Commit
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b9521de
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Parent(s):
1cb51b9
Initial commit: Add Mouse Glioblastoma snRNA-seq dataset
Browse files- README.md +201 -0
- cell_metadata.parquet +3 -0
- expression.parquet +3 -0
- gene_metadata.parquet +3 -0
- gene_statistics.parquet +3 -0
- highly_variable_gene_metadata.parquet +3 -0
- pca_embeddings.parquet +3 -0
- pca_explained_variance.parquet +3 -0
- umap_embeddings.parquet +3 -0
README.md
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---
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annotations_creators:
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- found
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language:
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- en
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license: cc-by-4.0 # Or the specific license provided by CELLxGENE for this collection
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multilinguality: monolingual
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pretty_name: Mouse Glioblastoma Atlas (snRNA-seq)
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size_categories:
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- 10K<n<100K # Based on 94,181 cells
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source_datasets:
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- original
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tags:
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- single-cell
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- snRNA-seq
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- mouse
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- brain
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- glioblastoma
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- cancer
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- aging
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- neuro-oncology
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- gene-expression
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- pca
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- umap
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- dimensionality-reduction
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- 10x-genomics
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---
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# Mouse Glioblastoma Atlas (snRNA-seq) Dataset
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## Dataset Overview
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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.
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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.
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### Relevance to Aging and Longevity Research
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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.
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This dataset offers an unprecedented opportunity to:
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* Investigate **age-dependent molecular signatures** within glioblastoma cells and their associated brain microenvironment.
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* Uncover how cellular processes like tumor progression, immune response, and cellular interactions differ between young and aged tumor contexts.
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* Discover **biomarkers** or **therapeutic targets** that are specific to age-related glioblastoma, potentially leading to age-stratified treatments.
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* Explore the contribution of brain aging to tumor susceptibility and progression.
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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.
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---
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## Data Details
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* **Organism:** *Mus musculus* (Mouse)
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* **Tissue:** Brain (Glioblastoma tumors and tumor microenvironment)
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* **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`.*
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* **Technology:** 10x Genomics 3' v3 snRNA-seq
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* **Condition:** Glioblastoma tumors from both young and aged mice.
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* **Number of Cells:** 94,181 (after initial filtering)
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* **Original Data Source:** CELLxGENE Discover Collection: "Glioblastoma from young and aged mice".
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* **Direct `.h5ad` link:** [https://datasets.cellxgene.cziscience.com/8797c27c-6937-429e-818a-6f2bce18521a.h5ad](https://datasets.cellxgene.cziscience.com/8797c27c-6937-429e-818a-6f2bce18521a.h5ad)
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* **Associated GEO Accession (from CELLxGENE):** [GSE186252](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE186252)
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---
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## Dataset Structure
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The dataset is provided in formats commonly used in single-cell genomics and tabular data analysis. After processing, the following files are generated:
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* **`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.
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* **`gene_metadata.parquet`**: A tabular representation of the gene (feature) metadata (`adata.var`), providing details about each gene.
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* **`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.
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* **`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.
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* **`pca_explained_variance.parquet`**: A table showing the proportion of variance explained by each principal component, useful for assessing the PCA's effectiveness.
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* **`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.
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* **`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.
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* **`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.
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---
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## Data Cleaning and Processing
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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:
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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.
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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.
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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.
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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.
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5. **Principal Component Analysis (PCA):** Performed on the scaled highly variable gene expression data to generate `pca_embeddings.parquet` and `pca_explained_variance.parquet`.
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6. **UMAP Embeddings:** UMAP was performed on the PCA embeddings to generate `umap_embeddings.parquet`, providing a non-linear 2D representation for visualization.
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---
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## Usage
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This dataset is ideal for a variety of research and machine learning tasks in the context of age-related neuro-oncology:
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### Single-Cell Analysis
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Explore cellular heterogeneity within glioblastoma tumors and their microenvironment, identifying novel cell states and expression patterns in both young and aged contexts.
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### Aging & Neuro-oncology Research
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* Investigate age-dependent molecular shifts within glioblastoma cells and the immune/stromal cells of the tumor microenvironment.
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* Identify biomarkers that correlate with age-related differences in tumor progression or response.
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* Explore the impact of host aging on tumor cell plasticity and communication.
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### Machine Learning
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* **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.
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* **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.
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* **Regression:** Predict specific age-related molecular or cellular properties within the tumor.
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* **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.
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* **Feature Selection:** Identify key genes or principal components relevant to glioblastoma subtypes or age-related tumor characteristics.
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### Direct Download and Loading from Hugging Face Hub
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This dataset is hosted on the Hugging Face Hub, allowing for easy programmatic download and loading of its component files.
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```python
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import pandas as pd
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from huggingface_hub import hf_hub_download
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import os
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# Define the Hugging Face repository ID and the local directory for downloads
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HF_REPO_ID = "longevity-db/mouse-glioblastoma-snRNAseq"
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LOCAL_DATA_DIR = "downloaded_mouse_glioblastoma_data"
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os.makedirs(LOCAL_DATA_DIR, exist_ok=True)
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print(f"Created local download directory: {LOCAL_DATA_DIR}")
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# List of Parquet files to download (matching the 8 files you generated)
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parquet_files = [
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"expression.parquet",
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"gene_metadata.parquet",
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"cell_metadata.parquet",
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"pca_embeddings.parquet",
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"pca_explained_variance.parquet",
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"umap_embeddings.parquet",
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"highly_variable_gene_metadata.parquet",
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"gene_statistics.parquet"
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]
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# Download each file
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downloaded_paths = {}
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for file_name in parquet_files:
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try:
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path = hf_hub_download(repo_id=HF_REPO_ID, filename=file_name, local_dir=LOCAL_DATA_DIR)
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downloaded_paths[file_name] = path
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print(f"Downloaded {file_name} to: {path}")
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except Exception as e:
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print(f"Warning: Could not download {file_name}. It might not be in the repository or its name differs. Error: {e}")
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# Load core Parquet files into Pandas DataFrames
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df_expression = pd.read_parquet(downloaded_paths["expression.parquet"])
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df_pca_embeddings = pd.read_parquet(downloaded_paths["pca_embeddings.parquet"])
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df_umap_embeddings = pd.read_parquet(downloaded_paths["umap_embeddings.parquet"])
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df_cell_metadata = pd.read_parquet(downloaded_paths["cell_metadata.parquet"])
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df_gene_metadata = pd.read_parquet(downloaded_paths["gene_metadata.parquet"])
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df_pca_explained_variance = pd.read_parquet(downloaded_paths["pca_explained_variance.parquet"])
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df_hvg_metadata = pd.read_parquet(downloaded_paths["highly_variable_gene_metadata.parquet"])
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df_gene_stats = pd.read_parquet(downloaded_paths["gene_statistics.parquet"])
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print("\n--- Data Loaded from Hugging Face Hub ---")
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print("Expression data shape:", df_expression.shape)
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print("PCA embeddings shape:", df_pca_embeddings.shape)
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print("UMAP embeddings shape:", df_umap_embeddings.shape)
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print("Cell metadata shape:", df_cell_metadata.shape)
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print("Gene metadata shape:", df_gene_metadata.shape)
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print("PCA explained variance shape:", df_pca_explained_variance.shape)
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print("HVG metadata shape:", df_hvg_metadata.shape)
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print("Gene statistics shape:", df_gene_stats.shape)
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# Example: Prepare data for a classification/prediction model
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# IMPORTANT: You need to inspect `df_cell_metadata.columns` to find the actual relevant columns.
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print("\nAvailable columns in cell_metadata.parquet (df_cell_metadata.columns):")
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print(df_cell_metadata.columns.tolist())
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```
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-----
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## Citation
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Please ensure you cite the original source of the Mouse Glioblastoma data from CELLxGENE.
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**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.
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**PMID:** 37400346
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**DOI:** [10.1038/s41467-023-39434-2](https://www.google.com/search?q=https://doi.org/10.1038/s41467-023-39434-2)
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**Associated GEO Accession (from CELLxGENE):** [GSE186252](https://www.google.com/url?sa=E&source=gmail&q=https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE186252)
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If you use the `scanpy` library for any further analysis or preprocessing, please also cite Scanpy.
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## Contributions
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This dataset was processed and prepared by:
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- Venkatachalam
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- Pooja
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- Albert
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*Curated on June 15, 2025.*
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**Hugging Face Repository:** [https://huggingface.co/datasets/longevity-db/mouse-glioblastoma-snRNAseq](https://www.google.com/search?q=https://huggingface.co/datasets/longevity-db/mouse-glioblastoma-snRNAseq)
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cell_metadata.parquet
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oid sha256:335f1a04b742a00205df8acc2591965d993e76b6bcb10b628d06cc8913fef216
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size 3039077
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expression.parquet
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gene_metadata.parquet
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gene_statistics.parquet
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highly_variable_gene_metadata.parquet
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pca_embeddings.parquet
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pca_explained_variance.parquet
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umap_embeddings.parquet
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