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--- |
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license: mit |
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task_categories: |
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- tabular-regression |
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tags: |
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- biology |
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- genomics |
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pretty_name: "Decima Dataset" |
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size_categories: |
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- 10K<n<100K |
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--- |
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# decima-data |
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## Dataset Summary |
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This dataset contains associated metadata for use with the **Decima** model as well as model predictions for 8856 pseudobulks and 18457 genes. It includes observations across various tissues, organs, and disease states. The dataset is provided as an `AnnData` object including predictions, metadata and model performance metrics (Pearson correlation). |
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## Dataset Structure |
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The dataset consists of **8856 observations** (pseudobulks) and **18457 variables** (genes). |
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### Data Fields |
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Here is the complete README.md file for your dataset, ready to be uploaded to the Genentech/decima-data repository on Hugging Face. |
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Markdown |
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--- |
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license: mit |
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task_categories: |
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- tabular-regression |
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tags: |
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- biology |
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- genomics |
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- single-cell |
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pretty_name: "Decima Dataset" |
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size_categories: |
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- 1M<n<10M |
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--- |
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# decima-data |
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## Dataset Summary |
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This dataset contains gene expression data and associated genomic features formatted as an `AnnData` object. It is designed for use with the **gReLU** and **Decima** frameworks to support tasks such as gene expression prediction and genomic sequence modeling. The data provides a comprehensive view of expression across various tissues, organs, and disease states, primarily centered on human brain atlas data. |
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## Dataset Structure |
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The dataset is an `AnnData` object with dimensions: **8856 observations × 18457 variables**. |
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### Data Fields |
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**In `.obs` (Observation metadata):** |
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| Column | Description | |
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| :--- | :--- | |
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| `cell_type` | Specific cell type label | |
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| `tissue` | Tissue of origin | |
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| `organ` | Organ of origin | |
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| `disease` | Clinical status or condition (e.g., healthy) | |
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| `study` | Source study identifier | |
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| `dataset` | Source dataset identifier | |
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| `region` | Anatomical region | |
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| `subregion` | Specific anatomical subregion | |
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| `celltype_coarse` | Broad cell type classification | |
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| `n_cells` | Number of cells aggregated into the pseudobulk | |
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| `total_counts` | Total read count | |
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| `n_genes` | Number of genes detected | |
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| `size_factor` | Sum after normalization | |
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| `train_pearson` | Pearson correlation on training set | |
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| `val_pearson` | Pearson correlation on validation set | |
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| `test_pearson` | Pearson correlation on test set | |
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**In `.var` (Metadata for variables/genes):** |
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| Column | Description | |
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| :--- | :--- | |
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| `chrom` | Chromosome | |
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| `start` | Genomic start coordinate (hg38) | |
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| `end` | Genomic end coordinate (hg38) | |
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| `strand` | Genomic strand (+/-) | |
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| `gene_type` | Gene biotype (e.g., protein coding) | |
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| `frac_nan` | Fraction of missing values | |
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| `mean_counts` | Average expression counts | |
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| `n_tracks` | Number of pseudobulks expressing the gene | |
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| `gene_start` | Gene start position | |
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| `gene_end` | Gene end position | |
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| `gene_length` | Total length of the gene | |
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| `gene_mask_start` | Start of the gene mask in the input sequence | |
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| `gene_mask_end` | End of the gene mask in the input sequence | |
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| `frac_N` | Fraction of ambiguous bases (N) in the input | |
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| `fold` | Borzoi fold assignment | |
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| `dataset` | Split assignment (e.g., train, test) | |
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| `gene_id` | Ensembl gene identifier | |
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| `pearson` | Overall Pearson correlation | |
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| `size_factor_pearson` | Pearson correlation using size factor | |
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| `ensembl_canonical_tss` | Canonical Transcription Start Site | |
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### Data Layers |
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* **`.layers['preds']`**: Predicted values from the Decima model. |
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* **`.layers['v1_rep0']` through `.layers['v1_rep3']`**: Data/predictions across four model replicates. |
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## Usage |
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To use this dataset, ensure you have `anndata` and `huggingface_hub` installed: |
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```python |
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import anndata |
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from huggingface_hub import hf_hub_download |
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# Download from Genentech/decima-data |
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file_path = hf_hub_download( |
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repo_id="Genentech/decima-data", |
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repo_type="dataset", |
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filename="data.h5ad" |
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) |
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# Load into memory |
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ad = anndata.read_h5ad(file_path) |
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# Access expression data |
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print(ad.X.shape) |
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``` |
