# Msi Data Table Export

> Use when after calculating mean intensity values across all spectra in an MSI dataset (or within a manually selected ROI), and you need to store the resulting m/z–intensity table in a portable format for downstream ROI analysis, database annotation, or external statistical pipelines.

- Skill: `holobiomicslab/msi-data-table-export` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/msi-data-table-export`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/msi-data-table-export/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/msi-data-table-export

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# msi-data-table-export

## Summary

Export mean intensity spectra and region-of-interest (ROI) derived m/z vs. intensity tables from MSI-Explorer as CSV or HDF5 files for downstream biochemical annotation and statistical analysis. This skill bridges napari-based MSI visualization and external analytical workflows.

## When to use

After calculating mean intensity values across all spectra in an MSI dataset (or within a manually selected ROI), and you need to store the resulting m/z–intensity table in a portable format for downstream ROI analysis, database annotation, or external statistical pipelines.

## When NOT to use

- If you need to export raw spectral data without aggregation (use data import/visualization export instead).
- If your MSI data is still in profile mode and has not been converted to centroid mode, as mean calculation requires discrete m/z bins.
- If you require real-time streaming of spectra during ongoing data acquisition rather than batch export of computed results.

## Inputs

- napari MSI layer with loaded imzML or vendor-specific mass spectrometry imaging data
- calculated mean intensity spectrum (array of m/z and intensity pairs)
- optional: user-drawn ROI mask (label layer)

## Outputs

- CSV file containing m/z vs. mean intensity table
- HDF5 file containing m/z vs. mean intensity table with metadata
- PNG export of spectrum plot (optional)

## How to apply

Following mean spectrum calculation in MSI-Explorer (via 'Show true mean spectrum'), click 'Export spectrum data' to serialize the plotted m/z vs. intensity table as a CSV file; alternatively, select the HDF5 format for compatibility with complex hierarchical metadata. For ROI-specific exports, first define the ROI using the brush and label tools, calculate the ROI mean spectrum via 'Calculate ROI mean spectrum', then export. The exported table contains columns for m/z values and their corresponding arithmetic mean intensities; verify row count matches the number of unique m/z values detected across the dataset.

## Related tools

- **napari** (Plugin host and visualization framework for interactive MSI layer display and ROI selection prior to export) — https://github.com/napari/napari
- **MSI-Explorer** (napari plugin that implements mean intensity calculation, ROI workflow, and CSV/HDF5 export functionality) — https://github.com/MMV-Lab/MSI-Explorer
- **Python** (Runtime environment for MSI-Explorer and CSV/HDF5 I/O libraries (csv, h5py))

## Evaluation signals

- Exported CSV/HDF5 file exists and is readable by standard data analysis tools (pandas, R data.frame).
- Row count in exported table equals the number of unique m/z values detected across all spectra in the dataset.
- Column headers are present and labeled 'm/z' and 'mean_intensity' (or equivalent); no missing or NaN values in m/z column.
- For ROI exports: row count is less than or equal to the full dataset table; intensity values are non-negative and within expected range for the normalization method applied.
- File timestamp and size are consistent with the scale of the input MSI dataset (e.g., 1000–100,000 m/z bins × 4–8 bytes per value).

## Limitations

- Export does not include uncertainty/variance estimates for individual m/z mean intensities; only point estimates are stored.
- CSV export is human-readable but lacks schema metadata; HDF5 is recommended for large datasets or when provenance tracking is critical.
- ROI export requires manual selection via napari brush; automated ROI definition is not supported by this export skill alone.
- Export inherits all preprocessing applied before mean calculation (noise reduction, normalization, hotspot removal); no option to export raw vs. processed side-by-side in a single file.

## Evidence

- [other] For each unique m/z value across all spectra, calculate the arithmetic mean of intensities at that m/z across all pixels/samples. 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 export the plotted data as .csv file, click `Export spectrum data`. To save the spectrum plot as image, click `Export spectrum plot`.: "To export the plotted data as .csv file, click `Export spectrum data`."
- [readme] To select the ROI, click on `Select ROI for mean spectrum`. Adjust the brush size and label color. You can fill the area by using paint icon. Then click on the `Calculate ROI mean spectrum`. You can export as `.csv` file by using `Export spectrum data`.: "Then click on the `Calculate ROI mean spectrum`. You can export as `.csv` file by using `Export spectrum data`."
- [intro] It covers data import, visualization, mean intensity calculation, region of interest (ROI) analysis, annotation with selected databases and pre-processing such as noise reduction and normalization.: "It covers data import, visualization, mean intensity calculation, region of interest (ROI) analysis, annotation with selected databases and pre-processing such as noise reduction and normalization."