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Interactive visualization inspection

Skill HolobiomicsLab/asb-skill-collections/collections/metabolomics/v2/skills/interactive-visualization-inspection

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Install
npx -y skills add HolobiomicsLab/asb-skill-collections --skill interactive-visualization-inspection

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Use when after msFeaST pipeline execution has produced a dashboard_data.json file containing quantification, metadata, and spectral matrices, or when you need to validate that preprocessing steps correctly loaded and rendered ms/ms feature data before downstream statistical or network analysis.

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SKILL.md

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interactive-visualization-inspection

Summary

Load preprocessed mass spectrometry data (quantification tables, metadata, and spectral data) into a browser-based interactive dashboard for exploratory analysis and validation. This skill enables rapid inspection of JSON-serialized pipeline outputs without requiring installation of Python, R, or command-line tools.

When to use

After msFeaST pipeline execution has produced a dashboard_data.json file containing quantification, metadata, and spectral matrices, or when you need to validate that preprocessing steps correctly loaded and rendered ms/ms feature data before downstream statistical or network analysis.

When NOT to use

  • Input is raw mass spectrometry files (.mzML, .mzXML, or .raw); use preprocessing_mushroom_type_comparison.ipynb instead.
  • You need to modify the preprocessing parameters or re-run the feature extraction pipeline; open the msfeast_pipeline_mushroom_type_comparison.ipynb notebooks in Jupyter instead.
  • You are working on Windows and have not yet installed conda/R dependencies; the dashboard visualization itself works on Windows, but pipeline execution is not yet supported.

Inputs

  • msFeaST_Dashboard_bundle.html (bundled HTML/JavaScript/CSS file)
  • dashboard_data.json (JSON file output by msFeaST jupyter-notebook pipeline containing quantification table, metadata, and spectral data)

Outputs

  • Rendered interactive dataview tab displaying quantification table
  • Rendered metadata table and columns
  • Rendered spectral data matrices
  • Visual confirmation of data integrity and completeness

How to apply

Download the msFeaST_Dashboard_bundle.html file and the JSON output from your pipeline run. Open the HTML bundle in a desktop web browser (Firefox, Chrome, Edge, or Safari) on any operating system. Use the bundle's data-load interface to select and load your dashboard_data.json file. Navigate to the dataview tab within the dashboard interface. Inspect the loaded quantification table, metadata columns, and spectral data matrices to verify that row/column counts, metadata fields, and intensity values match your expectations from the preprocessing notebook. Check that no cells are empty or malformed before proceeding to statistical analysis.

Related tools

  • msFeaST (Produces the dashboard_data.json file via jupyter-notebook pipeline and hosts the interactive visualization bundle) — https://github.com/kevinmildau/msFeaST
  • Desktop web browser (Renders the msFeaST_Dashboard_bundle.html and provides the interactive dataview interface)

Evaluation signals

  • Dashboard loads without JavaScript errors or blank sections in the browser console.
  • Dataview tab renders all three components: quantification table, metadata table, and spectral data matrices with correct row and column counts.
  • Metadata columns display expected field names and sample identifiers without truncation or encoding errors.
  • Quantification values are numeric and within expected intensity ranges (no NaN, Inf, or text artifacts).
  • Spectral data matrices contain non-zero m/z and intensity pairs corresponding to the input features from the JSON.

Limitations

  • Dashboard visualization requires a modern desktop web browser with JavaScript enabled; mobile browsers and text-only terminals are not supported.
  • This skill only inspects data; it does not modify, filter, or re-process the JSON. Any preprocessing errors will be visible but must be corrected by re-running the pipeline notebooks.
  • Large JSON files (>50 MB) may render slowly or cause browser memory issues on systems with <4 GB RAM.
  • No changelog or version tracking is provided in the repository, so compatibility between msFeaST_Dashboard_bundle.html and dashboard_data.json versions must be verified manually.

Evidence

  • [readme] To inspect the interactive dashboard for the illustrative examples, please download the msFeaST_Dashboard_bundle.html and the ready made data from notebooks\data\omsw_pleurotus_ms2deepscore\dashboard_data.json.: "To inspect the interactive dashboard for the illustrative examples, please download the msFeaST_Dashboard_bundle.html and the ready made data from notebooks\data\omsw_pleurotus_ms2deepscore\dashboar"
  • [readme] Open the html bundle in your browser and load the select and load the data. Changing to the dataview tab shows the now loaded data.: "Open the html bundle in your browser and load the select and load the data. Changing to the dataview tab shows the now loaded data."
  • [readme] The interactive visualization dashboard works regardless of os on desktop browsers (e.g., firefox, chrome, edge, safari): "The interactive visualization dashboard works regardless of os on desktop browsers (e.g., firefox, chrome, edge, safari)"
  • [readme] If you only want to inspect pre-processed example files using the visual dashboard, the msFeaST_Dashboard_bundle.html is the only file needed alongside the .json file. No dependencies need to be installed to do so.: "If you only want to inspect pre-processed example files using the visual dashboard, the msFeaST_Dashboard_bundle.html is the only file needed alongside the .json file. No dependencies need to be"
  • [intro] According to the msFeaST Quickstart instructions, after opening the msFeaST_Dashboard_bundle.html in a browser and loading the dashboard_data.json file, switching to the dataview tab displays the loaded data.: "switching to the dataview tab displays the loaded quantification table, metadata, and spectral data"

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