Update data/embeddings/depmap_crispr_gene_effect_pca256/README.md
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data/embeddings/depmap_crispr_gene_effect_pca256/README.md
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- Modality: Functional genomics / CRISPR gene-dependency profile
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- Algorithm: PCA-reduced CRISPR gene-effect profile across cancer models
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- Embedding source URL:
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- Original model/code URL: Not applicable; source is a DepMap CRISPR gene-effect matrix
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## Public files
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The source matrix had cancer models as rows and genes as columns. Each value represents a DepMap CRISPR gene-effect score for a gene in a cancer model. During processing, the matrix was transposed to genes × models, missing values were filled by the source-gene column mean before PCA, model/cell-line features were standardized across mapped genes, and randomized PCA was applied to produce a 256-dimensional gene embedding.
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This embedding should therefore be interpreted as a compact CRISPR dependency-profile embedding.
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### Provenance fields
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### Important implementation decisions
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PCA was used because the source representation has 1,208 model/cell-line features.
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Missing values were filled before PCA because PCA requires a complete numeric matrix.
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- Modality: Functional genomics / CRISPR gene-dependency profile
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- Algorithm: PCA-reduced CRISPR gene-effect profile across cancer models
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- Embedding source URL: CRISPRGeneEffect.csv and Model.csv from https://depmap.org/portal/download/
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## Public files
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The source matrix had cancer models as rows and genes as columns. Each value represents a DepMap CRISPR gene-effect score for a gene in a cancer model. During processing, the matrix was transposed to genes × models, missing values were filled by the source-gene column mean before PCA, model/cell-line features were standardized across mapped genes, and randomized PCA was applied to produce a 256-dimensional gene embedding.
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This embedding should therefore be interpreted as a compact CRISPR dependency-profile embedding.
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### Provenance fields
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### Important implementation decisions
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PCA was used because the source representation has 1,208 model/cell-line features.
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Missing values were filled before PCA because PCA requires a complete numeric matrix.
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| 89 |
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