Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization

TL;DR

This paper introduces TCAM, a novel tensor factorization method designed to effectively reduce dimensionality in complex longitudinal omics data, improving trajectory analysis and integration with machine learning tasks.

Contribution

The paper presents TCAM, a new tensor-based dimensionality reduction technique specifically tailored for longitudinal omics data analysis, addressing limitations of existing methods.

Findings

TCAM outperforms traditional and state-of-the-art tensor methods in microbiome data analysis.

TCAM demonstrates versatility across different omics datasets.

TCAM can serve as a drop-in replacement in machine learning workflows.

Abstract

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.