Literature DB >> 15177569

Are data from different gene expression microarray platforms comparable?

Anna-Kaarina Järvinen1, Sampsa Hautaniemi, Henrik Edgren, Petri Auvinen, Janna Saarela, Olli-P Kallioniemi, Outi Monni.   

Abstract

Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2), commercial cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library. Gene expression data from the commercial platforms showed good correlations across the experiments (r = 0.78-0.86), whereas the correlations between the custom-made and either of the two commercial platforms were lower (r = 0.62-0.76). Discrepant findings were due to clone errors on the custom-made microarrays, old annotations, or unknown causes. Even within platform, there can be several ways to analyze data that may influence the correlation between platforms. Our results indicate that combining data from different microarray platforms is not straightforward. Variability of the data represents a challenge for developing future diagnostic applications of microarrays.

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Year:  2004        PMID: 15177569     DOI: 10.1016/j.ygeno.2004.01.004

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  70 in total

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Review 2.  Reliability and reproducibility issues in DNA microarray measurements.

Authors:  Sorin Draghici; Purvesh Khatri; Aron C Eklund; Zoltan Szallasi
Journal:  Trends Genet       Date:  2005-12-27       Impact factor: 11.639

3.  Analysis of microarray experiments of gene expression profiling.

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4.  Assessing the performance of different high-density tiling microarray strategies for mapping transcribed regions of the human genome.

Authors:  Olof Emanuelsson; Ugrappa Nagalakshmi; Deyou Zheng; Joel S Rozowsky; Alexander E Urban; Jiang Du; Zheng Lian; Viktor Stolc; Sherman Weissman; Michael Snyder; Mark B Gerstein
Journal:  Genome Res       Date:  2006-11-21       Impact factor: 9.043

5.  Data quality in genomics and microarrays.

Authors:  Hanlee Ji; Ronald W Davis
Journal:  Nat Biotechnol       Date:  2006-09       Impact factor: 54.908

6.  A Bayesian mixture model for metaanalysis of microarray studies.

Authors:  Erin M Conlon
Journal:  Funct Integr Genomics       Date:  2007-09-19       Impact factor: 3.410

Review 7.  Accessing and integrating data and knowledge for biomedical research.

Authors:  A Burgun; O Bodenreider
Journal:  Yearb Med Inform       Date:  2008

8.  An integrated cross-platform prognosis study on neuroblastoma patients.

Authors:  Qing-Rong Chen; Young K Song; Jun S Wei; Sven Bilke; Shahab Asgharzadeh; Robert C Seeger; Javed Khan
Journal:  Genomics       Date:  2008-07-30       Impact factor: 5.736

9.  A wholly defined Agilent microarray spike-in dataset.

Authors:  Qianqian Zhu; Jeffrey C Miecznikowski; Marc S Halfon
Journal:  Bioinformatics       Date:  2011-03-16       Impact factor: 6.937

10.  A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide.

Authors:  Jonathan D Wren
Journal:  Bioinformatics       Date:  2009-05-15       Impact factor: 6.937

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