Single-Cell RNA Sequencing Analysis Using Machine Learning

Project Overview

This project is a comprehensive exploration of single-cell RNA sequencing (scRNA-seq) data using advanced machine learning techniques. The goal is to analyze and interpret high-dimensional scRNA-seq data to uncover cellular heterogeneity and underlying biological processes. By applying dimensionality reduction techniques and graph-based models, we aim to provide insights into the complex structure of gene expression data at a single-cell level.

Project Structure

• data/: Contains the raw and processed scRNA-seq datasets used for analysis.
• src/: Pre-trained models and scripts for training Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT).
• visual_data/: Contains visualizations, model performance metrics, and other outputs from the analysis.
• README.md: Overview of the project, structure, and instructions for running the analysis.

Data Description

The scRNA-seq data was obtained from the National Center for Biotechnology Information’s Gene Expression Omnibus (GEO) with accession number GSE86469. The dataset includes gene expression profiles across various experimental conditions, providing a rich resource for understanding cellular diversity.

Methodology

1. Data Preprocessing:

   • Normalization, quality control, and feature selection were performed using Python scripts to prepare the data for analysis.

2. Dimensionality Reduction:

   • Techniques like PCA, t-SNE, and UMAP were applied via Python scripts to reduce the high-dimensional data and visualize the structure of the data.

3. Graph-Based Models:

   • Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) were implemented in Python to model the data as graphs, capturing complex relationships between cells.

4. Visualization and Interpretation:

   • The results were visualized using Python libraries such as Plotly, providing insights into the clustering and differentiation of cell types.

Results

The project successfully demonstrated the application of advanced machine learning techniques to scRNA-seq data. The graph-based models, combined with dimensionality reduction, revealed distinct clusters corresponding to different cell types, providing a deeper understanding of cellular heterogeneity.

Requirements

• Python 3.x
• Numpy
• Pandas
• Scikit-learn
• Torch
• PyTorch Geometric
• Plotly
• Scanpy

How to Run

1. Clone the repository:

   git clone https://github.com/MusaibNagani/Single-Cell-RNA-Sequencing.git