Literature DB >> 15033598

Dye bias correction in dual-labeled cDNA microarray gene expression measurements.

Barry A Rosenzweig1, P Scott Pine, Olen E Domon, Suzanne M Morris, James J Chen, Frank D Sistare.   

Abstract

A significant limitation to the analytical accuracy and precision of dual-labeled spotted cDNA microarrays is the signal error due to dye bias. Transcript-dependent dye bias may be due to gene-specific differences of incorporation of two distinctly different chemical dyes and the resultant differential hybridization efficiencies of these two chemically different targets for the same probe. Several approaches were used to assess and minimize the effects of dye bias on fluorescent hybridization signals and maximize the experimental design efficiency of a cell culture experiment. Dye bias was measured at the individual transcript level within each batch of simultaneously processed arrays by replicate dual-labeled split-control sample hybridizations and accounted for a significant component of fluorescent signal differences. This transcript-dependent dye bias alone could introduce unacceptably high numbers of both false-positive and false-negative signals. We found that within a given set of concurrently processed hybridizations, the bias is remarkably consistent and therefore measurable and correctable. The additional microarrays and reagents required for paired technical replicate dye-swap corrections commonly performed to control for dye bias could be costly to end users. Incorporating split-control microarrays within a set of concurrently processed hybridizations to specifically measure dye bias can eliminate the need for technical dye swap replicates and reduce microarray and reagent costs while maintaining experimental accuracy and technical precision. These data support a practical and more efficient experimental design to measure and mathematically correct for dye bias.

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Year:  2004        PMID: 15033598      PMCID: PMC1241902          DOI: 10.1289/ehp.6694

Source DB:  PubMed          Journal:  Environ Health Perspect        ISSN: 0091-6765            Impact factor:   9.031


  10 in total

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Authors:  X Wang; S Ghosh; S W Guo
Journal:  Nucleic Acids Res       Date:  2001-08-01       Impact factor: 16.971

2.  Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects.

Authors:  G C Tseng; M K Oh; L Rohlin; J C Liao; W H Wong
Journal:  Nucleic Acids Res       Date:  2001-06-15       Impact factor: 16.971

3.  Assessing gene significance from cDNA microarray expression data via mixed models.

Authors:  R D Wolfinger; G Gibson; E D Wolfinger; L Bennett; H Hamadeh; P Bushel; C Afshari; R S Paules
Journal:  J Comput Biol       Date:  2001       Impact factor: 1.479

4.  Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

Authors:  Yee Hwa Yang; Sandrine Dudoit; Percy Luu; David M Lin; Vivian Peng; John Ngai; Terence P Speed
Journal:  Nucleic Acids Res       Date:  2002-02-15       Impact factor: 16.971

5.  Unfolding of microarray data.

Authors:  A B Goryachev; P F Macgregor; A M Edwards
Journal:  J Comput Biol       Date:  2001       Impact factor: 1.479

6.  Analysis of variance for gene expression microarray data.

Authors:  M K Kerr; M Martin; G A Churchill
Journal:  J Comput Biol       Date:  2000       Impact factor: 1.479

7.  Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data.

Authors:  T Ideker; V Thorsson; A F Siegel; L E Hood
Journal:  J Comput Biol       Date:  2000       Impact factor: 1.479

Review 8.  Microarray data normalization and transformation.

Authors:  John Quackenbush
Journal:  Nat Genet       Date:  2002-12       Impact factor: 38.330

9.  Statistical design of reverse dye microarrays.

Authors:  K Dobbin; J H Shih; R Simon
Journal:  Bioinformatics       Date:  2003-05-01       Impact factor: 6.937

10.  Transcriptional regulation of mitotic genes by camptothecin-induced DNA damage: microarray analysis of dose- and time-dependent effects.

Authors:  Yi Zhou; Fuad G Gwadry; William C Reinhold; Lance D Miller; Lawrence H Smith; Uwe Scherf; Edison T Liu; Kurt W Kohn; Yves Pommier; John N Weinstein
Journal:  Cancer Res       Date:  2002-03-15       Impact factor: 12.701

  10 in total
  25 in total

1.  Mathematical algorithm for discovering states of expression from direct genetic comparison by microarrays.

Authors:  Hassan M Fathallah-Shaykh; Bin He; Li-Juan Zhao; Aamir Badruddin
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2.  Correcting for gene-specific dye bias in DNA microarrays using the method of maximum likelihood.

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Journal:  Bioinformatics       Date:  2007-07-10       Impact factor: 6.937

3.  Comparative analysis of seed transcriptomes of ambient ozone-fumigated 2 different rice cultivars.

Authors:  Kyoungwon Cho; Junko Shibato; Akihiro Kubo; Yoshihisa Kohno; Kouji Satoh; Shoshi Kikuchi; Abhijit Sarkar; Ganesh Kumar Agrawal; Randeep Rakwal
Journal:  Plant Signal Behav       Date:  2013-09-11

4.  Valosin-containing protein (p97) is a regulator of endoplasmic reticulum stress and of the degradation of N-end rule and ubiquitin-fusion degradation pathway substrates in mammalian cells.

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Journal:  Mol Biol Cell       Date:  2006-08-16       Impact factor: 4.138

5.  Gene selection with multiple ordering criteria.

Authors:  James J Chen; Chen-An Tsai; Shengli Tzeng; Chun-Houh Chen
Journal:  BMC Bioinformatics       Date:  2007-03-05       Impact factor: 3.169

6.  Dynamics of 17alpha-ethynylestradiol exposure in rainbow trout (Oncorhynchus mykiss): absorption, tissue distribution, and hepatic gene expression pattern.

Authors:  Ann D Skillman; James J Nagler; Sharon E Hook; Jack A Small; Irvin R Schultz
Journal:  Environ Toxicol Chem       Date:  2006-11       Impact factor: 3.742

7.  Bayesian integrated modeling of expression data: a case study on RhoG.

Authors:  Rashi Gupta; Dario Greco; Petri Auvinen; Elja Arjas
Journal:  BMC Bioinformatics       Date:  2010-06-01       Impact factor: 3.169

8.  Down-regulation of defense genes and resource allocation into infected roots as factors for compatibility between Fagus sylvatica and Phytophthora citricola.

Authors:  Katja Schlink
Journal:  Funct Integr Genomics       Date:  2009-10-08       Impact factor: 3.410

9.  Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB.

Authors:  Aristotelis Chatziioannou; Panagiotis Moulos; Fragiskos N Kolisis
Journal:  BMC Bioinformatics       Date:  2009-10-27       Impact factor: 3.169

10.  Transcriptional profiling differences for articular cartilage and repair tissue in equine joint surface lesions.

Authors:  Michael J Mienaltowski; Liping Huang; David D Frisbie; C Wayne McIlwraith; Arnold J Stromberg; Arne C Bathke; James N Macleod
Journal:  BMC Med Genomics       Date:  2009-09-14       Impact factor: 3.063

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