VatsalPatel18/HNSCC-MultiOmics-10-Cancer-Hallmark-Gene-Network

HNSCC MultiOmics Cancer Gene Hallmark Network Patient Dataset

This dataset consists of network and adjacency matrix files related to head and neck squamous cell carcinoma (HNSCC) patient data. It is intended for research purposes in the field of cancer genomics and network analysis.

License

This dataset is available under the cc-by-nc-sa-4.0 License.

Dataset Files

There are three main files in this dataset:

1. hnscc.patient.chg.network.pth: This is a PyTorch dictionary where keys are patient IDs and values are data in PyTorch Geometric format. Each entry represents the network data associated with a specific patient.

2. hnsc.edges.npy: This file contains the adjacency matrix in NumPy format for cancer hallmark genesets. It represents the connectivity between different genes associated with cancer.

3. additional_data_file.extension: (Replace with actual file name and extension) Describe what this file contains and how it can be used.

Data Format

• The .pth file format is specific to PyTorch and should be used with PyTorch Geometric for loading the data.
• The .npy file is a NumPy binary format for storing arrays, suitable for loading with NumPy's load function.

Network Construction

In constructing the network for our Graph Attention Autoencoder, we utilized the Cancer Hallmark geneset, which comprises 2,784 genes. This geneset serves as the basis for building the graph’s nodes. The edges between these nodes are defined in a weighted manner, based on the shared pathways between gene pairs.

Specifically, the weight assigned to an edge connecting any two genes is determined by the number of pathways in which both genes are concurrently present. For example, if a pair of genes appears together in 5 different pathways, the edge connecting these two genes in our graph is assigned a weight of 5. This approach allows us to capture not only the dependent interactions between genes but also their independent behaviors.

Ideal gene-gene graph networks are constructed using the cancer hallmark geneset, resulting in 3,672,566 weighted edges among the 2,784 genes. This network construction was performed for each of the 430 patients in the TCGA-HNSCC cohort. A Graph Attention Autoencoder is trained on a dataset split, with 60% for training and the rest divided between validation and testing. This model achieves a validation cosine similarity of 0.835 and a test set cosine similarity of 0.8, measuring the similarity in the input multiomic features per node to recreated features. Latent features for each gene of every patient are extracted from the Graph Encoder, effectively reducing the gene dimensionality from R17 to R1, while encapsulating the influence of cancer hallmark pathways.

References

• Zhang D, Huo D, Xie H, Wu L, Zhang J, Liu L, Jin Q, Chen X. CHG: A Systematically Integrated Database of Cancer Hallmark Genes. Front Genet. 2020 Feb 5;11:29. doi: 10.3389/fgene.2020.00029. PMID: 32117445; PMCID: PMC7013921.

Downloading and Using the Data

To use this dataset, you will need Python installed along with PyTorch and PyTorch Geometric. You can install these packages using pip:

pip install torch torch-geometric numpy

Using the Dataset

Loading the Dataset

To load the dataset, you can use the following Python code:

import torch
import numpy as np

# Load the PyTorch dictionary
graph_data_dict = torch.load('path/to/hnscc.patient.chg.network.pth')

# Load the adjacency matrix
adjacency_matrix = np.load('path/to/hnsc.edges.npy')

# Example of accessing the data for a specific patient
patient_id = 'example_patient_id'  # Replace with an actual patient ID
patient_data = graph_data_dict[patient_id]

Data Preprocessing

Ensure that the gene expression data is standardized and robustly scaled to fall within the range of 0 to 1. The copy number alteration data should be linearly transformed from a discrete variable ranging from -2 to 2 to a continuous representation. Mutation types should be encoded in a binary format, with 1 indicating the presence of a mutation and 0 its absence. Methylation data should be maintained as continuous variables for six gene regions including the 1st exon, 3’UTR, 5’UTR, gene body, TSS1500, and TSS200.

Constructing the Network

The network is constructed using the Cancer Hallmark geneset, which includes 2,784 genes. The edges between these nodes are defined in a weighted manner, based on the shared pathways between gene pairs. The weight assigned to an edge is determined by the number of pathways in which both genes are concurrently present.

Training the Model

After loading and preprocessing the data, you can train the Graph Attention Autoencoder using the provided configuration and data loaders. The model is trained to achieve a validation cosine similarity of 0.835 and a test set cosine similarity of 0.8, measuring the similarity in the input multiomic features per node to recreated features.

Analysis and Visualization

You can perform various analyses and visualizations using the trained model. For example, you can extract latent features, cluster patients into distinct groups, and perform survival analysis. Detailed instructions and code examples for these tasks are provided in the README file of the associated repository.

Citation

If you use this dataset in your research, please cite the following paper:

Zhang D, Huo D, Xie H, Wu L, Zhang J, Liu L, Jin Q, Chen X. CHG: A Systematically Integrated Database of Cancer Hallmark Genes. Front Genet. 2020 Feb 5;11:29. doi: 10.3389/fgene.2020.00029. PMID: 32117445; PMCID: PMC7013921.