title: Comparison and consolidation of microarray data sets of human tissue expression.
abstract: BACKGROUND
  Human tissue displays a remarkable diversity in structure and function. To understand how such diversity emerges from the same DNA, systematic measurements of gene expression across different tissues in the human body are essential. Several recent studies addressed this formidable task using microarray technologies. These large tissue expression data sets have provided us an important basis for biomedical research. However, it is well known that microarray data can be compromised by high noise level and various experimental artefacts. Critical comparison of different data sets can help to reveal such errors and to avoid pitfalls in their application.
  
  
  RESULTS
  We present here the first comparison and integration of four freely available tissue expression data sets generated using three different microarray platforms and containing a total of 377 microarray hybridizations. When assessing the tissue expression of genes, we found that the results considerably depend on the chosen data set. Nevertheless, the comparison also revealed statistically significant similarity of gene expression profiles across different platforms. This enabled us to construct consolidated lists of platform-independent tissue-specific genes using a set of complementary measures. Follow-up analyses showed that results based on consolidated data tend to be more reliable.
  
  
  CONCLUSIONS
  Our study strongly indicates that the consolidation of the four different tissue expression data sets can increase data quality and can lead to biologically more meaningful results. The provided compendium of platform-independent gene lists should facilitate the identification of novel tissue-specific marker genes.
authors.affiliation: Institute for Theoretical Biology, Charité, Humboldt University, Berlin, Germany.
authors.forename: Jenny
authors.initials: J
authors.lastname: Russ
authors.forename: Matthias E
authors.initials: ME
authors.lastname: Futschik
mesh_terms: D001921:Brain / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D023281:Genomics; D006801:Humans; D008099:Liver / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data; D009928:Organ Specificity; D015203:Reproducibility of Results; D049490:Systems Biology
references.citation: Nat Biotechnol. 2004 May;22(5):615-21
references.pmid: 15122300
references.citation: Genome Biol. 2004;5(10):R73
references.pmid: 15461792
references.citation: Nucleic Acids Res. 2003 Oct 1;31(19):5676-84
references.pmid: 14500831
references.citation: Clin Chim Acta. 2001 Oct;312(1-2):13-23
references.pmid: 11580905
references.citation: Cell. 2005 Sep 23;122(6):947-56
references.pmid: 16153702
references.citation: Proc Natl Acad Sci U S A. 2002 Oct 1;99(20):12783-8
references.pmid: 12196633
references.citation: Genet Epidemiol. 2002 Jun;23(1):70-86
references.pmid: 12112249
references.citation: Lancet. 2005 Feb 5-11;365(9458):488-92
references.pmid: 15705458
references.citation: BMC Proc. 2009 Jul 16;3 Suppl 4:S1
references.pmid: 19615109
references.citation: Proc Natl Acad Sci U S A. 2005 Dec 6;102(49):17763-8
references.pmid: 16306258
references.citation: Proc Natl Acad Sci U S A. 2005 Dec 6;102(49):17551-8
references.pmid: 16251272
references.citation: Genome Biol. 2005;6(3):R22
references.pmid: 15774023
references.citation: Science. 1999 Oct 15;286(5439):531-7
references.pmid: 10521349
references.citation: Bioinformatics. 2005 Apr 15;21(8):1724-6
references.pmid: 15585527
references.citation: Nat Genet. 2001 Jan;27(1):117-20
references.pmid: 11138011
references.citation: BMC Bioinformatics. 2007 Nov 15;8:446
references.pmid: 18005434
references.citation: Appl Bioinformatics. 2003;2(3 Suppl):S53-8
references.pmid: 15130817
references.citation: Science. 1978 Jan 20;199(4326):313-5
references.pmid: 339349
references.citation: Nat Methods. 2005 May;2(5):337-44
references.pmid: 15846360
references.citation: Nature. 2000 Feb 3;403(6769):503-11
references.pmid: 10676951
references.citation: N Engl J Med. 2002 Jun 20;346(25):1937-47
references.pmid: 12075054
references.citation: DNA Res. 2002 Apr 30;9(2):35-45
references.pmid: 12056413
references.citation: Proc Natl Acad Sci U S A. 2004 Apr 20;101(16):6062-7
references.pmid: 15075390
references.citation: BMC Genomics. 2003 Jul 29;4(1):31
references.pmid: 12885301
references.citation: Genome Biol. 2003;4(12):R82
references.pmid: 14659019
references.citation: Proc Natl Acad Sci U S A. 2004 Jun 22;101(25):9309-14
references.pmid: 15184677
references.citation: Nat Genet. 2005 Apr;37(4):382-90
references.pmid: 15778709
references.citation: Science. 2003 Dec 19;302(5653):2141-4
references.pmid: 14684825
references.citation: Bioinformatics. 2002 Mar;18(3):405-12
references.pmid: 11934739
references.citation: Bioinformatics. 2008 Apr 15;24(8):1063-9
references.pmid: 18310054
references.citation: Trends Genet. 2006 Mar;22(3):132-8
references.pmid: 16480787
references.citation: BMC Bioinformatics. 2005 Apr 25;6:107
references.pmid: 15850491
references.citation: Bioinformatics. 2003 Jan 22;19(2):185-93
references.pmid: 12538238
references.citation: Nucleic Acids Res. 2009 Jan;37(Database issue):D657-60
references.pmid: 18984619
references.citation: Proc Natl Acad Sci U S A. 2002 Apr 2;99(7):4465-70
references.pmid: 11904358
references.citation: Genome Biol. 2004;5(8):R60
references.pmid: 15287982