ArticleScientific reports2022
A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Gene and cell line efficiency of CRISPR computed by tensor decomposition in genome-wide CRISPR-Cas9 knockout screens.Scientific reports · 2026Article
- Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Bioinformatics.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Application note: TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction.Frontiers in artificial intelligence · 2023Article
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2 authors.
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Abstract
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.
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