A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching

The integrated analysis of multiple gene expression profiles previously measured in distinct studies is problematic since missing both sample matches and common labels prevent their integration in fully data-driven, unsupervised training. In this study, we propose a strategy to enable the integration of multiple gene expression profiles among multiple independent studies with neither labeling nor sample matching using tensor decomposition unsupervised feature extraction. We apply this strategy to Alzheimer's disease (AD)-related gene expression profiles that lack precise correspondence among samples, including AD single-cell RNA sequence (scRNA-seq) data. We were able to select biologically reasonable genes using the integrated analysis. Overall, integrated gene expression profiles can function analogously to prior- and/or transfer-learning strategies in other machine-learning applications. For scRNA-seq, the proposed approach significantly reduces the required computational memory.

Automated summary

The study proposes a strategy for integrating multiple gene expression profiles from independent studies without labels or sample matching using unsupervised feature extraction. The strategy is applied to Alzheimer's disease-related gene expression profiles, resulting in the selection of biologically relevant genes. Integrated gene expression profiles can function similarly to prior- and/or transfer-learning strategies in other machine learning applications and reduce computational memory requirements in scRNA-seq.