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

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)