| | --- |
| | 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 predictions and associated genomic features formatted as an `AnnData` object. It is designed for use with the **Decima** framework 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. |
| |
|
| | For more details, please refer to the original paper: https://www.biorxiv.org/content/10.1101/2024.10.09.617507v3. |
| |
|
| | ## Dataset Structure |
| | The dataset is an `AnnData` object with dimensions: **8,856 observations (pseudobulks) × 18,457 variables (genes)**. |
| |
|
| | ### 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']`**: Predictions from four model replicates. |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | import anndata |
| | from huggingface_hub import hf_hub_download |
| | |
| | file_path = hf_hub_download( |
| | repo_id="Genentech/decima-data", |
| | repo_type="dataset", |
| | filename="metadata.h5ad" |
| | ) |
| | |
| | adata = anndata.read_h5ad(file_path) |
| | ``` |
| |
|
