Literature DB >> 18629271

The curse of normalization.

Olaf Wolkenhauer1, Carla Möller-Levet, Fatima Sanchez-Cabo.   

Abstract

Despite its enormous promise to further our understanding of cellular processes involved in the regulation of gene expression, microarray technology generates data for which statistical pre-processing has become a necessity before any interpretation of data can begin. The process by which we distinguish (and remove) non-biological variation from biological variation is called normalization. With a multitude of experimental designs, techniques and technologies influencing the acquisition of data, numerous approaches to normalization have been proposed in the literature. The purpose of this short review is not to add to the many suggestions that have been made, but to discuss some of the difficulties we encounter when analysing microarray data.

Year:  2002        PMID: 18629271      PMCID: PMC2448435          DOI: 10.1002/cfg.192

Source DB:  PubMed          Journal:  Comp Funct Genomics        ISSN: 1531-6912


  5 in total

1.  Adjustments and measures of differential expression for microarray data.

Authors:  A Tsodikov; A Szabo; D Jones
Journal:  Bioinformatics       Date:  2002-02       Impact factor: 6.937

2.  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

Review 3.  Computational analysis of microarray data.

Authors:  J Quackenbush
Journal:  Nat Rev Genet       Date:  2001-06       Impact factor: 53.242

4.  Extracting information from cDNA arrays.

Authors:  Hanspeter Herzel; Dieter Beule; Szymon Kielbasa; Jan Korbel; Christine Sers; Arif Malik; Holger Eickhoff; Hans Lehrach; Johannes Schuchhardt
Journal:  Chaos       Date:  2001-03       Impact factor: 3.642

5.  Experimental design for gene expression microarrays.

Authors:  M K Kerr; G A Churchill
Journal:  Biostatistics       Date:  2001-06       Impact factor: 5.899

  5 in total
  1 in total

1.  SIMAGE: simulation of DNA-microarray gene expression data.

Authors:  Casper J Albers; Ritsert C Jansen; Jan Kok; Oscar P Kuipers; Sacha Aft van Hijum
Journal:  BMC Bioinformatics       Date:  2006-04-13       Impact factor: 3.169

  1 in total

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