Masterclass Tensor Decompositions and Applications in Multi-Omics Data Analysis

Masterclass Tensor Decompositions and Applications in Multi-Omics Data Analysis

Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have revolutionized biological research by enabling comprehensive, high-throughput analysis of molecular components within cells and organisms. The resulting high-dimensional datasets pose significant analytical challenges, particularly in integrating diverse data types and uncovering complex biological relationships. Tensor-based approaches have emerged as powerful tools for analyzing these high-dimensional omics data, offering advantages over traditional matrix-based methods in capturing complex, multi-way relationships.

This masterclass delves into tensor decomposition techniques and their applications in omics data analysis. Participants will explore the mathematical foundations of tensors, key methods such as CP (CANDECOMP/PARAFAC), Tucker, and tensor train decompositions, as well as probabilistic decompositions. We discuss how tensors can naturally represent multi-dimensional omics datasets and how tensor factorization methods enable dimensionality reduction while preserving important structural information. A comprehensive biological background and an overview of relevant public databases and resources are provided to contextualize the computational methods. Case studies will illustrate the application of tensor methods for tasks such as identifying biological patterns, integrating multiple types of omics data, imputing missing values, and uncovering cell-cell interaction patterns, while demonstrating their impact in systems biology, cancer research, personalized medicine, and drug discovery. Challenges and future directions for tensor-based omics data analysis will also be discussed.

By the end of this masterclass, participants will grasp tensor decomposition techniques, gain practical experience with omics data, and stay updated on the latest trends in tensor-based multi-omics research.