Fix README formatting and loading code

- Remove duplicate/malformed YAML frontmatter
- Fix filename: data.h5ad -> metadata.h5ad (actual file)
- Fix variable name collision (ad -> adata)
- Add paper reference
- Clean up formatting

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

README.md

@@ -1,30 +1,4 @@
 ---
-# 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
@@ -40,10 +14,12 @@ size_categories:
 # 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.
+This dataset contains gene expression data 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: **8856 observations × 18457 variables**.
+The dataset is an `AnnData` object with dimensions: **8,856 observations (pseudobulks) × 18,457 variables (genes)**.
 
 ### Data Fields
 
@@ -95,26 +71,19 @@ The dataset is an `AnnData` object with dimensions: **8856 observations × 18457
 
 ### Data Layers
 * **`.layers['preds']`**: Predicted values from the Decima model.
-* **`.layers['v1_rep0']` through `.layers['v1_rep3']`**: Data/predictions across four model replicates.
+* **`.layers['v1_rep0']` through `.layers['v1_rep3']`**: Predictions from 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"
+    filename="metadata.h5ad"
 )
 
-# Load into memory
-ad = anndata.read_h5ad(file_path)
-
-# Access expression data
-print(ad.X.shape)
-```
+adata = anndata.read_h5ad(file_path)
+```