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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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Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants
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Estimating the arm-wise false discovery rate in array comparative genomic hybridization experiments.

Daniel P Gaile1, Elizabeth D Schifano, Jeffrey C Miecznikowski

  • 1State University of New York at Buffalo. dpgaile@buffalo.edu

Statistical Applications in Genetics and Molecular Biology
|December 7, 2007
PubMed
Summary
This summary is machine-generated.

Array Comparative Genomic Hybridization (aCGH) analysis involves spatially correlated data, complicating false discovery rate (FDR) estimation. This study introduces a novel method for estimating chromosome arm-wise FDR, aligning biological and testing objectives for more relevant genomic discovery.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Array Comparative Genomic Hybridization (aCGH) measures relative genetic abundance (RGA) across the genome using array-based technology.
  • Spatial correlation in aCGH spot assays complicates standard multiple hypothesis testing and false discovery rate (FDR) estimation.
  • Existing methods may not accurately reflect biological interest in regional genetic variations rather than individual spot-wise findings.

Purpose of the Study:

  • To address the challenge of estimating FDR in the presence of spatial correlation in aCGH data.
  • To develop and evaluate a method for estimating a 'regional' or chromosome arm-wise FDR.
  • To align the statistical testing objectives with biological interpretation of genomic discoveries.

Main Methods:

  • Utilized simulation studies based on real aCGH data with preferentially re-sampled spot assay values.
  • Compared the performance of the Benjamini and Hochberg method for spot-wise FDR estimation.
  • Developed and applied a novel method for calculating chromosome arm-wise FDR, defining discoveries at the chromosome arm level.

Main Results:

  • The Benjamini and Hochberg method can provide reasonable spot-wise FDR estimates under specific simulation conditions.
  • The proposed chromosome arm-wise FDR method offers a more biologically relevant measure of discovery.
  • Simulation results demonstrate the utility of the new method in accurately estimating regional FDR.

Conclusions:

  • Estimating chromosome arm-wise FDR is crucial for accurate interpretation of aCGH data in many biological contexts.
  • The developed method provides a statistically sound and biologically meaningful approach to regional FDR estimation.
  • This approach enhances the discovery process in genomic research by better aligning statistical findings with biological relevance.