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Option I – Virtual assay

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What if I have analyzed data in a way that is not perfectly reusable or reproducible – for instance when I use a proprietary software or a colleague helped with data analysis – but I would still want to store and annotate the data analysis in a way comprehensible to others?

A computational workflow is like a protocol

Section titled A computational workflow is like a protocol

Just like laboratory workflows produce samples or data, computational workflows produce data. They represent the processing steps used in data analysis or data transformation.

A computational workflow can simply be treated as a protocol. This can be a summary of the steps followed in any data analysis software. Or it can be a script. Hence the data analysis can simply be packaged as an assay, with the computational workflow stored in protocols and the resulting data stored in dataset

ARCitect

  1. Add a new assay Visualization.

  2. Import the python script heatmap.py into protocols.

  3. Import the figure heatmap.svg resulting from the script into dataset.

    • Directoryassays
      • DirectoryVisualization
        • Directorydataset
          • heatmap.svg
        • Directoryprotocols
          • heatmap.py
        • isa.assay.xlsx
        • README.md
  4. Add a new annotation table to Visualization.

  5. Add the following building blocks:

    • Input [Data]: Use File Picker to reference the proteomics output file.
    • Protocol REF: Use File Picker to reference python script.
    • Output [Data]: Use File Picker to reference to heatmap result file.
    Input [Data]ProtocolREFOutput [Data]
    sugar_result.csvheatmap.pyheatmap.svg