title: Selecting normalization genes for small diagnostic microarrays.
abstract: BACKGROUND
  Normalization of gene expression microarrays carrying thousands of genes is based on assumptions that do not hold for diagnostic microarrays carrying only few genes. Thus, applying standard microarray normalization strategies to diagnostic microarrays causes new normalization problems.
  
  
  RESULTS
  In this paper we point out the differences of normalizing large microarrays and small diagnostic microarrays. We suggest to include additional normalization genes on the small diagnostic microarrays and propose two strategies for selecting them from genomewide microarray studies. The first is a data driven univariate selection of normalization genes. The second is multivariate and based on finding a balanced diagnostic signature. Finally, we compare both methods to standard normalization protocols known from large microarrays.
  
  
  CONCLUSIONS
  Not including additional genes for normalization on small microarrays leads to a loss of diagnostic information. Using house keeping genes from the literature for normalization fails to work for certain datasets. While a data driven selection of additional normalization genes works well, the best results were obtained using a balanced signature.
authors.affiliation: Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany. jaeger@molgen.mpg.de
authors.forename: Jochen
authors.initials: J
authors.lastname: Jaeger
authors.forename: Rainer
authors.initials: R
authors.lastname: Spang
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D015894:Genome, Human / Q000235:genetics; D006801:Humans; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000175:diagnosis / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods; D013223:Statistics as Topic / Q000379:methods*