update

README.md

@@ -131,45 +131,119 @@ Exploring cellular heterogeneity, identifying novel cell states, and characteriz
 -   Discovering biomarkers or therapeutic targets for sarcopenia and other age-related muscle pathologies.
 
 ### **Machine Learning**
--   **Clustering:** Applying clustering algorithms (e.g., K-Means, Leiden) on `pca_embeddings.parquet` or `umap_embeddings.parquet` to identify distinct cell populations or sub-populations.
+-   **Clustering:** Applying clustering algorithms (e.g., K-Means, Louvain) on `pca_embeddings.parquet` or `umap_embeddings.parquet` to identify distinct cell populations or sub-populations.
 -   **Classification:** Building models to classify cell types, age groups (e.g., young vs. old), or disease states (if available) using `pca_embeddings.parquet` or `umap_embeddings.parquet` as features and `cell_metadata.parquet` for labels.
 -   **Regression:** Predicting the biological age of a cell or donor based on gene expression or cell type composition.
 -   **Dimensionality Reduction & Visualization:** Using the PCA and UMAP embeddings for generating 2D or 3D plots to visualize complex cell relationships and age-related trends.
 -   **Feature Selection:** Identifying key genes or principal components relevant to muscle aging processes.
 
-These Parquet files can be easily loaded into Pandas DataFrames in Python, or into other data analysis environments that support the Parquet format.
+### **Direct Download and Loading from Hugging Face Hub**
+This dataset is hosted on the Hugging Face Hub, allowing for easy programmatic download and loading of its component files.
 
 ```python
 import pandas as pd
-
-# Load expression data
-df_expression = pd.read_parquet("human_muscle_aging_atlas_ml_data/expression.parquet")
-
-# Load PCA embeddings
-df_pca_embeddings = pd.read_parquet("human_muscle_aging_atlas_ml_data/pca_embeddings.parquet")
-
-# Load UMAP embeddings
-df_umap_embeddings = pd.read_parquet("human_muscle_aging_atlas_ml_data/umap_embeddings.parquet")
-
-# Load cell metadata
-df_cell_metadata = pd.read_parquet("human_muscle_aging_atlas_ml_data/cell_metadata.parquet")
-
-# Load donor metadata (if available)
-try:
-    df_donor_metadata = pd.read_parquet("human_muscle_aging_atlas_ml_data/donor_metadata.parquet")
-except FileNotFoundError:
-    print("Donor metadata file not found (might not be generated if donor IDs were not clear).")
+from huggingface_hub import hf_hub_download
+import os
+
+# Define the Hugging Face repository ID and the local directory for downloads
+HF_REPO_ID = "longevity-db/human-muscle-aging-atlas-snRNAseq" # THIS IS YOUR NEW REPO ID
+LOCAL_DATA_DIR = "downloaded_human_muscle_data"
+
+os.makedirs(LOCAL_DATA_DIR, exist_ok=True)
+print(f"Created local download directory: {LOCAL_DATA_DIR}")
+
+# List of Parquet files to download (matching what your processing script outputs)
+parquet_files = [
+    "expression.parquet",
+    "gene_metadata.parquet",
+    "cell_metadata.parquet",
+    "pca_embeddings.parquet",
+    "pca_explained_variance.parquet",
+    "umap_embeddings.parquet",
+    "highly_variable_gene_metadata.parquet",
+    "gene_statistics.parquet",
+    "cell_type_proportions_overall.parquet",
+    "donor_metadata.parquet"
+    # Note: cell_type_proportions_by_{grouping_column}.parquet might have a dynamic name,
+    # so users might need to download it separately or infer its name.
+]
+
+# Download each file
+downloaded_paths = {}
+for file_name in parquet_files:
+    try:
+        path = hf_hub_download(repo_id=HF_REPO_ID, filename=file_name, local_dir=LOCAL_DATA_DIR)
+        downloaded_paths[file_name] = path
+        print(f"Downloaded {file_name} to: {path}")
+    except Exception as e:
+        print(f"Warning: Could not download {file_name}. It might not be in the repository or its name differs. Error: {e}")
+
+# Load core Parquet files into Pandas DataFrames
+df_expression = pd.read_parquet(downloaded_paths["expression.parquet"])
+df_pca_embeddings = pd.read_parquet(downloaded_paths["pca_embeddings.parquet"])
+df_umap_embeddings = pd.read_parquet(downloaded_paths["umap_embeddings.parquet"])
+df_cell_metadata = pd.read_parquet(downloaded_paths["cell_metadata.parquet"])
+df_gene_metadata = pd.read_parquet(downloaded_paths["gene_metadata.parquet"])
+df_pca_explained_variance = pd.read_parquet(downloaded_paths["pca_explained_variance.parquet"])
+df_hvg_metadata = pd.read_parquet(downloaded_paths["highly_variable_gene_metadata.parquet"])
+df_gene_stats = pd.read_parquet(downloaded_paths["gene_statistics.parquet"])
+df_cell_type_proportions_overall = pd.read_parquet(downloaded_paths["cell_type_proportions_overall.parquet"])
+try: # Donor metadata might be skipped if no column found, so use try-except
+    df_donor_metadata = pd.read_parquet(downloaded_paths["donor_metadata.parquet"])
+except KeyError:
     df_donor_metadata = None
 
 
+print("\n--- Data Loaded from Hugging Face Hub ---")
 print("Expression data shape:", df_expression.shape)
 print("PCA embeddings shape:", df_pca_embeddings.shape)
 print("UMAP embeddings shape:", df_umap_embeddings.shape)
 print("Cell metadata shape:", df_cell_metadata.shape)
+print("Gene metadata shape:", df_gene_metadata.shape)
+print("PCA explained variance shape:", df_pca_explained_variance.shape)
+print("HVG metadata shape:", df_hvg_metadata.shape)
+print("Gene statistics shape:", df_gene_stats.shape)
+print("Overall cell type proportions shape:", df_cell_type_proportions_overall.shape)
 if df_donor_metadata is not None:
     print("Donor metadata shape:", df_donor_metadata.shape)
+
+
+# Example: Prepare data for an age prediction model
+# IMPORTANT: You need to inspect `df_cell_metadata.columns` to find the actual age and cell type columns.
+print("\nAvailable columns in cell_metadata.parquet (df_cell_metadata.columns):")
+print(df_cell_metadata.columns.tolist())
+
+# --- USER ACTION REQUIRED ---
+# Replace 'your_age_column_name' and 'your_cell_type_column_name'
+# with the actual column names found in your df_cell_metadata.columns output.
+# Common names might be 'age', 'Age_Group', 'Age_in_weeks', 'cell_type_annotation', 'CellType' etc.
+age_column_name = 'Age' # <<<--- UPDATE THIS with the actual age column name found in your data (e.g., 'Age', 'age_group', 'Age_in_months')
+cell_type_column_name = 'cell_type' # <<<--- UPDATE THIS with the actual cell type column name (e.g., 'cell_type_annotation', 'CellType')
+# --- END USER ACTION REQUIRED ---
+
+
+# Example: Using age for a prediction task
+if age_column_name in df_cell_metadata.columns:
+    X_features_age_prediction = df_pca_embeddings # Or df_umap_embeddings, or df_expression (if manageable)
+    y_labels_age_prediction = df_cell_metadata[age_column_name]
+    print(f"\nPrepared X (features) for age prediction with shape {X_features_age_prediction.shape} and y (labels) with shape {y_labels_age_prediction.shape}")
+else:
+    print(f"\nWarning: Column '{age_column_name}' not found in cell metadata for age prediction example. Please check your data.")
+
+# Example: Using cell type for a classification task
+if cell_type_column_name in df_cell_metadata.columns:
+    X_features_cell_type = df_pca_embeddings # Or df_umap_embeddings, or df_expression
+    y_labels_cell_type = df_cell_metadata[cell_type_column_name]
+    print(f"Prepared X (features) for cell type classification with shape {X_features_cell_type.shape} and y (labels) with shape {y_labels_cell_type.shape}")
+else:
+    print(f"Warning: Column '{cell_type_column_name}' not found in cell metadata for cell type classification example. Please check your data.")
+
+# This data can then be split into train/test sets and used to train various ML models.
 ```
 
+
+-----
+
 ## **6. Citation**
 
 Please ensure you cite the original source of the Human Skeletal Muscle Aging Atlas data. Refer to the project's official website for the most up-to-date citation information for the atlas and its associated publications:
@@ -182,8 +256,11 @@ If you use the `scanpy` library for any further analysis or preprocessing, pleas
 ## **7. Contributions**
 
 This dataset was processed and prepared by:
-- Venkatachalam
-- Pooja
-- Albert
 
-*Curated on June 15, 2025.*
+  - Venkatachalam
+  - Pooja
+  - Albert
+
+*Curated on June 15, 2025.*
+
+**Hugging Face Repository:** [https://huggingface.co/datasets/longevity-db/human-muscle-aging-atlas-snRNAseq](https://huggingface.co/datasets/longevity-db/human-muscle-aging-atlas-snRNAseq)