title: Correlation test to assess low-level processing of high-density oligonucleotide microarray data.
abstract: BACKGROUND
  There are currently a number of competing techniques for low-level processing of oligonucleotide array data. The choice of technique has a profound effect on subsequent statistical analyses, but there is no method to assess whether a particular technique is appropriate for a specific data set, without reference to external data.
  
  
  RESULTS
  We analyzed coregulation between genes in order to detect insufficient normalization between arrays, where coregulation is measured in terms of statistical correlation. In a large collection of genes, a random pair of genes should have on average zero correlation, hence allowing a correlation test. For all data sets that we evaluated, and the three most commonly used low-level processing procedures including MAS5, RMA and MBEI, the housekeeping-gene normalization failed the test. For a real clinical data set, RMA and MBEI showed significant correlation for absent genes. We also found that a second round of normalization on the probe set level improved normalization significantly throughout.
  
  
  CONCLUSIONS
  Previous evaluation of low-level processing in the literature has been limited to artificial spike-in and mixture data sets. In the absence of a known gold-standard, the correlation criterion allows us to assess the appropriateness of low-level processing of a specific data set and the success of normalization for subsets of genes.
authors.affiliation: Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Alexander.Ploner@meb.ki.se
authors.forename: Alexander
authors.initials: A
authors.lastname: Ploner
authors.forename: Lance D
authors.initials: LD
authors.lastname: Miller
authors.forename: Per
authors.initials: P
authors.lastname: Hall
authors.forename: Jonas
authors.initials: J
authors.lastname: Bergh
authors.forename: Yudi
authors.initials: Y
authors.lastname: Pawitan
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D015342:DNA Probes; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D009841:Oligonucleotides / Q000737:chemistry; D015347:RNA Probes; D012107:Research Design; D012680:Sensitivity and Specificity; D012984:Software
chemical_list: D015342:DNA Probes; D009841:Oligonucleotides; D015347:RNA Probes