Literature DB >> 11473024

Centralization: a new method for the normalization of gene expression data.

A Zien1, T Aigner, R Zimmer, T Lengauer.   

Abstract

Microarrays measure values that are approximately proportional to the numbers of copies of different mRNA molecules in samples. Due to technical difficulties, the constant of proportionality between the measured intensities and the numbers of mRNA copies per cell is unknown and may vary for different arrays. Usually, the data are normalized (i.e., array-wise multiplied by appropriate factors) in order to compensate for this effect and to enable informative comparisons between different experiments. Centralization is a new two-step method for the computation of such normalization factors that is both biologically better motivated and more robust than standard approaches. First, for each pair of arrays the quotient of the constants of proportionality is estimated. Second, from the resulting matrix of pairwise quotients an optimally consistent scaling of the samples is computed.

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Year:  2001        PMID: 11473024     DOI: 10.1093/bioinformatics/17.suppl_1.s323

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  35 in total

1.  Ranking: a closer look on globalisation methods for normalisation of gene expression arrays.

Authors:  Torsten C Kroll; Stefan Wölfl
Journal:  Nucleic Acids Res       Date:  2002-06-01       Impact factor: 16.971

2.  A model-based analysis of microarray experimental error and normalisation.

Authors:  Yongxiang Fang; Andrew Brass; David C Hoyle; Andrew Hayes; Abdulla Bashein; Stephen G Oliver; David Waddington; Magnus Rattray
Journal:  Nucleic Acids Res       Date:  2003-08-15       Impact factor: 16.971

3.  Reliability of gene expression ratios for cDNA microarrays in multiconditional experiments with a reference design.

Authors:  Rainer König; Danila Baldessari; Nicolas Pollet; Christof Niehrs; Roland Eils
Journal:  Nucleic Acids Res       Date:  2004-02-13       Impact factor: 16.971

4.  Classification of a large microarray data set: algorithm comparison and analysis of drug signatures.

Authors:  Georges Natsoulis; Laurent El Ghaoui; Gert R G Lanckriet; Alexander M Tolley; Fabrice Leroy; Shane Dunlea; Barrett P Eynon; Cecelia I Pearson; Stuart Tugendreich; Kurt Jarnagin
Journal:  Genome Res       Date:  2005-05       Impact factor: 9.043

5.  Utilization of lymphoblastoid cell lines as a system for the molecular modeling of autism.

Authors:  Colin A Baron; Stephenie Y Liu; Chindo Hicks; Jeffrey P Gregg
Journal:  J Autism Dev Disord       Date:  2006-11

6.  MS-EmpiRe Utilizes Peptide-level Noise Distributions for Ultra-sensitive Detection of Differentially Expressed Proteins.

Authors:  Constantin Ammar; Markus Gruber; Gergely Csaba; Ralf Zimmer
Journal:  Mol Cell Proteomics       Date:  2019-06-24       Impact factor: 5.911

7.  CD4 T cells require ICOS-mediated PI3K signaling to increase T-Bet expression in the setting of anti-CTLA-4 therapy.

Authors:  Hong Chen; Tihui Fu; Woong-Kyung Suh; Dimitra Tsavachidou; Sijin Wen; Jianjun Gao; Derek Ng Tang; Qiuming He; Jingjing Sun; Padmanee Sharma
Journal:  Cancer Immunol Res       Date:  2013-11-19       Impact factor: 11.151

8.  Gene expression profiling of the hypoxia signaling pathway in hypoxia-inducible factor 1alpha null mouse embryonic fibroblasts.

Authors:  Ajith Vengellur; Barbara G Woods; Heather E Ryan; Randall S Johnson; John J LaPres
Journal:  Gene Expr       Date:  2003

9.  Microarray meta-analysis database (M(2)DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database.

Authors:  Wei-Chung Cheng; Min-Lung Tsai; Cheng-Wei Chang; Ching-Lung Huang; Chaang-Ray Chen; Wun-Yi Shu; Yun-Shien Lee; Tzu-Hao Wang; Ji-Hong Hong; Chia-Yang Li; Ian C Hsu
Journal:  BMC Bioinformatics       Date:  2010-08-10       Impact factor: 3.169

10.  Normalization and gene p-value estimation: issues in microarray data processing.

Authors:  Katrin Fundel; Robert Küffner; Thomas Aigner; Ralf Zimmer
Journal:  Bioinform Biol Insights       Date:  2008-05-28
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