Signal Quality Assessment

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"