# Signal Quality Assessment

> Use when you have imported raw MSI spectral data in imzML format and need to improve signal-to-noise ratio before performing mean intensity calculations, ROI analysis, or database annotation.

- Skill: `holobiomicslab/signal-quality-assessment` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/signal-quality-assessment`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/signal-quality-assessment/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/signal-quality-assessment

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# signal-quality-assessment

## Summary

Evaluate and enhance MSI spectral data quality by applying noise reduction and intensity normalization to remove background noise and standardize signal across all pixels and m/z values. This preprocessing step is essential before downstream biochemical annotation and ROI analysis to ensure reliable intensity measurements and comparisons.

## When to use

Apply this skill when you have imported raw MSI spectral data in imzML format and need to improve signal-to-noise ratio before performing mean intensity calculations, ROI analysis, or database annotation. Use it when spectra contain background noise or exhibit intensity variations across pixels that would compromise quantitative comparisons.

## When NOT to use

- Input data is already preprocessed or has been validated as high-quality without artifacts
- Spectra do not contain background noise or intensity artifacts (e.g., simulated or synthetic data)
- Analysis requires preservation of absolute intensity values for external quantitative comparison without normalization

## Inputs

- Raw MSI spectral data in imzML format
- Mass spectrometry imaging dataset with pixel-wise spectra
- Profile or centroid mode spectra

## Outputs

- Noise-reduced and intensity-normalized MSI spectra
- Preprocessed data compatible with downstream visualization and ROI analysis
- Mean spectrum plot showing quality improvement
- Mean spectrum data exportable as CSV

## How to apply

Load raw MSI spectral data using the MSI-Explorer napari plugin in Python. First, apply noise reduction by selecting a user-defined percentage threshold (e.g., 3%) to remove background signal and improve signal-to-noise ratio. Then apply one of four normalization methods—Total Ion Current (TIC), Root Mean Square (RMS), Median, or Reference Peak (internal standard)—depending on your experimental design and whether you have a known internal standard. Optionally apply hotspot removal at a quantile threshold (e.g., 99.99%) to suppress anomalous high-intensity pixels. Execute all preprocessing steps together, then verify output quality by calculating and visualizing the mean spectrum to confirm noise reduction and normalization have been applied appropriately.

## Related tools

- **napari** (Interactive visualization platform and plugin host for MSI data viewing and ROI selection during preprocessing) — https://github.com/napari/napari
- **MSI-Explorer** (napari plugin that implements noise reduction, normalization, and hotspot removal preprocessing workflows) — https://github.com/MMV-Lab/MSI-Explorer
- **Python** (Programming environment for loading, processing, and scripting MSI preprocessing pipelines)

## Evaluation signals

- Signal-to-noise ratio visibly improves in the mean spectrum plot after noise reduction is applied
- Intensity distributions are standardized across all pixels and m/z values after normalization (verified by comparing pre- and post-normalization spectra)
- No systematic bias or artifacts are introduced by the chosen normalization method
- Exported mean spectrum data shows consistent intensity scaling across replicates or regions after preprocessing
- Hotspot removal does not eliminate biologically relevant peaks (verify by checking that known metabolite m/z values are retained)

## Limitations

- Noise reduction percentage and normalization method must be chosen empirically based on dataset characteristics; no automatic selection is described
- Hotspot removal uses a fixed quantile threshold (99.99% default) which may not be optimal for all tissue types or acquisition modes
- Profile-mode spectra are converted to centroid mode via user prompt, which may alter downstream analysis if not handled consistently
- Normalization assumes that the chosen method (TIC, RMS, Median, or reference peak) is appropriate for the biological system and does not conflate abundance with instrumental sensitivity

## Evidence

- [other] MSI-Explorer includes a pre-processing module that performs noise reduction and normalization on imported MSI spectra as part of its workflow.: "MSI-Explorer includes a pre-processing module that performs noise reduction and normalization on imported MSI spectra"
- [other] Apply noise reduction filtering to remove background signal and improve signal-to-noise ratio across the spectral dataset.: "Apply noise reduction filtering to remove background signal and improve signal-to-noise ratio across the spectral dataset"
- [other] Apply intensity normalization to standardize spectral intensities across all pixels and m/z values.: "Apply intensity normalization to standardize spectral intensities across all pixels and m/z values"
- [readme] The normalization methods that the user can apply are Total ion current (TIC), Root mean square (RMS), Medium, Reference peak (or internal standard): "The normalization methods that the user can apply are - Total ion current (TIC) - Root mean square (RMS) - Medium - Reference peak (or internal standard)"
- [readme] Users can choose their desired level of noise reduction (shown as a percentage) for their experiment.: "Users can choose their desired level of noise reduction (shown as a percentage) for their experiment"
- [readme] Hotspot removal can also be applied using a default threshold of 99.99%.: "Hotspot removal can also be applied using a default threshold of 99.99%"
- [readme] After pre-processing steps are chosen, click `Execute` and `Show true mean spectrum` to calculate the mean intensity.: "After pre-processing steps are chosen, click `Execute` and `Show true mean spectrum` to calculate the mean intensity"