# Embedding name: Gene2Vec - Modality: Co-expression / expression - Embedding source URL: https://github.com/jingcheng-du/Gene2vec ## Public files - `embeddings.npz`: compressed NumPy archive containing the embedding matrix under key `embeddings`. - `genes.tsv`: row metadata table. Row order matches the embedding matrix. - `metadata.json`: machine-readable provenance and processing metadata. ## Matrix - Shape: `17,916 genes × 200 dimensions` - Dtype: `float32` ## Detailed processing notes ### Source and interpretation This embedding represents genes using the original Gene2Vec co-expression-derived 200-dimensional vectors. ### Provenance fields source_name: Gene2Vec original_model_or_method: Gene2Vec input_data_type: original Gene2Vec text vector file raw_source: ~/data/raw_embeddings/gene2vec/gene2vec_dim_200_iter_9.txt source_download_page: https://github.com/jingcheng-du/Gene2vec source_code_page: https://github.com/jingcheng-du/Gene2vec source_identifier_type: gene symbol mapping_identifier_type: priority symbol mapping to master_gene_table.csv source_rows_or_entries: 24,447 raw rows final_mapped_genes: 17,916 model_or_feature_dimension: 200 training_or_finetuning: none final_embedding_dtype: float32 ### Mapping and processing decisions Source gene symbols were mapped to master_gene_table.csv using the project priority order: current gene_symbol / HGNC approved symbol first, then HGNC previous symbols, then HGNC aliases. Only unique mappings were accepted. Ambiguous identifiers were excluded. Duplicate source rows mapping to the same final gene were averaged when safe. ### Processing workflow For this embedding: 1. Read the original Gene2Vec text vector file. 2. Parse the source identifiers. 3. Map source identifiers to master_gene_table.csv using the project mapping rule for this identifier type. 4. Exclude unmapped and ambiguous source identifiers. 5. Average duplicate source rows only when they map to the same final master-table gene. 6. Save the final matrix as float32. ### Summary of final mapping: Source identifiers / source rows or entries: 24,447 raw rows Dimensions: 200 Final mapped genes: 17,916 Master table rows: 20,097 Master coverage: 89.15% Mapping strategy: priority symbol mapping to master_gene_table.csv Ambiguous identifiers: excluded Duplicate final mappings averaged: yes ## Processing and provenance metadata This section preserves information moved out of `metadata.json` during catalog normalization. The loading-facing metadata is intentionally compact; detailed source, mapping, processing, quality-control, and reproducibility records remain here. Source catalog commit: `ab37742040d20b523aebd188d3079871a0c2b534`. ### Canonicalized field names | Previous field | Compact metadata field | | --- | --- | | `embedding_name` | `display_name` | | `mapping_strategy` | `mapping_method` | | `num_genes` | `n_genes` | | `embedding_dimension` | `n_dimensions` | | `matrix_shape[0]` | `n_genes` | | `matrix_shape[1]` | `n_dimensions` | | `dtype_saved` | `dtype` | ### Derived standardized fields - `species` was set to `Homo sapiens` from the validated human Ensembl `ENSG` identifier namespace. - `master_coverage_fraction` was computed as `n_genes / 20097`. ### Legacy processing and provenance record ```json { "all_zero_rows": { "count": 0, "documented": true, "expected": false, "reason": "No exact all-zero rows were detected in embeddings.npz.", "report_file": null }, "ambiguous_source_rows": "excluded and reported", "coverage_of_master_rows": 0.8914763397522019, "duplicate_source_rows": "averaged when multiple source rows mapped to the same master gene", "embedding_dim": 200, "former_processing_embedding_id": "gene2vec_v1_1", "mapping_method_counts": { "direct_current_gene_symbol": 16796, "hgnc_alias_symbol": 39, "hgnc_previous_symbol": 1130 }, "n_ambiguous_source_rows_excluded": 32, "n_bad_rows_skipped": 0, "n_genes_added_vs_gene2vec_strict_v1": 1126, "n_mapped_genes": 17916, "n_mapped_source_rows": 17965, "n_master_rows": 20097, "n_nonfinite_source_rows_skipped": 0, "n_source_rows_raw": 24447, "n_unmapped_source_rows": 6450, "n_zero_vectors": 0, "public_file_structure": { "embeddings": "embeddings.npz", "genes": "genes.tsv", "metadata": "metadata.json", "readme": "README.md" } } ```