longevity-db/mouse-muscle-aging-atlas-snRNAseq

Mouse Skeletal Muscle Aging Atlas (sn/scRNA-seq) Dataset

Dataset Overview

This dataset comprises single-nucleus and single-cell RNA sequencing (sn/scRNA-seq) data specifically focusing on the mouse skeletal muscle across different age groups. It serves as a valuable resource for investigating cell-type-specific gene expression changes and cellular composition shifts that occur during the aging process in a crucial mammalian model system.

The data was sourced from the Mouse Muscle Ageing Cell Atlas project, a comprehensive effort to map the aging process in skeletal muscle at single-cell resolution. This processed version has been converted into standardized .h5ad and .parquet formats, enhancing its usability for machine learning, bioinformatics pipelines, and enabling high-resolution insights into the molecular hallmarks of muscle aging.

Relevance to Aging and Longevity Research

Skeletal muscle undergoes significant functional and structural decline with age in mice, mirroring sarcopenia in humans. Mouse models are instrumental in longevity research, offering a controlled environment to study the intricate molecular and cellular mechanisms of aging. Insights gained from this dataset can inform human aging research and the development of interventions to promote healthy aging and extend healthspan.

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 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 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.

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Data Details

• Organism: Mus musculus (Mouse)
• Tissue: Skeletal Muscle
• Cell Types: Various (e.g., muscle stem cells, fibroblasts, immune cells, endothelial cells, myofibers). Specific cell type annotations should be found in cell_metadata.parquet.
• Technology: 10x Genomics scRNA-seq and snRNA-seq
• Condition: Healthy individuals across different age groups (e.g., young vs. old mice)
• Number of Cells: 96,529 (based on SKM_mouse_pp_cells2nuclei_2022-03-30.h5ad)
• Original AnnData File Used: SKM_mouse_pp_cells2nuclei_2022-03-30.h5ad (downloaded from Mouse Muscle Ageing Cell Atlas portal)

Primary Data Source Link: https://www.muscleageingcellatlas.org/mouse-pp/

Please refer to the original project website and associated publications for full details on experimental design, data collection, and initial processing.

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Dataset Structure

The dataset is provided in formats commonly used in single-cell genomics and tabular data analysis. After processing, the following files are generated:

• expression.parquet: A tabular representation of the gene expression matrix (adata.X), where rows are cells and columns are genes. Ideal for direct use as input features in machine learning models.
• gene_metadata.parquet: A tabular representation of the gene (feature) metadata (adata.var), providing details about each gene.
• cell_metadata.parquet: Contains comprehensive metadata for each cell (adata.obs), including donor information, potentially age, sex, and cell type annotations. This is crucial for labeling and grouping cells in ML tasks.
• pca_embeddings.parquet: Contains the data after Principal Component Analysis (PCA). This is a linear dimensionality reduction, where each row corresponds to a cell, and columns represent the principal components.
• pca_explained_variance.parquet: A table showing the proportion of variance explained by each principal component, useful for assessing the PCA's effectiveness.
• umap_embeddings.parquet: Contains the data after UMAP (Uniform Manifold Approximation and Projection). This is a non-linear dimensionality reduction, providing 2D or 3D embeddings excellent for visualization and capturing complex cell relationships. (This dataset utilizes pre-computed UMAP embeddings from the original .h5ad file if available).
• highly_variable_gene_metadata.parquet: Metadata specifically for genes identified as highly variable across cells during preprocessing. These genes often capture the most biological signal and are commonly used for dimensionality reduction and feature selection.
• gene_statistics.parquet: Basic statistics per gene, such as mean expression and the number of cells a gene is expressed in.

(Note: Cell type proportions and donor metadata aggregation were skipped in processing this specific dataset due to standard column names not being found in the original adata.obs. Please inspect cell_metadata.parquet for relevant columns to perform these analyses manually.)

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Data Cleaning and Processing

The data was accessed from the Mouse Muscle Ageing Cell Atlas project. The processing steps, performed using a Python script, are designed to prepare the data for machine learning and in-depth bioinformatics analysis:

1. AnnData Loading: The original .h5ad file was loaded into an AnnData object. Sparse expression matrices were converted to dense format for broader compatibility.
2. Pre-computed Embeddings Check: The script checks for and prioritizes existing PCA (X_pca) and UMAP (X_umap) embeddings within the .h5ad file's adata.obsm to leverage pre-processed results.
3. Mitochondrial Gene QC: Calculated the percentage of counts originating from mitochondrial genes (pct_counts_mt), a common quality control metric for single-cell data.
4. Highly Variable Gene (HVG) Identification: If not pre-identified, scanpy.pp.highly_variable_genes was used to identify a subset of genes (defaulting to the top 4000) that show significant biological variation. This subset is then used for efficient dimensionality reduction.
5. Principal Component Analysis (PCA): Performed on the (optionally scaled) highly variable gene expression data to generate pca_embeddings.parquet and pca_explained_variance.parquet.
6. UMAP Embeddings: The pre-computed UMAP embeddings from the adata.obsm were directly extracted and saved as umap_embeddings.parquet.
7. Metadata Extraction & Conversion: adata.obs (cell metadata) and adata.var (gene metadata) were extracted and saved as cell_metadata.parquet and gene_metadata.parquet respectively, with categorical columns converted to strings for Parquet compatibility.
8. Basic Gene Statistics: Calculated mean expression and number of cells expressed for each gene, saved to gene_statistics.parquet.

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Usage

This dataset is ideal for a variety of research and machine learning tasks in the context of mouse muscle aging and longevity:

Single-Cell Analysis

Explore cellular heterogeneity, identify novel cell states, and characterize gene expression patterns within the aging mouse skeletal muscle.

Aging & Longevity Research

• Investigate age-related changes in gene expression, cellular processes (e.g., inflammation, senescence, regeneration), and cellular composition within muscle.
• Identify molecular signatures that define "healthy" vs. "unhealthy" muscle aging phenotypes.
• Discover biomarkers or therapeutic targets for sarcopenia and other age-related muscle pathologies.

Machine Learning

• 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

This dataset is hosted on the Hugging Face Hub, allowing for easy programmatic download and loading of its component files.

import pandas as pd
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/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 Parquet files to download (matching ONLY the files you have available)
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"
]

# 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"])


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)


# 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
cell_type_column_name = 'cell_type' # <<<--- UPDATE THIS with the actual cell type column name
# --- 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.

Citation

Please ensure you cite the original source of the Mouse Muscle Ageing Cell Atlas data. Refer to the project's official website for the most up-to-date citation information for the atlas and its associated publications:

Mouse Muscle Ageing Cell Atlas Official Website: 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:

• Venkatachalam
• Pooja
• Albert

Curated on June 15, 2025.

Hugging Face Repository: https://huggingface.co/datasets/longevity-db/mouse-muscle-aging-atlas-snRNAseq