# Mean Intensity Aggregation > Use when after importing imzML or vendor-specific MSI data into napari and visualizing the raw spectral dataset. - Skill: `holobiomicslab/mean-intensity-aggregation` (Agent Skill) - Install (CLI): `npx skillmds@latest add holobiomicslab/mean-intensity-aggregation` - Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/mean-intensity-aggregation/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: CC-BY-4.0 - Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/holobiomicslab/mean-intensity-aggregation --- # mean-intensity-aggregation ## Summary Compute mean intensity values across all pixels in a mass spectrometry imaging (MSI) dataset by aggregating intensity arrays at each unique m/z value. This workflow step produces a representative mean spectrum and optionally exports it for downstream ROI analysis and metabolite annotation. ## When to use Apply this skill after importing imzML or vendor-specific MSI data into napari and visualizing the raw spectral dataset. Use it when you need a global intensity profile across the entire MSI experiment to identify dominant m/z peaks, normalize intensity ranges, or generate a reference spectrum for region-of-interest (ROI) comparisons. ## When NOT to use - Input MSI data has not been imported and loaded into the napari environment—perform data import first. - You require intensity values for a single pixel or localized region only—use ROI selection instead. - The dataset is already pre-aggregated into a mean spectrum; recalculating would be redundant. ## Inputs - Imported MSI spectral dataset in imzML or vendor-specific format - m/z values and intensity arrays from all pixels/samples - Optional pre-processing parameters (noise reduction %, normalization method, hotspot removal quantile) ## Outputs - Mean spectrum: m/z vs. mean intensity table - CSV or HDF5 export file of aggregated mean intensities - Mean spectrum visualization plot (optional) - Image view of mean intensity distribution (optional) ## How to apply Load the imported MSI spectral data (imzML or converted centroid format) into the MSI-Explorer napari plugin environment. Extract m/z values and their corresponding intensity arrays for all spectra in the dataset. For each unique m/z value across all pixels/samples, calculate the arithmetic mean of intensities at that m/z. Optionally apply pre-processing steps (noise reduction as a percentage threshold, normalization methods such as TIC, RMS, or reference peak, and hotspot removal at a quantile threshold like 99.9%) before mean calculation. Aggregate results into a structured m/z vs. mean intensity table and export as CSV or HDF5 for compatibility with downstream ROI analysis and annotation workflows. ## Related tools - **napari** (Plugin framework environment for loading, visualizing, and processing MSI spectral data) — https://github.com/napari/napari - **MSI-Explorer** (napari plugin that implements mean intensity calculation as a workflow step following data import and visualization) — https://github.com/MMV-Lab/MSI-Explorer - **Python** (Programming language for arithmetic aggregation and export of mean intensity arrays) ## Evaluation signals - Output table contains all unique m/z values present in the original dataset with no missing entries - Each mean intensity value is the arithmetic average of intensities at that m/z across all pixels; spot-check by manual calculation on a subset - Output file is valid CSV or HDF5 and is compatible with downstream ROI analysis and annotation tools - Mean spectrum plot shows expected dominant peaks and relative intensity relationships consistent with the raw data distribution - When pre-processing is applied, mean intensities reflect the specified noise reduction percentage, normalization method, and hotspot removal threshold ## Limitations - Profile mode MSI data must be converted to centroid mode before processing; the plugin prompts for this conversion but cannot be circumvented - Mean intensity calculation assumes all pixels have comparable sampling; highly uneven sampling or missing pixels may skew the aggregate - Pre-processing steps (noise reduction, normalization, hotspot removal) are applied before aggregation; the order and parameters chosen will significantly affect the final mean spectrum - Export format (CSV vs. HDF5) must be chosen appropriately for downstream tools; some tools may require specific formats ## Evidence - [other] MSI-Explorer implements mean intensity calculation as a workflow step that processes imported MSI data following data import and visualization stages, operating within the napari plugin framework.: "MSI-Explorer implements mean intensity calculation as a workflow step that processes imported MSI data following data import and visualization stages, operating within the napari plugin framework." - [other] For each unique m/z value across all spectra, calculate the arithmetic mean of intensities at that m/z across all pixels/samples.: "For each unique m/z value across all spectra, calculate the arithmetic mean of intensities at that m/z across all pixels/samples." - [other] Aggregate results into a structured table (m/z vs. mean intensity) and save as a CSV or HDF5 file compatible with downstream ROI analysis and annotation workflows.: "Aggregate results into a structured table (m/z vs. mean intensity) and save as a CSV or HDF5 file compatible with downstream ROI analysis and annotation workflows." - [readme] To calculate the mean spectrum, click on `Show true mean spectrum`. Clicking `Show image` will create an image view of the currently plotted data: "To calculate the mean spectrum, click on `Show true mean spectrum`. Clicking `Show image` will create an image view" - [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."