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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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: Jun 13, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
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Published on: May 21, 2019

Cross-platform microarray data integration using the normalised linear transform.

Huilin Xiong1, Ya Zhang, Xue-Wen Chen

  • 1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China. hlxiong@sjtu.edu.cn

International Journal of Data Mining and Bioinformatics
|April 29, 2010
PubMed
Summary
This summary is machine-generated.

Integrating microarray data from different platforms is crucial for robust analysis. The Normalised Linear Transform (NLT) method effectively combines datasets, improving classification accuracy and identifying more significant marker genes.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Analysis

Background:

  • Small sample size poses a significant challenge in microarray data analysis.
  • Integrating data from multiple studies can overcome sample size limitations for more reliable statistical insights.

Purpose of the Study:

  • To introduce a novel data integration scheme, Normalised Linear Transform (NLT), for combining microarray data from diverse platforms.
  • To evaluate the performance of NLT against existing integration methods for classification and gene marker selection.

Main Methods:

  • Developed and applied the Normalised Linear Transform (NLT) for integrating heterogeneous microarray datasets.
  • Compared NLT with three other data integration schemes using classification and gene marker selection tasks.

Main Results:

  • The NLT scheme demonstrated superior performance in classification accuracy compared to other methods.
  • NLT facilitated the identification of more biologically relevant marker genes.

Conclusions:

  • Normalised Linear Transform (NLT) offers a simple yet effective approach for integrating microarray data across different platforms.
  • NLT enhances the reliability of statistical analyses, leading to improved biological discoveries in genomics research.