title: The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies.
abstract: BACKGROUND
  Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers.
  
  
  RESULTS
  Using two publicly available datasets, i.e., gene-expression data of 285 patients with Acute Myeloid Leukemia (AML, Affymetrix HG-U133A GeneChip) and 42 samples of tumor tissue of the embryonal central nervous system (CNS, Affymetrix HuGeneFL GeneChip), we tested the effect of the four pre-processing strategies mentioned above, on (1) expression level measurements, (2) detection of differential expression, (3) cluster analysis and (4) classification of samples. In most cases, the effect of pre-processing is relatively small compared to other choices made in an analysis for the AML dataset, but has a more profound effect on the outcome of the CNS dataset. Analyses on individual probe sets, such as testing for differential expression, are affected most; supervised, multivariate analyses such as classification are far less sensitive to pre-processing.
  
  
  CONCLUSIONS
  Using two experimental datasets, we show that the choice of pre-processing method is of relatively minor influence on the final analysis outcome of large microarray studies whereas it can have important effects on the results of a smaller study. The data source (platform, tissue homogeneity, RNA quality) is potentially of bigger importance than the choice of pre-processing method.
authors.affiliation: Department of Hematology, Erasmus Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl
authors.forename: Roel G W
authors.initials: RG
authors.lastname: Verhaak
authors.forename: Frank J T
authors.initials: FJ
authors.lastname: Staal
authors.forename: Peter J M
authors.initials: PJ
authors.lastname: Valk
authors.forename: Bob
authors.initials: B
authors.lastname: Lowenberg
authors.forename: Marcel J T
authors.initials: MJ
authors.lastname: Reinders
authors.forename: Dick
authors.initials: D
authors.lastname: de Ridder
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D002986:Clinical Trials as Topic; D015331:Cohort Studies; D030541:Databases, Genetic; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D012986:Software Validation
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins