Literature DB >> 11931570

Normalizing DNA microarray data.

Martin Bilban1, Lukas K Buehler, Steven Head, Gernot Desoye, Vito Quaranta.   

Abstract

DNA microarrays are a powerful tool to investigate differential gene expression for thousands of genes simultaneously. Although DNA microarrays have been widely used to understand the critical events underlying growth, development, homeostasis, behavior and the onset of disease, the management of the resulting data has received little attention. Presently, the fluorescent dyes Cy3 and Cy5 are most often used to prepare labeled cDNA for microarray hybridizations. Raw microarray data are image files that have to be transformed into gene expression formats--a process that requires data manipulation due to systematic variations which may be attributed to differences in the physical and chemical dye applications is to identify differences in transcript levels calculated from fluorescence ratios it is necessary to normalize fluorescence signals to compensate for systematic variations. Here, we will review current normalization strategies applied to cDNA microarrays and discuss their limits. We will show that experimental design determines normalization success.

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Year:  2002        PMID: 11931570

Source DB:  PubMed          Journal:  Curr Issues Mol Biol        ISSN: 1467-3037            Impact factor:   2.081


  25 in total

1.  ExpressYourself: A modular platform for processing and visualizing microarray data.

Authors:  Nicholas M Luscombe; Thomas E Royce; Paul Bertone; Nathaniel Echols; Christine E Horak; Joseph T Chang; Michael Snyder; Mark Gerstein
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

2.  Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments.

Authors:  Bedilu Alamirie Ejigu; Dirk Valkenborg; Geert Baggerman; Manu Vanaerschot; Erwin Witters; Jean-Claude Dujardin; Tomasz Burzykowski; Maya Berg
Journal:  OMICS       Date:  2013-06-29

3.  Timecourse microarray analyses reveal global changes in gene expression of susceptible Glycine max (soybean) roots during infection by Heterodera glycines (soybean cyst nematode).

Authors:  Nadim W Alkharouf; Vincent P Klink; Imed B Chouikha; Hunter S Beard; Margaret H MacDonald; Susan Meyer; Halina T Knap; Rana Khan; Benjamin F Matthews
Journal:  Planta       Date:  2006-03-31       Impact factor: 4.116

4.  Microarray Data Preprocessing: From Experimental Design to Differential Analysis.

Authors:  Antonio Federico; Laura Aliisa Saarimäki; Angela Serra; Giusy Del Giudice; Pia Anneli Sofia Kinaret; Giovanni Scala; Dario Greco
Journal:  Methods Mol Biol       Date:  2022

Review 5.  Review of the literature examining the correlation among DNA microarray technologies.

Authors:  Carole L Yauk; M Lynn Berndt
Journal:  Environ Mol Mutagen       Date:  2007-06       Impact factor: 3.216

6.  SWISS MADE: Standardized WithIn Class Sum of Squares to evaluate methodologies and dataset elements.

Authors:  Christopher R Cabanski; Yuan Qi; Xiaoying Yin; Eric Bair; Michele C Hayward; Cheng Fan; Jianying Li; Matthew D Wilkerson; J S Marron; Charles M Perou; D Neil Hayes
Journal:  PLoS One       Date:  2010-03-26       Impact factor: 3.240

7.  Real-time fluorescent image analysis of DNA spot hybridization kinetics to assess microarray spot heterogeneity.

Authors:  Archana N Rao; Christopher K Rodesch; David W Grainger
Journal:  Anal Chem       Date:  2012-10-29       Impact factor: 6.986

8.  Normalization benefits microarray-based classification.

Authors:  Jianping Hua; Yoganand Balagurunathan; Yidong Chen; James Lowey; Michael L Bittner; Zixiang Xiong; Edward Suh; Edward R Dougherty
Journal:  EURASIP J Bioinform Syst Biol       Date:  2006

Review 9.  Microarray-based gene expression profiling in pancreatic ductal carcinoma: status quo and perspectives.

Authors:  Robert Grützmann; Hans Detlev Saeger; Jutta Lüttges; Hans Konrad Schackert; Holger Kalthoff; Günter Klöppel; Christian Pilarsky
Journal:  Int J Colorectal Dis       Date:  2004-01-24       Impact factor: 2.571

10.  The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis.

Authors:  Xing Qiu; Hulin Wu; Rui Hu
Journal:  BMC Bioinformatics       Date:  2013-04-11       Impact factor: 3.169

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