README.md

---
annotations_creators:
  - found

language:
  - en

license: cc-by-4.0 # Or the specific license of the Muscle Aging Atlas data if different

multilinguality: monolingual

pretty_name: Human Skeletal Muscle Aging Atlas (sn/scRNA-seq)

size_categories:
  - 100K<n<1M # Based on 183,161 cells, adjust if your downloaded file varies

source_datasets:
  - original

tags:
  - single-cell
  - single-nucleus
  - snRNA-seq
  - scRNA-seq
  - human
  - skeletal-muscle
  - aging
  - longevity
  - cell-atlas
  - anndata
  - gene-expression
  - pca
  - umap
  - dimensionality-reduction
  - cell-types
  - biomarkers
---

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

## **1. Data Overview**

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

The original data was sourced from the Human Skeletal Muscle Aging Atlas project, which aims to provide a comprehensive cellular and molecular map of skeletal muscle aging. This processed version has been transformed 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, a process known as sarcopenia, which contributes to frailty, loss of independence, and reduced quality of life in older adults. Understanding the cellular and molecular basis of muscle aging is crucial for developing 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, endothelial 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 (sarcopenia) and related conditions.
- **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 advancing our understanding of the intricate molecular mechanisms of aging within a vital human tissue, with direct implications for longevity and healthspan research.

## **2. Source**

The original data for this processed dataset originates from the Human Skeletal Muscle Aging Atlas, a comprehensive single-cell/nucleus RNA sequencing study of human skeletal muscle.

**Organism:** Homo sapiens (Human)  
**Tissue:** Skeletal Muscle  
**Cell Types:** Various (e.g., muscle stem cells, fibroblasts, immune cells, endothelial cells, myofibers)  
**Technology:** 10x Genomics scRNA-seq and snRNA-seq  
**Condition:** Healthy individuals across different age groups (Young, Old)  
**Number of Cells:** ~183,161 (based on `SKM_human_pp_cells2nuclei_2023-06-22.h5ad`, may vary by specific file)  
**Original AnnData File Used (Example):** `SKM_human_pp_cells2nuclei_2023-06-22.h5ad` (downloaded from Human Skeletal Muscle Aging Atlas portal)

**Primary Data Source Link:**
[https://www.muscleageingcellatlas.org/human-pp/](https://www.muscleageingcellatlas.org/human-pp/)

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

## **3. Transformations**

The data has undergone the following transformations, designed to prepare it for machine learning and in-depth bioinformatics analysis:

1.  **AnnData Loading:** The original `.h5ad` file was loaded into an AnnData object, a standard format for single-cell data in Python. Sparse matrices were converted to dense for broader compatibility.
2.  **Expression Data Extraction and Conversion:** The `adata.X` matrix (gene expression counts/values for each cell) was extracted and saved as `expression.parquet`. This serves as the primary input for gene-level analyses.
3.  **Feature/Gene Metadata Extraction and Conversion:** The `adata.var` DataFrame (containing metadata about each gene/feature) was extracted and saved as `gene_metadata.parquet`.
4.  **Observation/Cell Metadata Extraction and Conversion:** The `adata.obs` DataFrame (containing comprehensive metadata about each cell, such as cell type annotations, donor age, sex, and sample information) was extracted and saved as `cell_metadata.parquet`. Categorical columns were converted to string for better Parquet compatibility.
5.  **Highly Variable Gene (HVG) Identification and Subsetting:**
    * The script first checks if HVGs are already marked in `adata.var`.
    * If not, `scanpy.pp.highly_variable_genes` is used to identify genes that show significant variation across cells, which are often the most biologically interesting.
    * For dimensionality reduction (PCA and UMAP), the AnnData object is **subsetted to only these highly variable genes** (`adata_for_dr`), significantly reducing memory usage and computational time while preserving biological signal.
6.  **Principal Component Analysis (PCA):**
    * The script checks for existing `X_pca` embeddings in `adata.obsm`; if found and sufficient, they are used directly.
    * Otherwise, PCA is performed on the (optionally scaled) highly variable gene expression data to reduce dimensionality.
    * The resulting PCA embeddings are saved as `pca_embeddings.parquet`.
    * The explained variance ratio for each component is saved as `pca_explained_variance.parquet`.
7.  **UMAP (Uniform Manifold Approximation and Projection):**
    * The script checks for existing `X_umap` embeddings in `adata.obsm`; if found, they are used directly.
    * Otherwise, UMAP is performed, typically on the PCA embeddings (or HVG expression data if PCA was skipped), to generate a low-dimensional (e.g., 2D or 3D) non-linear representation of the data.
    * The UMAP embeddings are saved as `umap_embeddings.parquet`.
8.  **Basic Gene Statistics:**
    * Calculates and saves `mean_expression` and `n_cells_expressed` for each gene to `gene_statistics.parquet`, providing basic insights into gene prevalence.
9.  **Cell Type Proportion Analysis:**
    * Calculates the overall proportions of different cell types (`cell_type_proportions_overall.parquet`).
    * Calculates cell type proportions grouped by a key metadata column (e.g., 'Age', 'donor_id') if available, saving to `cell_type_proportions_by_{grouping_column}.parquet`. This is vital for studying age-related cellular compositional changes.
10. **Sample/Donor Metadata Aggregation:**
    * Aggregates key metadata (e.g., Age, Sex) at the donor or sample level into `donor_metadata.parquet`, useful for population-level analyses.

## **4. Contents**

-   **`expression.parquet`**: Contains the full gene expression matrix (Cells x Genes). Each row represents a cell (observation), and each column represents a gene (feature).
-   **`gene_metadata.parquet`**: Contains metadata about each gene, such as gene symbols, Ensembl IDs, and a 'highly_variable' flag.
-   **`cell_metadata.parquet`**: Contains comprehensive metadata for each cell, including (but not limited to) inferred cell types, donor ID, age, sex, and experimental batch information. This is crucial for labeling and grouping cells in ML tasks.
-   **`pca_embeddings.parquet`**: Contains the data after linear dimensionality reduction using PCA. Each row corresponds to a cell, and columns represent the principal components (e.g., PC1, PC2, ...). Ideal as features for ML models and for linear visualizations.
-   **`pca_explained_variance.parquet`**: A table showing the proportion of variance explained by each principal component. Useful for determining the optimal number of components to retain.
-   **`umap_embeddings.parquet`**: Contains the data after non-linear dimensionality reduction using UMAP. Each row corresponds to a cell, and columns represent the UMAP coordinates (e.g., UMAP1, UMAP2). Excellent for visualization of cell clusters and relationships.
-   **`highly_variable_gene_metadata.parquet`**: Metadata specifically for genes identified as highly variable across cells. Useful for feature selection in ML models. (Only generated if HVGs are found/computed).
-   **`gene_statistics.parquet`**: Basic statistics per gene, such as mean expression and the number of cells a gene is expressed in.
-   **`cell_type_proportions_overall.parquet`**: A table showing the overall proportion of each cell type across the entire dataset.
-   **`cell_type_proportions_by_{grouping_column}.parquet`**: (e.g., `cell_type_proportions_by_Age.parquet`) Tables showing cell type proportions broken down by a key grouping variable like donor age or donor ID. Crucial for studying age-related cellular shifts.
-   **`donor_metadata.parquet`**: Aggregated metadata at the donor/sample level, containing unique donor IDs and associated information like age and sex.

## **5. Usage**

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

### **Single-Cell Analysis**
Exploring cellular heterogeneity, identifying novel cell states, and characterizing gene expression patterns within the aging skeletal muscle.

### **Aging & Longevity Research**
-   Investigating age-related changes in gene expression, cellular processes (e.g., inflammation, senescence, regeneration), and cellular composition within muscle.
-   Identifying molecular signatures that define "healthy" vs. "unhealthy" muscle aging.
-   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.
-   **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.

```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).")
    df_donor_metadata = None


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)
if df_donor_metadata is not None:
    print("Donor metadata shape:", df_donor_metadata.shape)
```

## **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:

**Human Skeletal Muscle Aging Atlas Official Website:**
[https://www.muscleageingcellatlas.org/](https://www.muscleageingcellatlas.org/)

If you use the `scanpy` library for any further analysis or preprocessing, please also cite Scanpy.

## **7. Contributions**

This dataset was processed and prepared by:
- Venkatachalam
- Pooja
- Albert

*Curated on June 15, 2025.*