Integrating Multi-Omics Data for Advanced Clustering Analysis

Introduction

The era of multi-omics data brings exciting opportunities for deeper insights into biological processes. In one of my course projects, I explored how to integrate diverse data types such as gene expression, DNA methylation, and miRNA expression to perform clustering analysis and uncover meaningful patterns.

The Process

1. Data Integration: Combined three distinct omics datasets to create a comprehensive view of biological systems.
2. Dimensionality Reduction: Reduced the high-dimensional data into manageable representations using neural network models.
3. Clustering and Evaluation: Validated clustering results through survival analysis to assess biological significance.

Takeaways

This project enhanced my understanding of multi-omics data and demonstrated the power of machine learning in uncovering hidden insights.