update

README.md

@@ -7,7 +7,7 @@ license: cc-by-4.0 # Or the specific license provided by the Muscle Ageing Cell
 multilinguality: monolingual
 pretty_name: Mouse Skeletal Muscle Aging Atlas (sn/scRNA-seq)
 size_categories:
-  - 100K<n<1M # Based on ~96K cells, this category fits
+  - 10K<n<100K # Based on ~96K cells, this category fits
 source_datasets:
   - original
 tags:
@@ -43,11 +43,11 @@ Skeletal muscle undergoes significant functional and structural decline with age
 This dataset offers an unprecedented opportunity to:
 * Identify **age-specific molecular signatures** within various skeletal muscle cell types (e.g., muscle stem cells, fibroblasts, immune cells).
 * Uncover how cellular processes like muscle regeneration, metabolism, inflammation, and cellular senescence change with age at the single-cell level.
-* Discover **biomarkers** or **therapeutic targets** for age-associated muscle decline.
+* Discover **biomarkers** or **therapeutic targets** related to age-associated muscle decline.
 * Investigate the contribution of different cell types to the overall aging process of skeletal muscle and their interplay.
 * Analyze shifts in cellular composition within the muscle tissue with advancing age.
 
-This dataset is therefore a powerful resource for understanding the intricate molecular mechanisms of aging within a vital mammalian tissue, with direct implications for longevity and healthspan research.
+This dataset thus serves as a powerful resource for understanding the intricate molecular mechanisms of aging within a vital mammalian tissue, with direct implications for longevity and healthspan research.
 
 ---
 
@@ -113,30 +113,29 @@ Explore cellular heterogeneity, identify novel cell states, and characterize gen
 * Discover biomarkers or therapeutic targets for sarcopenia and other age-related muscle pathologies.
 
 ### Machine Learning
-* **Clustering:** Apply clustering algorithms (e.g., K-Means, Leiden) on `pca_embeddings.parquet` or `umap_embeddings.parquet` to identify distinct cell populations or sub-populations.
+* **Clustering:** Apply clustering algorithms (e.g., K-Means, Louvain) on `pca_embeddings.parquet` or `umap_embeddings.parquet` to identify distinct cell populations or sub-populations.
 * **Classification:** Build models to classify cell types, age groups (e.g., young vs. old mice), or other relevant phenotypes using `pca_embeddings.parquet` or `umap_embeddings.parquet` as features. `cell_metadata.parquet` provides the necessary labels.
 * **Regression:** Predict the biological age of a cell or donor based on gene expression or derived features.
 * **Dimensionality Reduction & Visualization:** Use the PCA and UMAP embeddings for generating 2D or 3D plots to visualize complex cell relationships and age-related trends.
 * **Feature Selection:** Identify key genes or principal components relevant to muscle aging processes.
 
-### **Direct Download and Loading from Hugging Face Hub**
+### 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
 from huggingface_hub import hf_hub_download
 import os
-import anndata as ad # Needed if you download the .h5ad file
 
 # Define the Hugging Face repository ID and the local directory for downloads
-HF_REPO_ID = "longevity-db/your-new-mouse-muscle-dataset-name" # <<<--- REPLACE with your actual HF repo name
+HF_REPO_ID = "longevity-db/mouse-muscle-aging-atlas-snRNAseq"
 LOCAL_DATA_DIR = "downloaded_mouse_muscle_data"
 
 os.makedirs(LOCAL_DATA_DIR, exist_ok=True)
 print(f"Created local download directory: {LOCAL_DATA_DIR}")
 
-# List of files to download
-data_files = [
+# List of Parquet files to download (matching ONLY the files you have available)
+parquet_files = [
     "expression.parquet",
     "gene_metadata.parquet",
     "cell_metadata.parquet",
@@ -144,27 +143,29 @@ data_files = [
     "pca_explained_variance.parquet",
     "umap_embeddings.parquet",
     "highly_variable_gene_metadata.parquet",
-    "gene_statistics.parquet",
-    # Add the original .h5ad if you upload it
-    # "SKM_mouse_pp_cells2nuclei_2022-03-30.h5ad",
+    "gene_statistics.parquet"
 ]
 
 # Download each file
 downloaded_paths = {}
-for file_name in data_files:
+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. Error: {e}")
+        print(f"Warning: Could not download {file_name}. It might not be in the repository or its name differs. Error: {e}")
 
-# Load key Parquet files into Pandas DataFrames
+# 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"])
+
 
 print("\n--- Data Loaded from Hugging Face Hub ---")
 print("Expression data shape:", df_expression.shape)
@@ -172,11 +173,14 @@ 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)
 
 
 # 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:")
+print("\nAvailable columns in cell_metadata.parquet (df_cell_metadata.columns):")
 print(df_cell_metadata.columns.tolist())
 
 # --- USER ACTION REQUIRED ---
@@ -194,7 +198,7 @@ if age_column_name in df_cell_metadata.columns:
     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.")
+    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:
@@ -202,7 +206,7 @@ if cell_type_column_name in df_cell_metadata.columns:
     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.")
+    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.
 ```
@@ -214,6 +218,8 @@ Please ensure you cite the original source of the Mouse Muscle Ageing Cell Atlas
 **Mouse Muscle Ageing Cell Atlas Official Website:**
 [https://www.muscleageingcellatlas.org/mouse-pp/](https://www.muscleageingcellatlas.org/mouse-pp/)
 
+If you use the `scanpy` library for any further analysis or preprocessing, please also cite Scanpy.
+
 ## Contributions
 
 This dataset was processed and prepared by:
@@ -224,4 +230,4 @@ This dataset was processed and prepared by:
 
 *Curated on June 15, 2025.*
 
-**Hugging Face Repository:** [https://huggingface.co/datasets/longevity-db/your-new-mouse-muscle-dataset-name](https://www.google.com/search?q=https://huggingface.co/datasets/longevity-db/your-new-mouse-muscle-dataset-name) \# \<\<\<--- REPLACE with your actual HF repo URL
+**Hugging Face Repository:** [https://huggingface.co/datasets/longevity-db/mouse-muscle-aging-atlas-snRNAseq](https://huggingface.co/datasets/longevity-db/mouse-muscle-aging-atlas-snRNAseq)