Add dataset loading script to fix load_dataset functionality

skeletal_muscle_atlas.py

@@ -0,0 +1,250 @@
+"""
+HuggingFace Dataset Loading Script for Skeletal Muscle Aging Atlas
+
+This script defines how to load the skeletal muscle dataset parquet files
+into a structured HuggingFace dataset with multiple configurations.
+"""
+
+import datasets
+import pandas as pd
+from typing import Dict, List, Any
+
+
+# Dataset metadata
+_CITATION = """
+@article{kedlian2024human,
+  title={Human skeletal muscle aging atlas},
+  author={Kedlian, Veronika R and Wang, Yaning and Liu, Tianliang and Chen, Xiaoping and Bolt, Liam and Tudor, Catherine and Shen, Zhuojian and Fasouli, Eirini S and Prigmore, Elena and Kleshchevnikov, Vitalii and others},
+  journal={Nature Aging},
+  volume={4},
+  pages={727--744},
+  year={2024},
+  publisher={Nature Publishing Group},
+  doi={10.1038/s43587-024-00613-3},
+  url={https://www.nature.com/articles/s43587-024-00613-3}
+}
+"""
+
+_DESCRIPTION = """
+A comprehensive single-cell RNA-seq atlas of human skeletal muscle aging across the lifespan (15-75 years).
+This dataset provides 183,161 cells from 17 donors with gene expression, metadata, and pre-computed embeddings.
+"""
+
+_HOMEPAGE = "https://www.muscleageingcellatlas.org/"
+
+_LICENSE = "MIT"
+
+_URLS = {
+    "expression": "skeletal_muscle_10x_expression.parquet",
+    "sample_metadata": "skeletal_muscle_10x_sample_metadata.parquet", 
+    "feature_metadata": "skeletal_muscle_10x_feature_metadata.parquet",
+    "projection_pca": "skeletal_muscle_10x_projection_X_pca.parquet",
+    "projection_umap": "skeletal_muscle_10x_projection_X_umap.parquet",
+    "projection_tsne": "skeletal_muscle_10x_projection_X_tsne.parquet",
+    "projection_scvi": "skeletal_muscle_10x_projection_X_scVI.parquet",
+    "unstructured_metadata": "skeletal_muscle_10x_unstructured_metadata.json"
+}
+
+
+class SkeletalMuscleAtlasConfig(datasets.BuilderConfig):
+    """Configuration for Skeletal Muscle Atlas dataset."""
+    
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+class SkeletalMuscleAtlas(datasets.GeneratorBasedBuilder):
+    """Skeletal Muscle Aging Atlas dataset."""
+
+    BUILDER_CONFIGS = [
+        SkeletalMuscleAtlasConfig(
+            name="expression",
+            version=datasets.Version("1.0.0"),
+            description="Gene expression matrix (183,161 cells × 29,400 genes)",
+        ),
+        SkeletalMuscleAtlasConfig(
+            name="sample_metadata", 
+            version=datasets.Version("1.0.0"),
+            description="Cell-level metadata (age, cell type, sex, etc.)",
+        ),
+        SkeletalMuscleAtlasConfig(
+            name="feature_metadata",
+            version=datasets.Version("1.0.0"), 
+            description="Gene-level metadata (symbols, IDs, etc.)",
+        ),
+        SkeletalMuscleAtlasConfig(
+            name="projection_pca",
+            version=datasets.Version("1.0.0"),
+            description="PCA embeddings (50 components)",
+        ),
+        SkeletalMuscleAtlasConfig(
+            name="projection_umap",
+            version=datasets.Version("1.0.0"),
+            description="UMAP embeddings (2D visualization)",
+        ),
+        SkeletalMuscleAtlasConfig(
+            name="projection_tsne", 
+            version=datasets.Version("1.0.0"),
+            description="t-SNE embeddings (2D visualization)",
+        ),
+        SkeletalMuscleAtlasConfig(
+            name="projection_scvi",
+            version=datasets.Version("1.0.0"),
+            description="scVI embeddings (30D latent space)",
+        ),
+        SkeletalMuscleAtlasConfig(
+            name="all",
+            version=datasets.Version("1.0.0"),
+            description="All data combined (expression + metadata + embeddings)",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        if self.config.name == "expression":
+            # Dynamic features for expression matrix
+            features = datasets.Features({
+                "cell_id": datasets.Value("string"),
+                **{f"gene_{i}": datasets.Value("float32") for i in range(29400)}
+            })
+        elif self.config.name == "sample_metadata":
+            features = datasets.Features({
+                "cell_id": datasets.Value("string"),
+                "Age_group": datasets.Value("string"),
+                "age_numeric": datasets.Value("float32"),
+                "Sex": datasets.Value("string"),
+                "annotation_level0": datasets.Value("string"),
+                "annotation_level1": datasets.Value("string"),
+                "annotation_level2": datasets.Value("string"),
+                "DonorID": datasets.Value("string"),
+                "batch": datasets.Value("string"),
+                # Add other metadata columns as needed
+            })
+        elif self.config.name == "feature_metadata":
+            features = datasets.Features({
+                "gene_id": datasets.Value("string"),
+                "SYMBOL": datasets.Value("string"),
+                "ENSEMBL": datasets.Value("string"),
+                "n_cells": datasets.Value("int32"),
+            })
+        elif self.config.name.startswith("projection_"):
+            # Dynamic features for embeddings
+            if self.config.name == "projection_pca":
+                n_dims = 50
+            elif self.config.name == "projection_scvi":
+                n_dims = 30
+            else:  # umap, tsne
+                n_dims = 2
+            
+            features = datasets.Features({
+                "cell_id": datasets.Value("string"),
+                **{f"dim_{i}": datasets.Value("float32") for i in range(n_dims)}
+            })
+        else:  # "all" configuration
+            features = datasets.Features({
+                "cell_id": datasets.Value("string"),
+                "data_type": datasets.Value("string"),
+                "data": datasets.Value("string"),  # JSON string of the data
+            })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Download and prepare the data."""
+        
+        # Download all files
+        downloaded_files = dl_manager.download(_URLS)
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": downloaded_files,
+                    "config_name": self.config.name,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files: Dict[str, str], config_name: str):
+        """Generate examples based on the configuration."""
+        
+        if config_name == "expression":
+            # Load expression matrix
+            df = pd.read_parquet(files["expression"])
+            
+            for idx, (cell_id, row) in enumerate(df.iterrows()):
+                yield idx, {
+                    "cell_id": str(cell_id),
+                    **{f"gene_{i}": float(row.iloc[i]) for i in range(len(row))}
+                }
+                
+        elif config_name == "sample_metadata":
+            # Load sample metadata
+            df = pd.read_parquet(files["sample_metadata"])
+            
+            for idx, (cell_id, row) in enumerate(df.iterrows()):
+                yield idx, {
+                    "cell_id": str(cell_id),
+                    "Age_group": str(row.get("Age_group", "")),
+                    "age_numeric": float(row.get("age_numeric", 0.0)),
+                    "Sex": str(row.get("Sex", "")),
+                    "annotation_level0": str(row.get("annotation_level0", "")),
+                    "annotation_level1": str(row.get("annotation_level1", "")),
+                    "annotation_level2": str(row.get("annotation_level2", "")),
+                    "DonorID": str(row.get("DonorID", "")),
+                    "batch": str(row.get("batch", "")),
+                }
+                
+        elif config_name == "feature_metadata":
+            # Load feature metadata
+            df = pd.read_parquet(files["feature_metadata"])
+            
+            for idx, (gene_id, row) in enumerate(df.iterrows()):
+                yield idx, {
+                    "gene_id": str(gene_id),
+                    "SYMBOL": str(row.get("SYMBOL", "")),
+                    "ENSEMBL": str(row.get("ENSEMBL", "")),
+                    "n_cells": int(row.get("n_cells", 0)),
+                }
+                
+        elif config_name.startswith("projection_"):
+            # Load projection data
+            projection_key = config_name  # e.g., "projection_pca"
+            df = pd.read_parquet(files[projection_key])
+            
+            for idx, (cell_id, row) in enumerate(df.iterrows()):
+                yield idx, {
+                    "cell_id": str(cell_id),
+                    **{f"dim_{i}": float(row.iloc[i]) for i in range(len(row))}
+                }
+                
+        elif config_name == "all":
+            # Load all data types and provide a unified interface
+            sample_idx = 0
+            
+            # Expression data
+            expr_df = pd.read_parquet(files["expression"])
+            for cell_id, row in expr_df.iterrows():
+                yield sample_idx, {
+                    "cell_id": str(cell_id),
+                    "data_type": "expression",
+                    "data": row.to_json(),
+                }
+                sample_idx += 1
+            
+            # Sample metadata
+            meta_df = pd.read_parquet(files["sample_metadata"])
+            for cell_id, row in meta_df.iterrows():
+                yield sample_idx, {
+                    "cell_id": str(cell_id),
+                    "data_type": "sample_metadata", 
+                    "data": row.to_json(),
+                }
+                sample_idx += 1