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Spatial-LDA viz and preprocessing notebook #468
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…or spatial-LDA preprocessing notebook.
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
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Looking good. You added a ton of files in this PR. I think it makes sense to have the input data, but I don't think we need to check in the outputs, especially the plots that are generated.
…ate mock imports in conf.py.
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Just a few minor things to add.
Co-authored-by: alex-l-kong <31424707+alex-l-kong@users.noreply.github.com>
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Looks good!
What is the purpose of this PR?
This PR creates the notebook and associated functions/visualizations for preprocessing data for spatial-LDA.
How did you implement your changes
The LDA_Preprocessing notebook is similar to the other example notebooks in the sense that it walks the user through the steps of loading, formatting, and visualizing cell data for use in spatial-LDA analysis.
processing.py
is the main module with the formatting and processing functions. Some visualization functions were added toanalysis/visualize.py
, and a few random helper functions were added toutils/spatial_lda_utils.py
.The notebook uses an example dataset with two FOVs to show how the functions work. This dataset is included in
example_dataset/spatial_lda_input_data
. There are also directories with examples of the different visualizations as well as the processed output data which will get passed to the training/inference functions in the next step.Remaining issues
A few points here:
topic_eda()
can be a bit slow, especially for larger sets of FOVs and also larger topic numbers. One thought is to add support for parallel processing either across topics or across bootstrap iterations.