Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization
This work addresses a bottleneck in precision medicine by providing a computational solution for longitudinal omics data analysis, though it appears incremental as it builds on tensor factorization methods.
The paper tackled the challenge of analyzing complex, high-dimensional longitudinal omics data in precision medicine by developing TCAM, a dimensionality reduction method that outperforms traditional and state-of-the-art tensor-based approaches in trajectory analysis.
Precision medicine is a clinical approach for disease prevention, detection and treatment, which considers each individual's genetic background, environment and lifestyle. The development of this tailored avenue has been driven by the increased availability of omics methods, large cohorts of temporal samples, and their integration with clinical data. Despite the immense progression, existing computational methods for data analysis fail to provide appropriate solutions for this complex, high-dimensional and longitudinal data. In this work we have developed a new method termed TCAM, a dimensionality reduction technique for multi-way data, that overcomes major limitations when doing trajectory analysis of longitudinal omics data. Using real-world data, we show that TCAM outperforms traditional methods, as well as state-of-the-art tensor-based approaches for longitudinal microbiome data analysis. Moreover, we demonstrate the versatility of TCAM by applying it to several different omics datasets, and the applicability of it as a drop-in replacement within straightforward ML tasks.