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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...
RNA-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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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

Signal quality measurements for cDNA microarray data.

Tracy L Bergemann1, Lue Ping Zhao

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, 420 Delaware St. SE, Minneapolis, MN 55455, USA. berge319@umn.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 1, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces two methods to estimate spot-specific error in microarray expression data, accounting for spatial correlation. Both approaches effectively capture noise and identify poor spot quality in microarrays.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Modeling

Background:

  • Microarray expression data reliability is a significant concern in genomics research.
  • Measurement error in microarrays originates from both technical and experimental factors.
  • Existing models for spot-specific error estimation are limited.

Purpose of the Study:

  • To develop and compare two novel approaches for quantifying spot-specific error in microarray intensity estimates.
  • To account for the impact of spatial correlation between pixels on spot quality.
  • To assess the performance of these methods in identifying poor quality spots.

Main Methods:

  • A parametric method estimating within-spot variance assuming Gaussian distribution and using an overdispersion factor for spatial correlation.
  • A nonparametric method, Mean Square Prediction Error (MSPE), which involves smoothing pixel regions and measuring deviations.
  • Comparison of both methods using real and simulated microarray data.

Main Results:

  • Both the parametric and MSPE methods successfully capture noise within the microarray platform.
  • The study assesses the numerical characteristics and effectiveness of each method in describing poor spot quality.
  • Specific scenarios are identified where one method demonstrates superiority over the other.

Conclusions:

  • The developed methods provide valuable tools for assessing the reliability of microarray spot intensity estimates.
  • Accounting for spatial correlation is crucial for accurate error estimation in microarrays.
  • The choice between the parametric and MSPE methods depends on the specific characteristics of the data and the research question.