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DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Aug 18, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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

Y-H Taguchi¹, Turki Turki²

¹Department of Physics, Chuo University, Tokyo, 112-8551, Japan. tag@granular.com.

Scientific Reports

|December 9, 2022

Summary

This summary is machine-generated.

This study introduces a novel tensor decomposition method for integrating diverse gene expression datasets without sample matching or labels. This approach effectively analyzes Alzheimer's disease data and reduces computational memory for single-cell RNA sequencing.

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Last Updated: Aug 18, 2025

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Integrating multiple gene expression profiles from independent studies is challenging due to missing sample matches and common labels.
Existing methods often require fully data-driven, unsupervised training, which is hindered by data heterogeneity.

Purpose of the Study:

To develop a strategy for integrating multiple gene expression profiles across independent studies without requiring sample matching or common labels.
To apply this strategy to Alzheimer's disease (AD) gene expression data, including single-cell RNA sequencing (scRNA-seq) data.

Main Methods:

Utilizing tensor decomposition for unsupervised feature extraction to enable data integration.
Applying the proposed strategy to Alzheimer's disease-related gene expression profiles lacking precise sample correspondence.

Main Results:

Successfully integrated multiple gene expression profiles from independent studies without sample matching or labels.
Identified biologically relevant genes through integrated analysis of Alzheimer's disease data.
Demonstrated that integrated gene expression profiles can function similarly to transfer-learning strategies.
Significantly reduced computational memory requirements for single-cell RNA sequencing data analysis.

Conclusions:

The proposed tensor decomposition strategy enables the integration of heterogeneous gene expression profiles across studies.
This approach facilitates the selection of biologically meaningful genes and enhances machine learning applications.
The method offers a computationally efficient solution for analyzing large-scale gene expression data, particularly scRNA-seq.