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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...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Published on: August 16, 2017

Multiple testing for gene sets from microarray experiments.

Insuk Sohn1, Kouros Owzar, Johan Lim

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

BMC Bioinformatics
|May 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new gene set testing framework to identify gene sets linked to clinical outcomes. The method effectively controls the false discovery rate (FDR) and outperforms existing approaches like GSEA and GSA.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Identifying individual genes associated with clinical outcomes is crucial in microarray studies.
  • Discovering biologically functional gene sets linked to outcomes is also of significant interest.

Purpose of the Study:

  • To propose a novel permutation-based framework for gene set testing.
  • To control the false discovery rate (FDR) while considering gene dependencies within and across sets.
  • To compare the proposed method against existing Gene Set Enrichment Analysis (GSEA) and Gene Set Analysis (GSA) methods.

Main Methods:

  • A general permutation-based framework for gene set testing was developed.
  • The method accounts for complex dependencies among genes.
  • The framework was applied to three public microarray datasets.

Main Results:

  • The proposed method demonstrated effective control of the false discovery rate (FDR).
  • Simulations and case studies confirmed the method's performance.
  • The new framework outperformed GSEA and GSA, particularly with numerous prognostic gene sets.

Conclusions:

  • The developed gene set testing framework reliably controls the FDR.
  • The method offers superior performance compared to GSEA and GSA in identifying prognostic gene sets.
  • This approach enhances the analysis of microarray data for clinical outcome prediction.