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mouse-glioblastoma-snRNAseq

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Venkatachalam
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metadata
annotations_creators:
  - found
language:
  - en
license: cc-by-4.0
multilinguality: monolingual
pretty_name: Mouse Glioblastoma Atlas (snRNA-seq)
size_categories:
  - 10K<n<100K
source_datasets:
  - original
tags:
  - single-cell
  - snRNA-seq
  - mouse
  - brain
  - glioblastoma
  - cancer
  - aging
  - neuro-oncology
  - gene-expression
  - pca
  - umap
  - dimensionality-reduction
  - 10x-genomics

Mouse Glioblastoma Atlas (snRNA-seq) Dataset

Dataset Overview

This dataset comprises single-nucleus RNA sequencing (snRNA-seq) data from the brain (glioblastoma tumors and their microenvironment) of both young and aged mice. It provides a high-resolution cellular and molecular census of glioblastoma, a highly aggressive brain tumor, with crucial insights into its age-related characteristics.

The original data was sourced from a CELLxGENE Discover collection titled "Glioblastoma from young and aged mice." This processed version has been transformed from its original AnnData format into standardized .parquet files, enhancing its usability for machine learning, bioinformatics pipelines, and enabling detailed analysis of age-dependent changes in glioblastoma.

Relevance to Aging and Longevity Research

Glioblastoma incidence significantly increases with age, making it a prominent age-related cancer. Understanding how the tumor's cellular composition, gene expression programs, and microenvironment are modulated by age is critical for developing more effective therapies and understanding the biology of aging.

This dataset offers an unprecedented opportunity to:

  • Investigate age-dependent molecular signatures within glioblastoma cells and their associated brain microenvironment.
  • Uncover how cellular processes like tumor progression, immune response, and cellular interactions differ between young and aged tumor contexts.
  • Discover biomarkers or therapeutic targets that are specific to age-related glioblastoma, potentially leading to age-stratified treatments.
  • Explore the contribution of brain aging to tumor susceptibility and progression.

This dataset thus serves as a powerful resource for understanding the intricate molecular mechanisms of age-related brain cancer, with direct implications for neuro-oncology and aging research.

Data Details

  • Organism: Mus musculus (Mouse)
  • Tissue: Brain (Glioblastoma tumors and tumor microenvironment)
  • Cell Types: Various brain cell types, including tumor cells, immune cells (e.g., microglia, macrophages), astrocytes, oligodendrocytes, and neuronal cells. Specific cell type annotations should be found in cell_metadata.parquet.
  • Technology: 10x Genomics 3' v3 snRNA-seq
  • Condition: Glioblastoma tumors from both young and aged mice.
  • Number of Cells: 94,181 (after initial filtering)
  • Original Data Source: CELLxGENE Discover Collection: "Glioblastoma from young and aged mice".

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 (normalized and log-transformed 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 (e.g., mouse ID, age), disease status, 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.
  • 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 (of log-normalized data) and the number of cells a gene is expressed in.

Data Cleaning and Processing

The raw data was sourced as a pre-processed .h5ad file from CELLxGENE. 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. Any sparse expression matrices were converted to dense format for broader compatibility.
  2. Basic Quality Control (QC): Cells with fewer than 200 genes and genes expressed in fewer than 3 cells were filtered out to remove low-quality data.
  3. Normalization and Log-transformation: Total counts per cell were normalized to a target sum of 10,000, and then the data was log-transformed.
  4. Highly Variable Gene (HVG) Identification: scanpy.pp.highly_variable_genes was used to identify a subset of highly variable genes (top 4000) that capture most of the biological signal. This subset is then used for efficient dimensionality reduction.
  5. Principal Component Analysis (PCA): Performed on the scaled highly variable gene expression data to generate pca_embeddings.parquet and pca_explained_variance.parquet.
  6. UMAP Embeddings: UMAP was performed on the PCA embeddings to generate umap_embeddings.parquet, providing a non-linear 2D representation for visualization.

Usage

This dataset is ideal for a variety of research and machine learning tasks in the context of age-related neuro-oncology:

Single-Cell Analysis

Explore cellular heterogeneity within glioblastoma tumors and their microenvironment, identifying novel cell states and expression patterns in both young and aged contexts.

Aging & Neuro-oncology Research

  • Investigate age-dependent molecular shifts within glioblastoma cells and the immune/stromal cells of the tumor microenvironment.
  • Identify biomarkers that correlate with age-related differences in tumor progression or response.
  • Explore the impact of host aging on tumor cell plasticity and communication.

Machine Learning

  • Clustering: Apply clustering algorithms (e.g., K-Means, Louvain) on pca_embeddings.parquet or umap_embeddings.parquet to identify distinct cell populations (tumor cells, immune cells, etc.) and age-specific subpopulations.
  • Classification: Build models to classify cell types, distinguish between tumor and non-tumor cells, or predict the age group (young vs. aged) of the mouse donor based on cellular features. cell_metadata.parquet provides the necessary labels.
  • Regression: Predict specific age-related molecular or cellular properties within the tumor.
  • Dimensionality Reduction & Visualization: Use the PCA and UMAP embeddings for generating 2D or 3D plots to visualize complex cell relationships and age-related trends within the glioblastoma.
  • Feature Selection: Identify key genes or principal components relevant to glioblastoma subtypes or age-related tumor characteristics.

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-glioblastoma-snRNAseq"
LOCAL_DATA_DIR = "downloaded_mouse_glioblastoma_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 the 8 files you generated)
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 a classification/prediction model
# IMPORTANT: You need to inspect `df_cell_metadata.columns` to find the actual relevant columns.
print("\nAvailable columns in cell_metadata.parquet (df_cell_metadata.columns):")
print(df_cell_metadata.columns.tolist())

Citation

Please ensure you cite the original source of the Mouse Glioblastoma data from CELLxGENE.

Original Publication: Darmanis, S., Sloan, S. A., et al. (2023). "Transcriptional programs of glioblastoma subclasses are preserved in the tumor microenvironment." Nature Communications, 14(1), 3848. PMID: 37400346 DOI: 10.1038/s41467-023-39434-2

Associated GEO Accession (from CELLxGENE): GSE186252

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-glioblastoma-snRNAseq