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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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Related Experiment Video

Updated: Jul 4, 2026

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform
13:14

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform

Published on: August 10, 2009

Estimating gene signals from noisy microarray images.

P Sarder1, A Nehorai, P H Davis

  • 1Department of Electrical and Systems Engineering, Washington University, St. Louis, MO 63130, USA. psarde1@ese.wustl.edu

IEEE Transactions on Nanobioscience
|June 17, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel estimation technique for oligonucleotide microarray experiments, improving gene signal detection amidst background noise. The method enhances accuracy in noisy images, outperforming conventional approaches.

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
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Published on: March 15, 2011

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Last Updated: Jul 4, 2026

Global Gene Expression Analysis Using a Zebrafish Oligonucleotide Microarray Platform
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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Area of Science:

  • Genomics
  • Bioinformatics
  • Signal Processing

Background:

  • Oligonucleotide microarrays are crucial for biological studies but suffer from noise.
  • Low photomultiplier tube (PMT) voltage images present challenges with weak gene signals and background fluorescence.
  • Nonspecific sequence binding further complicates accurate measurements in microarray data.

Purpose of the Study:

  • To develop an analytically based estimation technique for precise foreground and background signal separation in microarray images.
  • To improve the detection of weak gene signals in the presence of significant noise and non-specific binding.
  • To offer a robust method for analyzing noisy microarray data, particularly in complex biological contexts.

Main Methods:

  • Proposed an estimation technique assuming a priori spot-shape information (circular periphery, elliptical center hole).
  • Modeled foreground and background signals using Gaussian statistics, with mean quantifying gene signal and variance measuring undesired binding.
  • Developed a foreground-signal and shape-estimation algorithm utilizing the Gibbs sampling method.

Main Results:

  • The developed Gibbs sampling algorithm demonstrated superior performance compared to Mann-Whitney (MW) and Expectation Maximization (EM)/Iterated Conditional Modes (ICM) methods.
  • Achieved considerably smaller mean-square error (MSE) across all signal-to-noise ratios (SNRs) in computer-generated images.
  • Provided better qualitative results for low-SNR real-data images, effectively observing gene-signal fluctuations in noisy conditions.

Conclusions:

  • The proposed method effectively separates foreground and background signals, offering improved accuracy in noisy microarray data.
  • While computationally intensive due to sampling, the technique is highly effective for very noisy-spot images.
  • Enables better observation of gene-signal fluctuations, crucial for understanding biological systems in natural contexts.