to compare different samples.: "These fingerprints can in a second stage be aligned to compare different samples"
• [intro] 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 spectrometers technology.: "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"
• [intro] a method allowing a Retention Time (RT) agnostic alignment of metabolomics samples using the fragmentation spectra (MS2) of their consituents: "a method allowing a Retention Time (RT) agnostic alignment of metabolomics samples using the fragmentation spectra (MS2) of their consituents"
• [intro] different filtering (remove peaks/losses from blanks for example) and visualization techniques (MDS/PCoA, TMAP, Heatmap, ...) can be used: "different filtering (remove peaks/losses from blanks for example) and visualization techniques (MDS/PCoA, TMAP, Heatmap, ...) can be used"