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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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Linear mixed model selection for false discovery rate control in microarray data analysis.

Cumhur Yusuf Demirkale1, Dan Nettleton, Tapabrata Maiti

  • 1Department of Statistics, Iowa State University, Ames, Iowa 50011, USA.

Biometrics
|June 16, 2009
PubMed
Summary
This summary is machine-generated.

A new method improves gene expression analysis by combining full and selected linear mixed models. This approach ensures accurate control of the false discovery rate (FDR) even when gene-specific random effects vary.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray experiments generate expression data for all genes.
  • Linear mixed models are commonly used for gene expression analysis.
  • Standard methods can fail to control the false discovery rate (FDR) when random effects vary across genes.

Purpose of the Study:

  • To develop a novel method for identifying differentially expressed genes in microarray data.
  • To ensure robust false discovery rate (FDR) control when the random effects structure differs between genes.
  • To improve the reliability of gene expression analysis in complex experimental designs.

Main Methods:

  • Fitting a full linear mixed model for each gene.
  • Fitting selected linear mixed models tailored to gene-specific random effects.
  • Combining results from both model types to identify differentially expressed genes.
  • Implementing a strategy for controlling the false discovery rate (FDR) at desired levels.

Main Results:

  • The proposed method effectively controls the false discovery rate (FDR) across genes with varying random effects structures.
  • Differential gene expression identification is more reliable compared to standard methods that assume a uniform model.
  • The approach maintains FDR control even when variance components for random factors are zero for certain genes.

Conclusions:

  • Combining full and selected linear mixed models offers improved FDR control in gene expression studies.
  • This method enhances the accuracy of identifying differentially expressed genes with complex random effects.
  • The proposed approach provides a more robust statistical framework for microarray data analysis.