Version: v1.0 Release Date: 2024.7.27 Last Update Date: 2024.7.27
Introduction: SVD is a matrix factorization technique used to decompose a matrix into three other matrices, which represent the original data in a lower-dimensional space. In multi-omics analysis, SVD is particularly useful for identifying latent structures and relationships between different omics datasets. It helps in uncovering hidden patterns and features by decomposing the multi-omics data matrix into singular vectors and values. This can enhance the understanding of the interactions between different omics layers and improve the integration of heterogeneous data sources.
National Key Laboratory for Tropical Crop Breeding, Sanya Institute of Breeding and Multiplication,