Venkatachalam commited on
Commit
b9521de
·
1 Parent(s): 1cb51b9

Initial commit: Add Mouse Glioblastoma snRNA-seq dataset

Browse files
README.md ADDED
@@ -0,0 +1,201 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ annotations_creators:
3
+ - found
4
+ language:
5
+ - en
6
+ license: cc-by-4.0 # Or the specific license provided by CELLxGENE for this collection
7
+ multilinguality: monolingual
8
+ pretty_name: Mouse Glioblastoma Atlas (snRNA-seq)
9
+ size_categories:
10
+ - 10K<n<100K # Based on 94,181 cells
11
+ source_datasets:
12
+ - original
13
+ tags:
14
+ - single-cell
15
+ - snRNA-seq
16
+ - mouse
17
+ - brain
18
+ - glioblastoma
19
+ - cancer
20
+ - aging
21
+ - neuro-oncology
22
+ - gene-expression
23
+ - pca
24
+ - umap
25
+ - dimensionality-reduction
26
+ - 10x-genomics
27
+ ---
28
+
29
+ # Mouse Glioblastoma Atlas (snRNA-seq) Dataset
30
+
31
+ ## Dataset Overview
32
+
33
+ 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.
34
+
35
+ 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.
36
+
37
+ ### Relevance to Aging and Longevity Research
38
+
39
+ 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.
40
+
41
+ This dataset offers an unprecedented opportunity to:
42
+ * Investigate **age-dependent molecular signatures** within glioblastoma cells and their associated brain microenvironment.
43
+ * Uncover how cellular processes like tumor progression, immune response, and cellular interactions differ between young and aged tumor contexts.
44
+ * Discover **biomarkers** or **therapeutic targets** that are specific to age-related glioblastoma, potentially leading to age-stratified treatments.
45
+ * Explore the contribution of brain aging to tumor susceptibility and progression.
46
+
47
+ 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.
48
+
49
+ ---
50
+
51
+ ## Data Details
52
+
53
+ * **Organism:** *Mus musculus* (Mouse)
54
+ * **Tissue:** Brain (Glioblastoma tumors and tumor microenvironment)
55
+ * **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`.*
56
+ * **Technology:** 10x Genomics 3' v3 snRNA-seq
57
+ * **Condition:** Glioblastoma tumors from both young and aged mice.
58
+ * **Number of Cells:** 94,181 (after initial filtering)
59
+ * **Original Data Source:** CELLxGENE Discover Collection: "Glioblastoma from young and aged mice".
60
+ * **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)
61
+ * **Associated GEO Accession (from CELLxGENE):** [GSE186252](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE186252)
62
+
63
+ ---
64
+
65
+ ## Dataset Structure
66
+
67
+ The dataset is provided in formats commonly used in single-cell genomics and tabular data analysis. After processing, the following files are generated:
68
+
69
+ * **`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.
70
+ * **`gene_metadata.parquet`**: A tabular representation of the gene (feature) metadata (`adata.var`), providing details about each gene.
71
+ * **`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.
72
+ * **`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.
73
+ * **`pca_explained_variance.parquet`**: A table showing the proportion of variance explained by each principal component, useful for assessing the PCA's effectiveness.
74
+ * **`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.
75
+ * **`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.
76
+ * **`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.
77
+
78
+ ---
79
+
80
+ ## Data Cleaning and Processing
81
+
82
+ 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:
83
+
84
+ 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.
85
+ 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.
86
+ 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.
87
+ 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.
88
+ 5. **Principal Component Analysis (PCA):** Performed on the scaled highly variable gene expression data to generate `pca_embeddings.parquet` and `pca_explained_variance.parquet`.
89
+ 6. **UMAP Embeddings:** UMAP was performed on the PCA embeddings to generate `umap_embeddings.parquet`, providing a non-linear 2D representation for visualization.
90
+
91
+ ---
92
+
93
+ ## Usage
94
+
95
+ This dataset is ideal for a variety of research and machine learning tasks in the context of age-related neuro-oncology:
96
+
97
+ ### Single-Cell Analysis
98
+ Explore cellular heterogeneity within glioblastoma tumors and their microenvironment, identifying novel cell states and expression patterns in both young and aged contexts.
99
+
100
+ ### Aging & Neuro-oncology Research
101
+ * Investigate age-dependent molecular shifts within glioblastoma cells and the immune/stromal cells of the tumor microenvironment.
102
+ * Identify biomarkers that correlate with age-related differences in tumor progression or response.
103
+ * Explore the impact of host aging on tumor cell plasticity and communication.
104
+
105
+ ### Machine Learning
106
+ * **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.
107
+ * **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.
108
+ * **Regression:** Predict specific age-related molecular or cellular properties within the tumor.
109
+ * **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.
110
+ * **Feature Selection:** Identify key genes or principal components relevant to glioblastoma subtypes or age-related tumor characteristics.
111
+
112
+ ### Direct Download and Loading from Hugging Face Hub
113
+ This dataset is hosted on the Hugging Face Hub, allowing for easy programmatic download and loading of its component files.
114
+
115
+ ```python
116
+ import pandas as pd
117
+ from huggingface_hub import hf_hub_download
118
+ import os
119
+
120
+ # Define the Hugging Face repository ID and the local directory for downloads
121
+ HF_REPO_ID = "longevity-db/mouse-glioblastoma-snRNAseq"
122
+ LOCAL_DATA_DIR = "downloaded_mouse_glioblastoma_data"
123
+
124
+ os.makedirs(LOCAL_DATA_DIR, exist_ok=True)
125
+ print(f"Created local download directory: {LOCAL_DATA_DIR}")
126
+
127
+ # List of Parquet files to download (matching the 8 files you generated)
128
+ parquet_files = [
129
+ "expression.parquet",
130
+ "gene_metadata.parquet",
131
+ "cell_metadata.parquet",
132
+ "pca_embeddings.parquet",
133
+ "pca_explained_variance.parquet",
134
+ "umap_embeddings.parquet",
135
+ "highly_variable_gene_metadata.parquet",
136
+ "gene_statistics.parquet"
137
+ ]
138
+
139
+ # Download each file
140
+ downloaded_paths = {}
141
+ for file_name in parquet_files:
142
+ try:
143
+ path = hf_hub_download(repo_id=HF_REPO_ID, filename=file_name, local_dir=LOCAL_DATA_DIR)
144
+ downloaded_paths[file_name] = path
145
+ print(f"Downloaded {file_name} to: {path}")
146
+ except Exception as e:
147
+ print(f"Warning: Could not download {file_name}. It might not be in the repository or its name differs. Error: {e}")
148
+
149
+ # Load core Parquet files into Pandas DataFrames
150
+ df_expression = pd.read_parquet(downloaded_paths["expression.parquet"])
151
+ df_pca_embeddings = pd.read_parquet(downloaded_paths["pca_embeddings.parquet"])
152
+ df_umap_embeddings = pd.read_parquet(downloaded_paths["umap_embeddings.parquet"])
153
+ df_cell_metadata = pd.read_parquet(downloaded_paths["cell_metadata.parquet"])
154
+ df_gene_metadata = pd.read_parquet(downloaded_paths["gene_metadata.parquet"])
155
+ df_pca_explained_variance = pd.read_parquet(downloaded_paths["pca_explained_variance.parquet"])
156
+ df_hvg_metadata = pd.read_parquet(downloaded_paths["highly_variable_gene_metadata.parquet"])
157
+ df_gene_stats = pd.read_parquet(downloaded_paths["gene_statistics.parquet"])
158
+
159
+
160
+ print("\n--- Data Loaded from Hugging Face Hub ---")
161
+ print("Expression data shape:", df_expression.shape)
162
+ print("PCA embeddings shape:", df_pca_embeddings.shape)
163
+ print("UMAP embeddings shape:", df_umap_embeddings.shape)
164
+ print("Cell metadata shape:", df_cell_metadata.shape)
165
+ print("Gene metadata shape:", df_gene_metadata.shape)
166
+ print("PCA explained variance shape:", df_pca_explained_variance.shape)
167
+ print("HVG metadata shape:", df_hvg_metadata.shape)
168
+ print("Gene statistics shape:", df_gene_stats.shape)
169
+
170
+
171
+ # Example: Prepare data for a classification/prediction model
172
+ # IMPORTANT: You need to inspect `df_cell_metadata.columns` to find the actual relevant columns.
173
+ print("\nAvailable columns in cell_metadata.parquet (df_cell_metadata.columns):")
174
+ print(df_cell_metadata.columns.tolist())
175
+ ```
176
+
177
+ -----
178
+
179
+ ## Citation
180
+
181
+ Please ensure you cite the original source of the Mouse Glioblastoma data from CELLxGENE.
182
+
183
+ **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.
184
+ **PMID:** 37400346
185
+ **DOI:** [10.1038/s41467-023-39434-2](https://www.google.com/search?q=https://doi.org/10.1038/s41467-023-39434-2)
186
+
187
+ **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)
188
+
189
+ If you use the `scanpy` library for any further analysis or preprocessing, please also cite Scanpy.
190
+
191
+ ## Contributions
192
+
193
+ This dataset was processed and prepared by:
194
+
195
+ - Venkatachalam
196
+ - Pooja
197
+ - Albert
198
+
199
+ *Curated on June 15, 2025.*
200
+
201
+ **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)
cell_metadata.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:335f1a04b742a00205df8acc2591965d993e76b6bcb10b628d06cc8913fef216
3
+ size 3039077
expression.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:460c5c40e4e9f6d5c8c4c5a5a1c592b3d6b5c5a44df95b8576ef7431cb1ecbec
3
+ size 1997848472
gene_metadata.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:39a76cdbcc284464ae9e8a42f62e198d91ba8543a198578f277102cc13b2bd19
3
+ size 1591156
gene_statistics.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9b0f439bb6056bc0cd18efcbc5da823a83c44a1f1ed8725cc9b7cfea8f3aa4fc
3
+ size 443408
highly_variable_gene_metadata.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3c46d81521a3295bd5baba9c84e7542a86dfd89cd6e4f7d60651a966af1b25ae
3
+ size 567129
pca_embeddings.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:dc7d7803376e83738b29f1db2d215bc2e97625891e9f0cb6dc4e28478912705f
3
+ size 29691111
pca_explained_variance.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:19b62392322c3b61c580b29501c30905827bd30fb9a325e6feb8c524734adb69
3
+ size 4049
umap_embeddings.parquet ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ea77735ab6cc587512d8b98becabcea70b7d96a605ff8043a6ec917b6ccf9e51
3
+ size 1974467