Literature DB >> 12388780

Quantitative noise analysis for gene expression microarray experiments.

Y Tu1, G Stolovitzky, U Klein.   

Abstract

A major challenge in DNA microarray analysis is to effectively dissociate actual gene expression values from experimental noise. We report here a detailed noise analysis for oligonuleotide-based microarray experiments involving reverse transcription, generation of labeled cRNA (target) through in vitro transcription, and hybridization of the target to the probe immobilized on the substrate. By designing sets of replicate experiments that bifurcate at different steps of the assay, we are able to separate the noise caused by sample preparation and the hybridization processes. We quantitatively characterize the strength of these different sources of noise and their respective dependence on the gene expression level. We find that the sample preparation noise is small, implying that the amplification process during the sample preparation is relatively accurate. The hybridization noise is found to have very strong dependence on the expression level, with different characteristics for the low and high expression values. The hybridization noise characteristics at the high expression regime are mostly Poisson-like, whereas its characteristics for the small expression levels are more complex, probably due to cross-hybridization. A method to evaluate the significance of gene expression fold changes based on noise characteristics is proposed.

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Year:  2002        PMID: 12388780      PMCID: PMC137831          DOI: 10.1073/pnas.222164199

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  5 in total

Review 1.  Genomics, gene expression and DNA arrays.

Authors:  D J Lockhart; E A Winzeler
Journal:  Nature       Date:  2000-06-15       Impact factor: 49.962

2.  Characterization of variability in large-scale gene expression data: implications for study design.

Authors:  Jaroslav P Novak; Robert Sladek; Thomas J Hudson
Journal:  Genomics       Date:  2002-01       Impact factor: 5.736

3.  Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations.

Authors:  M L Lee; F C Kuo; G A Whitmore; J Sklar
Journal:  Proc Natl Acad Sci U S A       Date:  2000-08-29       Impact factor: 11.205

Review 4.  Exploring the new world of the genome with DNA microarrays.

Authors:  P O Brown; D Botstein
Journal:  Nat Genet       Date:  1999-01       Impact factor: 38.330

5.  Expression monitoring by hybridization to high-density oligonucleotide arrays.

Authors:  D J Lockhart; H Dong; M C Byrne; M T Follettie; M V Gallo; M S Chee; M Mittmann; C Wang; M Kobayashi; H Horton; E L Brown
Journal:  Nat Biotechnol       Date:  1996-12       Impact factor: 54.908

  5 in total
  66 in total

1.  Modeling of DNA microarray data by using physical properties of hybridization.

Authors:  G A Held; G Grinstein; Y Tu
Journal:  Proc Natl Acad Sci U S A       Date:  2003-06-13       Impact factor: 11.205

2.  Identification and handling of artifactual gene expression profiles emerging in microarray hybridization experiments.

Authors:  Leonid Brodsky; Andrei Leontovich; Michael Shtutman; Elena Feinstein
Journal:  Nucleic Acids Res       Date:  2004-03-03       Impact factor: 16.971

3.  Computing gene expression data with a knowledge-based gene clustering approach.

Authors:  Bruce A Rosa; Sookyung Oh; Beronda L Montgomery; Jin Chen; Wensheng Qin
Journal:  Int J Biochem Mol Biol       Date:  2010-06-15

4.  Comparison of transcript profiling on Arabidopsis microarray platform technologies.

Authors:  Jeffrey D Pylatuik; Pierre R Fobert
Journal:  Plant Mol Biol       Date:  2005-07       Impact factor: 4.076

5.  Statistical analysis of MPSS measurements: application to the study of LPS-activated macrophage gene expression.

Authors:  G A Stolovitzky; A Kundaje; G A Held; K H Duggar; C D Haudenschild; D Zhou; T J Vasicek; K D Smith; A Aderem; J C Roach
Journal:  Proc Natl Acad Sci U S A       Date:  2005-01-24       Impact factor: 11.205

Review 6.  Metabolic engineering in the -omics era: elucidating and modulating regulatory networks.

Authors:  Goutham N Vemuri; Aristos A Aristidou
Journal:  Microbiol Mol Biol Rev       Date:  2005-06       Impact factor: 11.056

7.  Symbolic data analysis to defy low signal-to-noise ratio in microarray data for breast cancer prognosis.

Authors:  Lyamine Hedjazi; Marie-Veronique Le Lann; Tatiana Kempowsky; Florence Dalenc; Joseph Aguilar-Martin; Gilles Favre
Journal:  J Comput Biol       Date:  2013-08       Impact factor: 1.479

8.  Optimal Objective-Based Experimental Design for Uncertain Dynamical Gene Networks with Experimental Error.

Authors:  Daniel N Mohsenizadeh; Roozbeh Dehghannasiri; Edward R Dougherty
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2016-08-25       Impact factor: 3.710

9.  Empirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray data.

Authors:  Dake Yang; Rudolph S Parrish; Guy N Brock
Journal:  Comput Biol Med       Date:  2013-12-13       Impact factor: 4.589

10.  Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

Authors:  Sapna Kumari; Jeff Nie; Huann-Sheng Chen; Hao Ma; Ron Stewart; Xiang Li; Meng-Zhu Lu; William M Taylor; Hairong Wei
Journal:  PLoS One       Date:  2012-11-30       Impact factor: 3.240

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