MultiOmics

Multi-omics data analysis is a comprehensive approach that involves the integration and analysis of multiple types of biological data sets, often referred to as "omics" data. The term "omics" generally refers to large-scale data sets derived from various high-throughput technologies, such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics, among others. It aims to integrate diverse types of omics data to gain a more holistic and systems-level understanding of biological systems. By combining information from multiple omics layers, researchers can uncover complex relationships, identify regulatory networks, and gain a more comprehensive view of biological processes. MOFA (Multi-Omics Factor Analysis) is a computational framework and statistical method designed for the integration and analysis of multi-omics data. The purpose of MOFA is to unravel the underlying factors that explain the observed variations in multiple types of omics data. It allows researchers to identify common and specific patterns across different omics layers, providing insights into the biological processes and molecular mechanisms at play.