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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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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Related Experiment Video

Updated: May 13, 2026

A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA
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An imputation approach for oligonucleotide microarrays.

Ming Li1, Yalu Wen, Qing Lu

  • 1Division of Biostatistics, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.

Plos One
|March 19, 2013
PubMed
Summary

This study introduces a novel mixed-effects model to improve microarray data analysis by imputing intensities affected by image noise. This method enhances genotyping accuracy without needing biological replicates.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Oligonucleotide microarrays are vital for quantifying molecules in biological samples.
  • Analysis relies on interpreting hybridization signals from CEL images, which can be corrupted by noise.
  • Current methods often remove affected intensities, potentially losing valuable data.

Purpose of the Study:

  • To develop and evaluate a novel imputation method for handling noise-corrupted CEL images in microarray data analysis.
  • To improve the accuracy of downstream analyses, such as genotyping, by effectively processing defective image data.

Main Methods:

  • A mixed-effects model was developed for imputing intensities in CEL images affected by various noise sources.
  • The approach is single-array-based, eliminating the need for biological replicates or between-array normalization.
  • Performance was assessed using Affymetrix high-density SNP arrays.

Main Results:

  • The proposed imputation procedure significantly reduced genotyping error rates on SNP arrays.
  • The method demonstrated effectiveness in handling image defects like bright spots and dark clouds.
  • The approach is adaptable for other oligonucleotide microarrays, including gene expression profiling.

Conclusions:

  • Mixed-effects model imputation offers a robust solution for noise in microarray CEL images.
  • This method enhances data quality and reduces errors in genetic analyses.
  • The freely available R code facilitates broader application in genomic research.