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Hung LH, Straw E, Reddy S, Schmitz R, Colburn Z, Yeung KY. Cloud-enabled Biodepot workflow builder integrates image processing using Fiji with reproducible data analysis using Jupyter notebooks. Sci Rep 2022; 12:14920. [PMID: 36056115 PMCID: PMC9440253 DOI: 10.1038/s41598-022-19173-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
Modern biomedical image analyses workflows contain multiple computational processing tasks giving rise to problems in reproducibility. In addition, image datasets can span both spatial and temporal dimensions, with additional channels for fluorescence and other data, resulting in datasets that are too large to be processed locally on a laptop. For omics analyses, software containers have been shown to enhance reproducibility, facilitate installation and provide access to scalable computational resources on the cloud. However, most image analyses contain steps that are graphical and interactive, features that are not supported by most omics execution engines. We present the containerized and cloud-enabled Biodepot-workflow-builder platform that supports graphics from software containers and has been extended for image analyses. We demonstrate the potential of our modular approach with multi-step workflows that incorporate the popular and open-source Fiji suite for image processing. One of our examples integrates fully interactive ImageJ macros with Jupyter notebooks. Our second example illustrates how the complicated cloud setup of an computationally intensive process such as stitching 3D digital pathology datasets using BigStitcher can be automated and simplified. In both examples, users can leverage a form-based graphical interface to execute multi-step workflows with a single click, using the provided sample data and preset input parameters. Alternatively, users can interactively modify the image processing steps in the workflow, apply the workflows to their own data, change the input parameters and macros. By providing interactive graphics support to software containers, our modular platform supports reproducible image analysis workflows, simplified access to cloud resources for analysis of large datasets, and integration across different applications such as Jupyter.
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Affiliation(s)
- Ling-Hong Hung
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA
| | - Evan Straw
- Biodepot LLC, Seattle, 98195, WA, USA
- University of Washington, Seattle, 98195, WA, USA
| | - Shishir Reddy
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA
| | - Robert Schmitz
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA
- Biodepot LLC, Seattle, 98195, WA, USA
| | | | - Ka Yee Yeung
- School of Engineering and Technology, University of Washington Tacoma, Box 358426, Tacoma, 98402, WA, USA.
- Biodepot LLC, Seattle, 98195, WA, USA.
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