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Democratising-Imaging

This repo will contain introductory wiki for the Imaging Project. This wiki serves as a central hub for documentation, guides, and resources related to the project. It provides valuable information to help contributors and users understand the project and get involved.

Overview of the project

The primary motivation behind this project is to make the HPC democratisable for Imaging researchers at WEHI. We can distinguish between two types of users: technical users who are adept in Python and UNIX, and non-technical users who are less familiar with coding. Technical users can directly engage with Milton HPC (The high performance computing system at WEHI), however, non-technical users face challenges in utilising these resources efficiently due to their lack of coding expertise. Therefore, the project seeks to bridge this gap by providing a user-friendly interface, facilitating job submission and workflow management, thus empowering non-technical users to leverage HPC capabilities effectively.

2024 Semester 1 Implementation

To achieve this goal, the Bioimaging Team has attempted to run their scripts on Jupyter Notebook, Nextflow Tower and Python Flask. The Python Flask app is most preferable since it serves as the middle ground between Jupyter Notebook (user-friendly) and Nextflow Tower (customisable workflows at a central location). However, the Python Flask app cannot be run on Milton due to resource exhaustion. Hence, we are looking at an R/Shiny app to serve as an easy to use interface while also keeping it highly customisable.

Watch our Final Presentation here

Main Pages:

1. Onboarding Checklist for WEHI Students to follow progress of Semester 1 2024 interns
2. Overview of Domain
3. User Story
4. Custom Platform running on R/Shiny on Open OnDemand as per 2024 Semester 1 (move to onboarding checklist and then remove later)
5. Box of Archive containing work done by previous interns but no longer relevant to the project

Notes:

• The code in this GitHub is outdated, this is a previous implementation of a Galaxy app which the 2023 Semester 2 intake did. The most up to date work can be found in the 2024 Semester 1 intake in the R/Shiny wiki