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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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Performing Custom MicroRNA Microarray Experiments
07:04

Performing Custom MicroRNA Microarray Experiments

Published on: October 28, 2011

Algorithm-driven artifacts in median polish summarization of microarray data.

Federico M Giorgi1, Anthony M Bolger, Marc Lohse

  • 1Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Golm, Germany.

BMC Bioinformatics
|November 13, 2010
PubMed
Summary
This summary is machine-generated.

We developed tRMA, a modified normalization procedure for microarray data. It corrects overestimation of sample similarity in correlation analyses, improving gene expression studies.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Affymetrix microarrays generate vast transcript intensity data.
  • Various preprocessing techniques exist for gene expression estimation.
  • Limited benchmarking of these techniques for coexpression studies like sample classification.

Purpose of the Study:

  • Benchmark normalization procedures (MAS5, RMA, GCRMA) for inter-array correlation analysis.
  • Assess their performance in coexpression-based studies.
  • Identify sources of over-similarity artifacts in normalization.

Main Methods:

  • Benchmarking of MAS5, RMA, and GCRMA normalization procedures.
  • Inter-array correlation analysis.
  • Analysis of probeset characteristics and internal signal disagreement.
  • Development of a modified summarization procedure (tRMA).

Main Results:

  • RMA and GCRMA normalization consistently overestimate sample similarity.
  • Median polish summarization is a key contributor to over-similarity artifacts.
  • Affected probesets often exhibit internal signal disagreement and target multiple transcripts.
  • The proposed correction (tRMA) significantly reduces inter-array correlation artifacts.

Conclusions:

  • tRMA is proposed as a modified RMA procedure for normalizing microarray experiments.
  • tRMA effectively addresses correlation-based analysis artifacts.
  • The method maintains the detection of differentially expressed genes.