Spatial Distribution Pattern Analysis

spatial-distribution-pattern-analysis

Summary

Extract and evaluate spatial distribution patterns of ions across a mass spectrometry imaging dataset to classify matrix-related vs. analyte signals. This skill leverages co-localization coherence and spatial heterogeneity as discriminative features alongside chemical formula matching.

When to use

When you have loaded MSI peak data with associated m/z values and need to disambiguate matrix ions from analyte ions. Apply this skill when chemical formula alone is insufficient (e.g., overlapping or isobaric peaks exist) and you have pixel-level spatial intensity maps for each ion. Particularly useful for silver-assisted or other matrix-dependent ionization methods where matrix peaks cluster spatially.

When NOT to use

• Input is already processed into a feature table or data matrix without spatial coordinates—spatial patterns cannot be reconstructed.
• MSI dataset lacks pixel-level spatial intensity maps or has been heavily summarized (e.g., only mean spectra per region).
• Matrix composition is unknown or highly variable across the tissue—spatial coherence assumptions may not hold.

Inputs

• MSI peak matrix (rMSIproc .zip format or equivalent)
• Processed MSI data with pixel-level intensity maps (.tar or equivalent)
• Chemical formula assignments or m/z calibration data for each ion
• Optional: known matrix or analyte m/z reference list

Outputs

• Binary matrix/non-matrix classification labels per ion
• Spatial coherence metrics and pattern descriptors per ion
• Visual annotation report justifying spatial classification
• Cleaned peak matrix with matrix ions flagged or removed

How to apply

Load MSI peak data and spatial intensity maps for each ion using rMSI or rMSIproc. Extract the spatial distribution pattern—pixel-by-pixel intensity across the imaging region—for each ion m/z. Compute spatial coherence metrics (e.g., uniformity, clustering, co-localization with known matrix or analyte landmarks) to classify ions. Matrix-related ions typically exhibit uniform or highly clustered spatial patterns reflecting matrix deposition; analytes show heterogeneous or focal distributions. Cross-reference spatial patterns against chemical formula predictions to resolve ambiguous cases (e.g., isobaric peaks with different spatial signatures). Generate a visual spatial report to document each ion's classification rationale.

Related tools

• rMSIcleanup (Primary annotation and spatial pattern classification engine; integrates chemical formula and spatial distribution to label ions) — https://github.com/gbaquer/rMSIcleanup
• rMSI (Loads and manages MSI data structures and pixel-level intensity maps required for spatial pattern extraction) — https://github.com/prafols/rMSI
• rMSIproc (Processes raw imzML files into peak matrices and spatial datasets; handles data I/O in standardized formats) — https://github.com/prafols/rMSIproc
• R (Execution environment for rMSI, rMSIproc, and rMSIcleanup packages)
• devtools (Dependency management and installation of rMSI, rMSIproc, and rMSIcleanup from GitHub)

Examples

results <- rMSIcleanup::annotate_matrix(pks, "Ag1", full); rMSIcleanup::generate_pdf_report(results, pks, full, "test", folder="/home/user/")

Evaluation signals

• Spatial patterns for each ion are reproducible: re-running on the same dataset yields identical or near-identical coherence metrics and classifications.
• Matrix ions show clustering or uniformity (low variance) across pixels; analytes show heterogeneous (high variance or focal) distributions—verified by visual inspection of generated spatial maps.
• Overlapping peak detection correctly identifies isobaric ions by distinct spatial signatures (e.g., same m/z but different spatial localization receive different labels).
• Visual report transparently justifies at least 80% of classifications with spatial coherence and chemical formula evidence; annotations align with domain knowledge of the matrix and tissue type.
• Cleaned peak matrix after matrix removal shows reduced background noise in downstream analysis without loss of analyte signal in expected tissue regions.

Limitations

• Algorithm assumes matrix-related ions exhibit spatially coherent (uniform or highly clustered) distributions; fails when matrix is inhomogeneously distributed or analytes co-localize exactly with matrix regions.
• Overlapping peak detection relies on distinct spatial signatures; co-localized isobaric ions (identical m/z and identical spatial pattern) cannot be disambiguated by this method alone.
• Chemical formula matching requires accurate mass calibration and known ion adducts; formula misassignment or unexpected ionization products may confound spatial classification.
• Spatial resolution and pixel size affect pattern detectability; low-resolution imaging may blur distinct spatial features and reduce classification confidence.

Evidence

• [intro] The algorithm takes into account the chemical formula and the spatial distribution to determine which ions are matrix-related: "The algorithm takes into account the chemical formula and the spatial distribution to determine which ions are matrix-related"
• [intro] The package incorporates an overlapping peak detection feature to prevent misclassification of overlapped or isobaric ions: "The package incorporates an overlapping peak detection feature to prevent misclassification of overlapped or isobaric ions"
• [intro] The package generates a visual report to transparently justify each annotation: "the package generates a visual report to transparently justify each annotation"
• [other] Extract spatial distribution patterns for each ion across the imaging dataset: "Extract spatial distribution patterns for each ion across the imaging dataset"
• [other] Assign binary matrix/non-matrix labels to each ion based on formula matching and spatial coherence metrics: "Assign binary matrix/non-matrix labels to each ion based on formula matching and spatial coherence metrics"