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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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Related Experiment Video

Updated: Jun 10, 2026

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays
10:56

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Published on: January 16, 2018

Robust test method for time-course microarray experiments.

Insuk Sohn1, Kouros Owzar, Stephen L George

  • 1Biostatistics and Bioinformatics Center, Samsung Cancer Research Institute, Samsung Medical Center, Seoul 137-710, Republic of Korea. insuk.sohn@samsung.com

BMC Bioinformatics
|July 24, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a robust statistical method for identifying genes with time-dependent expression profiles in microarray experiments. The new approach addresses limitations of existing methods, improving the analysis of gene expression data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray experiments measure gene expression over time to understand temporal gene-expression profiles.
  • Identifying genes with expression trajectories influenced by experimental factors is a key scientific objective.
  • Current statistical methods often rely on regression, which can be sensitive to outliers in gene expression data.

Purpose of the Study:

  • To develop a robust statistical testing method for identifying genes with factor-dependent expression time profiles.
  • To introduce a multiple testing procedure for adjusting for multiplicity in gene expression analysis.

Main Methods:

  • Proposed a novel robust testing methodology for time-course gene expression data.
  • Developed a multiple testing procedure to control false discovery rates.

Main Results:

  • The proposed robust method demonstrates improved performance in identifying differentially expressed genes.
  • Simulation studies confirm the method's effectiveness and robustness.

Conclusions:

  • The developed robust testing method offers a reliable approach for analyzing time-course gene expression data.
  • The method is applicable to identifying genes influenced by experimental or phenotypic factors.
  • Further applications and extensions of the method are discussed.