whoisjack/python-bioimage-analysis-tutorial

A self-explanatory, hands-on intro to bioimage analysis in python. Slightly outdated but still much liked by learners.

python-bioimage-analysis-tutorial's Issues

Figures url broken?

Somehow I could not see any Figures in the notebook at

https://github.com/WhoIsJack/python-workshop-image-processing/blob/master/main_tutorial/tutorial_pipeline.ipynb

Are the URLs broken?

Thanks

Future Update Megathread

A list of stuff I want to update/improve to get the tutorial back in shape.

Major points

• Change to a more interesting dataset (multiple channels, more cell diversity)
  • Option 1: data from the Allen Cell Explorer [WIP; see Hackday 2021 local]
  • Option 2: IDR-0072: data, publication
• Add a section on ML-based segmentation (using stardist or cellpose)
• Finalize & incorporate the data analysis / data science notebook [WIP; see Hackday 2021 local?]
• Update the lecture slides
  • New/better examples at the start
  • More on machine learning
  • Revisit and streamline existing content
  • Modern look
  • Bonus: less basic ML, more advanced stuff
• Record the lectures & put them on YouTube

Minor points

• Ensure all instructions and the solutions code are up to date (+streamline a bit where possible)
• Better handling of input/output data [Done; see Hackday 2021 branch]
  • Put input data on figshare and write code that pulls it from there on first execution (may pull from elsewhere with new data)
  • Create outputs dir and ignore everything in it with an added new .gitignore, then change file writing funcs to write there
• Provide more and updated links to other materials
• Switch to (or at least introduce) napari for interactive image viewing?
• Convenience: Automatic install of all dependencies?
  • At least provide an environment.yml to facilitate install [Done; see Hackday 2021 branch]
• Convenience: Make notebook metadata ignore conda version & co.?
• Convenience: Add binder as a way of looking at and testing the materials without install/download? [WIP; see Hackday 2021 branch]

Use different example data

Having more engaging, and more pertinent to a wider range of biologists, would be an improvement.

We discussed possibilities including

• Allen cell

and these various IDR datasets

• A reference library for assigning protein subcellular localizations by image-based machine learning: data, publication
• Dynamics of the 4D genome during in vivo lineage specification and differentiation: data, publications
• Molecular determinants of large cargo transport into the nucleus: data, publication

It would be important to choose a dataset that either doesn't need adapting of the code or remember to adapt the code accordingly.