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?
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
Add a new assay Visualization.
Import the python script heatmap.py into protocols.
Import the figure heatmap.svg resulting from the script into dataset.
Directoryassays
DirectoryVisualization
Directorydataset
heatmap.svg
Directoryprotocols
heatmap.py
isa.assay.xlsx
README.md
…
Add a new annotation table to Visualization.
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.
