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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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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

High-throughput DNA methylation datasets for evaluating false discovery rate methodologies.

N Asomaning1, K J Archer

  • 1Nancy Asomaning, Center on Health Disparities, Virginia Commonwealth University, Richmond, Virginia 23298.

Computational Statistics & Data Analysis
|May 22, 2012
PubMed
Summary
This summary is machine-generated.

High-throughput methylation data offers a novel approach to evaluate false discovery rate (FDR) and family-wise error rate (FWER) methods. This study assesses common FDR/FWER procedures using methylation datasets for improved genomic data analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Multiple comparison problem is prevalent in high-throughput genomic data analysis.
  • Commonly used methods include false discovery rate (FDR) and family-wise error rate (FWER) control.
  • Existing comparisons rely on limited simulations or microarray data with unknown ground truth.

Purpose of the Study:

  • To evaluate the performance of commonly used FDR/FWER methodologies.
  • To leverage high-throughput methylation data for objective assessment of multiple comparison procedures.
  • To compare FDR/FWER methods in identifying differentially methylated CpG sites between healthy males and females.

Main Methods:

  • Applied q-value, Benjamini & Hochberg, Benjamini & Yekutieli, and Holm's step down methods to methylation datasets.
  • Utilized sex chromosome CpG sites in healthy males versus females to establish a known ground truth.
  • Assessed method performance based on sensitivity, specificity, and observed FDR.

Main Results:

  • Methylation datasets provide a suitable platform for evaluating FDR/FWER methods due to inherent correlation structures.
  • Comparison revealed differences in the ability of methods to detect sex chromosome-specific methylation differences.
  • Sensitivity, specificity, and observed FDR varied among the tested multiple comparison procedures.

Conclusions:

  • High-throughput methylation data offers an objective benchmark for assessing multiple comparison correction methods.
  • The findings aid in selecting appropriate FDR/FWER procedures for genomic studies.
  • Methylation datasets can also be used for evaluating variable selection and meta-analysis methods.