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README.md

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

metadata.h5ad

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