Added UMAP Figures

[MattsonCam]

Computed UMAP from sampled JUMP data and visualized UMAPs labeled by different categories.

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Pull request overview

Adds a workflow to compute UMAP embeddings from sampled JUMP single-cell data and generate labeled UMAP visualizations (treatment type, anomaly score, control type).

Changes:

• Pin umap-learn in the conda environment for compatibility with the repo’s scikit-learn version.
• Add a notebook + nbconverted Python script to sample per-plate single cells and compute 2D UMAP coordinates.
• Add an nbconverted R script to render and save UMAP figures colored by multiple metadata fields.

Reviewed changes

Copilot reviewed 4 out of 8 changed files in this pull request and generated 7 comments.

File	Description
environment.yml	Pins umap-learn to a specific version to support the new UMAP computation workflow.
3.analyze_data/visualize_umaps/nbconverted/compute_plate_umaps.py	Implements plate sampling + UMAP computation and writes a parquet used for plotting.
3.analyze_data/visualize_umaps/compute_plate_umaps.ipynb	Notebook version of the UMAP sampling/computation workflow.
3.analyze_data/visualize_umaps/nbconverted/visualize_plate_umaps.r	Generates and saves UMAP plots labeled by treatment type, anomaly score, and control type.

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[Copilot]

@MattsonCam I've opened a new pull request, #61, to work on those changes. Once the pull request is ready, I'll request review from you.

[MattsonCam]

Thanks @d33bs, merging now!