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Update data/embeddings/depmap_crispr_gene_effect_pca256/README.md

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data/embeddings/depmap_crispr_gene_effect_pca256/README.md CHANGED
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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: Local processing from DepMap CRISPRGeneEffect.csv and Model.csv
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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. Genes with similar vectors have similar patterns of CRISPR dependency across DepMap cancer models. It is not a sequence embedding, expression embedding, or model-token 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. The 256-dimensional PCA output captures most of the standardized dependency-profile variance while making the embedding size comparable to other compact embeddings in the resource.
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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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