PCA feature compression for organelle attribution - #10
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Scatterplot with linear regression + discordant gene highlighting, across both full and downsampled outputs. Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
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Summary
pca_optimization.py) to find the optimal number of PCs per biological signal using copairs mAP scoringobsmfollowing scanpy convention (X_pca,X_umap,X_phate)compare_dino_cp_map.py) with scatterplot + linear regression and discordant gene highlightinghconcat_by_perturbationand multi-level aggregation helpers toanndata_utils.pyDependencies
Requires czbiohub-sf/ops_utils#4 to be merged to
mainfirst.Test plan
--slurm --downsampledon DINO features, verify per-signal h5ads written todino/per_signal/--aggregate-only --downsampled, verifyguide_pca_optimized.h5adhasobsm['X_pca'],obsm['X_umap'],obsm['X_phate']python -m ops_utils.validation.embedding_convention guide_pca_optimized.h5ad— all checks pass--cell-profiler --slurm --downsampled, verify outputs land incellprofiler/compare_dino_cp_map.pyonce both feature types complete🤖 Generated with Claude Code